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PMC11277355_p28
|
PMC11277355
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sec[2]/p[2]
|
3. Discussion
| 4.253906 |
biomedical
|
Study
|
[
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Here, using transcriptomic, genomic, and clinical data for diverse solid tumors, we investigated the association of HDAC expression, cancer stemness, and anti-tumor immunity. Previous studies demonstrated the overexpression of class I HDAC members in different cancer types, including gastric, esophagus, colorectal, prostate, glioma, melanoma, lung, and breast cancers (reviewed in ). Also, their upregulation is frequently associated with poor prognosis, especially in lung , gastric , liver , colorectal , ovarian , bladder , and breast carcinomas . Here, we show a consistent upregulation of all class I HDACs in tumor tissues in contrast to other classes, especially IIA and IIB HDAC members, whose expression pattern is more divergent and tumor type specific. Using clinical data from the TCGA and Prognoscan databases, we demonstrated that high class I HDAC members’ expression corresponds with worse survival of patients, especially in BRCA, LUAD, and GBM. Our results are in line with the previously reported worse survival rate of cancer patients with overexpressing class I HDACs, especially HDAC1 and HDAC3 in lung cancers , HDAC2 in liver or breast cancers , and HDAC1/2/3 in ovarian cancers , gastric cancers , or sarcomas .
|
[
"Kacper Maciejewski",
"Marek Giers",
"Urszula Oleksiewicz",
"Patrycja Czerwinska"
] |
https://doi.org/10.3390/ijms25147841
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11277355_p29
|
PMC11277355
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sec[2]/p[3]
|
3. Discussion
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biomedical
|
Study
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In comparison, high class IIA HDAC members’ expression is associated with better survival of BRCA, COAD, LUAD, and GBM patients. Our results align with a previously demonstrated better outcome for high-expressing patients of specific class IIA HDAC members, specifically in non-small cell lung carcinomas , HDAC7 in triple-negative breast cancers , and HDAC4/5 in gliomas .
|
[
"Kacper Maciejewski",
"Marek Giers",
"Urszula Oleksiewicz",
"Patrycja Czerwinska"
] |
https://doi.org/10.3390/ijms25147841
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277355_p30
|
PMC11277355
|
sec[2]/p[4]
|
3. Discussion
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biomedical
|
Study
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Also, HDACs are differentially associated with tumor stage and grade, however, in a cancer-dependent and gene-specific manner, and do not follow any specific trends, even within each HDAC family class. We observed several distinct HDACs associated with tumor grade in LGG, KIRC, and BLCA, while significant associations with stage are primarily observed in KIRC, KIRP, BLCA, and TGCT. Previously, higher expression of class I HDACs was observed in higher stages of colon cancer . In childhood neuroblastoma, upregulation of HDAC8 was associated with advanced stage, poor prognosis, and poor survival . Elevated expression of class I HDACs was also shown in higher-grade prostate cancers . Higher-grade ovarian tumors are characterized by upregulation of HDAC2 . Also, HDAC2 overexpression was associated with more aggressive stage III breast cancers, which significantly correlated with a worse prognosis . HDAC2 overexpression showed a statistically significant correlation with increased lymphatic spreading of the tumor (N stage) and lower tumor differentiation (higher grade) in esophageal adenocarcinomas . In lung cancer, HDAC1 levels were substantially lower in patients with well-differentiated adenocarcinoma than in those with a lower differentiation grade . Both HDAC1 and HDAC2 were significantly associated with higher tumor grades of urothelial bladder carcinoma . Together, highly expressed class I HDACs are usually associated with terminal illness and inferior outcomes in cancer patients.
|
[
"Kacper Maciejewski",
"Marek Giers",
"Urszula Oleksiewicz",
"Patrycja Czerwinska"
] |
https://doi.org/10.3390/ijms25147841
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11277355_p31
|
PMC11277355
|
sec[2]/p[5]
|
3. Discussion
| 4.140625 |
biomedical
|
Study
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Class II HDACs (HDAC5/7/9) downregulation was observed in glioblastomas compared to grade I–II astrocytomas . Higher-grade ovarian tumors exhibit downregulation of HDAC4 . Li H. et al. demonstrated that HDAC9 expression is negatively associated with both T and N stages (albeit not correlated with clinical stages) in PDAC tissues. Also, HDAC6 expression was significantly associated with earlier histopathological stages of pancreatic adenocarcinoma . Taken together, class II HDACs are usually associated with less progressed disease and superior outcomes in cancer patients.
|
[
"Kacper Maciejewski",
"Marek Giers",
"Urszula Oleksiewicz",
"Patrycja Czerwinska"
] |
https://doi.org/10.3390/ijms25147841
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11277355_p32
|
PMC11277355
|
sec[2]/p[6]
|
3. Discussion
| 4.179688 |
biomedical
|
Study
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Next, we demonstrated very robust and positive associations of class I HDACs’ expression and cancer stemness followed by a negative association of class IIA HDACs levels and tumor dedifferentiation status as measured with previously reported stemness quantifiers: the mRNA-stemness index (mRNA-SI) and other stem cell-derived gene signatures: Ben-Porath ES2, Wong ESC, and Ben-Porath ES core . The mRNA-SI was developed by a one-class logistic regression algorithm on transcriptomic data extracted from distinct stem cell populations and their differentiated progeny, creating a comprehensive stemness signature that allows for the quantification of tumor dedifferentiation in almost 12,000 samples across 33 tumor types.
|
[
"Kacper Maciejewski",
"Marek Giers",
"Urszula Oleksiewicz",
"Patrycja Czerwinska"
] |
https://doi.org/10.3390/ijms25147841
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277355_p33
|
PMC11277355
|
sec[2]/p[7]
|
3. Discussion
| 4.25 |
biomedical
|
Study
|
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Ben-Porath I. et al. have found that histologically poorly differentiated tumors show preferential overexpression of genes typically enriched in embryonic stem (ES) cells. They demonstrated that this ES-like signature was associated with high-grade estrogen receptor (ER)-negative tumors, often of the basal-like subtype, and with poor clinical outcomes. Moreover, the ES signature was present in poorly differentiated glioblastomas and bladder carcinomas, suggesting its versatility in the acquisition of cancer dedifferentiation status regardless of the tumor type. Furthermore, Wong DJ. et al. have recognized the embryonic stem cell (ESC) transcriptional program that is frequently activated in diverse human epithelial cancers and strongly predicts metastasis and death. They also identified that the c-Myc oncogene is sufficient to reactivate cancer cells’ ESC-like program. Using these independent transcriptome signatures, we quantified the level of cancer stemness in diverse solid tumors. We demonstrated that class I HDAC members’ expression was positively associated with the strongest correlations for HDAC2 regardless of the stemness score or signature. In contrast, the level of class IIA HDACs correlates negatively with the most robust and consistent associations observed for HDAC7.
|
[
"Kacper Maciejewski",
"Marek Giers",
"Urszula Oleksiewicz",
"Patrycja Czerwinska"
] |
https://doi.org/10.3390/ijms25147841
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11277355_p34
|
PMC11277355
|
sec[2]/p[8]
|
3. Discussion
| 4.601563 |
biomedical
|
Study
|
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Previously, Saunders A. et al. demonstrated that HDAC2 is critical for the reprogramming-promoting function of the SIN3A complex, facilitating the acquisition of pluripotency by non-transformed cells in NANOG-driven reprogramming. The SIN3A/HDAC2 complex and NANOG transcription factor are required to directly induce a synergistic transcriptional program, encompassing the activation of pluripotency genes and repression of differentiating genes. In light of cancer stemness acquisition significantly resembling somatic cell reprogramming, the abovementioned results strongly suggest that HDAC2 could contribute to the stem cell-like properties of cancer cells. Furthermore, both HDAC1 and HDAC2 were identified as proteins present in complexes with SOX2 transcription factor , further supporting the stem cell-associated roles for those class I HDACs. Also, the oncogenic activity of MYC is significantly induced by HDAC2, suggesting the potential benefits of applying HDAC inhibitors in the prevention and treatment of Myc-driven cancers. Recently, Bahia RK. et al. have identified HDAC2 as the most relevant histone deacetylase that facilitates stem cell-like properties in brain tumor cells. HDAC2 activity regulates chromatin compaction that impacts the expression of SMAD3 and SOX2 and is, thus, critical for the self-renewal of brain cancer cells. Also, the inhibition of HDAC2 activity disrupted the interaction with SMAD3, resulting in the loss of stem cell-like characteristics of brain tumor cells.
|
[
"Kacper Maciejewski",
"Marek Giers",
"Urszula Oleksiewicz",
"Patrycja Czerwinska"
] |
https://doi.org/10.3390/ijms25147841
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11277355_p35
|
PMC11277355
|
sec[2]/p[9]
|
3. Discussion
| 4.164063 |
biomedical
|
Study
|
[
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In our data, class I HDAC expression, especially HDAC2, correlates with the level of selected pluripotency markers, including BMI1, OCT4, MYC, and SOX2. Also, the associations were the strongest in testicular germ cell tumors, which exhibit the most pronounced stem cell-like phenotype of all tested solid tumors. On the other hand, class II HDACs, particularly HDAC7, are associated negatively with pluripotency markers’ expression in most tumor types, except for OCT4 (encoded by POU5F1), and NANOG in KIRC, KIRP, LGG, LUAD, LUSC, PRAD, and THCA. This suggests that class I HDACs may occur as attractive targets for anti-cancer treatment, especially for dedifferentiated (stem cell-like) solid tumors.
|
[
"Kacper Maciejewski",
"Marek Giers",
"Urszula Oleksiewicz",
"Patrycja Czerwinska"
] |
https://doi.org/10.3390/ijms25147841
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11277355_p36
|
PMC11277355
|
sec[2]/p[10]
|
3. Discussion
| 4.417969 |
biomedical
|
Study
|
[
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[
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When defining their transcriptome-based stemness score, Malta T. et al. have revealed an unanticipated negative correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. This was further supported by Miranda A. et al. , who observed that cancer stemness is associated with suppressed immune response, higher intratumoral heterogeneity, and dramatically worse outcomes for most TCGA cancers. The persistent interaction of cancer stem cells with the tumor microenvironment (TME) provides the ability to avoid recognition and elimination by immune cells, facilitating CSC’s survival and tumor progression . Cancer stem cells protect themselves against immune surveillance through several distinct mechanisms, including suppression of T cell activation, aberrant MHC class I expression, repression of tumor-associated antigens (TAAs), or by exploiting the immunosuppressive function of multiple immune checkpoint (IC) molecules . As cancer stemness maintenance relies significantly on epigenetic mechanisms, restoring dysregulated histone modifications to overcome cancer resistance to immunotherapy is a promising anticancer strategy.
|
[
"Kacper Maciejewski",
"Marek Giers",
"Urszula Oleksiewicz",
"Patrycja Czerwinska"
] |
https://doi.org/10.3390/ijms25147841
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11277355_p37
|
PMC11277355
|
sec[2]/p[11]
|
3. Discussion
| 4.5625 |
biomedical
|
Study
|
[
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Here, we demonstrated that HDAC2-high expressing tumors that exhibit enriched stem cell-like phenotypes are significantly depleted with immune cells, while the remaining infiltrating populations are responsible for the formation of an immunosuppressive microenvironment. On the other hand, solid tumors with HDAC7 upregulation are enriched with specific T cell subpopulations, including CD4+ naive and central memory T cells and CD8+ T cells, and activated dendritic cells, suggesting the formation of T cell-inflamed tumors . HDAC7 overexpression is associated with the upregulation of vast chemokine molecules, including the following T cell-attracting chemokines : CCL2, CCL3, CCL4, CCL5, CXCL9, and CXCL10, which explains their strong immune cell infiltration. These results further correspond with a significant upregulation of HDAC7 and a robust depletion of HDAC2 in the C3 immune subtype (inflammatory) in most tested tumors. However, HDAC7 is significantly associated with the upregulation of immune checkpoint molecules, including PD-1, PD-L1, and CTLA4. These suggest that, despite HDAC7-high expressing tumors are heavily infiltrated with immune cells that could kill cancer cells, the effectiveness of immune responses might be significantly abrogated at the level of immune checkpoint signaling. Therefore, HDAC inhibitors represent an exciting opportunity to improve the efficacy of immunotherapeutic regimens. Mechanistically, the tumor microenvironment and specific anti-tumor immune responses might be affected by HDAC inhibitors at several distinct levels, including the stimulation of cancer antigens’ expression or MHC class I/II expression, the modulation of immunosuppressive signaling pathways, the reduction in immunosuppressive cell populations, or the enrichment of chemokine expression .
|
[
"Kacper Maciejewski",
"Marek Giers",
"Urszula Oleksiewicz",
"Patrycja Czerwinska"
] |
https://doi.org/10.3390/ijms25147841
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11277355_p38
|
PMC11277355
|
sec[2]/p[12]
|
3. Discussion
| 4.277344 |
biomedical
|
Study
|
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Previously, Wang H-F. et al. have shown significant elimination of MDSC in the microenvironment of mice breast tumors treated with HDAC inhibitor SAHA (suberoylanilide hydroxamic acid, a potent inhibitor of HDAC1/2/3/6/7/11) that corresponds with an increased proportion of T cells (particularly that of IFN-γ- or perforin-producing CD8+ T cells) . Yan M. et al. have demonstrated that class I HDAC members associate negatively with CD8+ effector T cells, NK, and NKT cells in gynecologic cancers, including BRCA, CESC, OV, and UCEC tumors. Also, class I HDAC expression significantly correlated with the downregulation of specific T cell marker genes and suppressed inflammatory markers. In their study, the combination of HDAC inhibitor SAHA with anti-PD-1 in breast tumor-bearing mice suppressed tumor cell proliferation, promoted inflammatory responses, and increased the numbers of tumor-infiltrating T lymphocytes in vivo. Furthermore, Yang et al. observed that selective HDAC8 inhibition resulted in increased CD8+ T cell tumor infiltration in a preclinical model of hepatocellular carcinoma due to elevated production of T cell-recruiting chemokines. Direct inhibition of HDAC8 coupled with PD-L1 blockade further reinvigorated CD8+ T cells, turning from a functional exhausted state to IL-2- and IFN-γ-producing TILs.
|
[
"Kacper Maciejewski",
"Marek Giers",
"Urszula Oleksiewicz",
"Patrycja Czerwinska"
] |
https://doi.org/10.3390/ijms25147841
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277355_p39
|
PMC11277355
|
sec[2]/p[13]
|
3. Discussion
| 4.476563 |
biomedical
|
Study
|
[
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Nowadays, the involvement of epigenetic dysregulation in accelerating cancer progression (at least partially by facilitating cancer stemness) is unquestionable. An increasing number of studies demonstrate the potential of HDAC family members to become druggable targets in solid tumors, albeit those studies do not raise the question of therapeutic targeting of the stem cell-like compartment. Also, the fact that specific HDACs are essential players in molecular mechanisms modulating cancer stemness is mainly ignored. Taking that into consideration and based on our results, we suggest that direct inhibition of class I HDAC family members (which are significantly overexpressed in stem cell-like, low-infiltrated solid tumors), together with immune checkpoint inhibitors, might result in a better outcome of treated patients, presumably by abolishing the self-renewal properties of cancer cells and by strengthening the anti-tumor immune responses. Our observations stay in line with previously reported enhanced antitumor immunity in triple-negative breast cancers achieved by HDAC2 knockout in the breast cancer mice models. As presented by Xu P. et al. , HDAC2 is required for the chromatin remodeling of IFNγ-induced PD-L1 expression in breast tumors, and direct HDAC2 targeting suppresses immune escape of the tumor. Also, Zheng H. et al. have found that class I HDAC inhibitor romidepsin induced a strong T cell-dependent antitumor response and enhanced the therapeutic effect of PD-1 inhibitors in lung adenocarcinoma. Later on, Orillion A. et al. reported the enhancement of the antitumor effect of PD-1 inhibition by entinostat, another class I HDAC inhibitor, in murine models of lung and renal cell carcinomas. Recently, Han R. et al. have elegantly summarized the rationale of targeting HDAC2 and immune checkpoint inhibitors in hepatocellular carcinomas. This novel tumor treatment strategy is endowed with great clinical application and research prospects, which provides a new opportunity to improve the overall prognosis of cancer patients further. Therefore, we suggest that HDAC2-high expressing cancer patients who exhibit stem cell-like tumor phenotype might benefit from the combination therapy, including both class I HDAC and immune checkpoint inhibitors.
|
[
"Kacper Maciejewski",
"Marek Giers",
"Urszula Oleksiewicz",
"Patrycja Czerwinska"
] |
https://doi.org/10.3390/ijms25147841
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11277355_p40
|
PMC11277355
|
sec[3]/sec[0]/p[0]
|
4.1. TCGA Solid Tumor Types Selected for the Study
| 3.78125 |
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|
Study
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In the current study, we initially selected for the analysis 22 solid TCGA tumor types with more than 100 mRNA-SeqV2 available samples ( Table 2 ). Samples within individual studies (namely, tumor types) included in the TCGA come from primary untreated tumor resection fragments which are composed of at least 80% tumor nuclei . All data (including raw mRNA bulk sequencing results and clinical information) are available online, and the access is unrestricted and does not require patients’ consent or other permissions. The use of the data does not violate any personal or institutional rights.
|
[
"Kacper Maciejewski",
"Marek Giers",
"Urszula Oleksiewicz",
"Patrycja Czerwinska"
] |
https://doi.org/10.3390/ijms25147841
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
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|
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|
4.2. TCGA Genetic and Clinical Data
| 4.15625 |
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Study
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The RNA sequencing-based mRNA expression data were directly downloaded from the cBioportal through web-API . RNASeq V2 from TCGA is processed and normalized using RSEM . Specifically, the RNASeq V2 data in cBioPortal corresponds to the rsem.genes.normalized_results file from TCGA. Expression data of HDAC family members in cancer and normal adjacent samples for pan-can overviews were taken through the UCSC Xena Browser from GDC and GTEx datasets, respectively . Specific chemokines and chemokine receptors associated with HDAC2/7 expression were selected through the TISIDB (Tumor-Immune System Interaction Database) web-based resource . Expression data of chemokines, chemokine receptors, literature-specific inflammatory genes, transcription factors, and immune checkpoint genes in selected cancers was directly downloaded from the TCGA dataset using the UCSC Xena Browser . All clinical data (including grade, stage, tumor detailed subtype, tumor size, lymph nodes, and metastasis) for each sample was downloaded directly from the cBioPortal. Survival analysis (OS and DFS) was conducted with the GEPIA2 database . The hazard ratio was estimated through the Mantel–Cox test for patients with high (Q3, 75th percentile) relative to low (Q1, 25th percentile) expression of specific HDAC family members. Statistical values were extracted from resulting individual plots using the Selenium WebDriver .
|
[
"Kacper Maciejewski",
"Marek Giers",
"Urszula Oleksiewicz",
"Patrycja Czerwinska"
] |
https://doi.org/10.3390/ijms25147841
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999995 |
PMC11277355_p42
|
PMC11277355
|
sec[3]/sec[2]/p[0]
|
4.3. Prognosis Analysis Using the Prognoscan Database
| 3.996094 |
biomedical
|
Study
|
[
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] |
The PrognoScan database was used for the meta-analysis of the prognostic value of various genes. This online platform assists in investigating the relationship between gene expression and patient prognosis across a large collection of cancer microarray datasets. The significance threshold of associations between HDAC family members’ expression and patients’ overall survival was adjusted to a Cox p -value < 0.05.
|
[
"Kacper Maciejewski",
"Marek Giers",
"Urszula Oleksiewicz",
"Patrycja Czerwinska"
] |
https://doi.org/10.3390/ijms25147841
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11277355_p43
|
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sec[3]/sec[3]/p[0]
|
4.4. Stemness-Associated Scores
| 4.097656 |
biomedical
|
Study
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The mRNA-SI stemness score and other stemness signatures (Ben-Porath ES core, Ben-Porath ES2, and Wong ESC_core) used in this study were previously described . Briefly, the mRNA-SI signature was calculated based on a previously built predictive model using one-class logistic regression (OCLR) on the pluripotent stem cell samples (ESC and iPSC) from the Progenitor Cell Biology Consortium (PCBC) dataset. The obtained signature was further applied to score TCGA samples using the Spearman correlations between the model’s weight vector and the sample’s expression profile. The index was subsequently mapped to the range. As for Ben-Porat ES core, Ben-Porath ES2, and Wong ESC core signatures, we used the Gene Set Cancer Analysis (GSCA) platform to calculate the enrichment score of inputted gene sets in each sample of selected cancers with Gene Set Variation Analysis (GSVA) method .
|
[
"Kacper Maciejewski",
"Marek Giers",
"Urszula Oleksiewicz",
"Patrycja Czerwinska"
] |
https://doi.org/10.3390/ijms25147841
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277355_p44
|
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sec[3]/sec[4]/p[0]
|
4.5. Gene Set Enrichment Analysis
| 4.085938 |
biomedical
|
Study
|
[
0.99951171875,
0.0001952648162841797,
0.0002002716064453125
] |
[
0.9990234375,
0.0007266998291015625,
0.0003972053527832031,
0.00006651878356933594
] |
We employed the Enrichr tool which is an integrative web-based software application providing various types of computing gene set enrichment, and visualization summaries of collective functions of single genes or gene lists . We used the top 100 most relevant genes (identified with ARCHS4 RNA-seq gene–gene co-expression matrix) for a queried gene (HDAC2 or HDAC7) to determine significantly enriched pathways or to later on detect potential targets for known transcription factors (Transcription Factor PPIs module).
|
[
"Kacper Maciejewski",
"Marek Giers",
"Urszula Oleksiewicz",
"Patrycja Czerwinska"
] |
https://doi.org/10.3390/ijms25147841
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277355_p45
|
PMC11277355
|
sec[3]/sec[4]/p[1]
|
4.5. Gene Set Enrichment Analysis
| 4.195313 |
biomedical
|
Study
|
[
0.99951171875,
0.00027751922607421875,
0.00018262863159179688
] |
[
0.9990234375,
0.00027298927307128906,
0.00048351287841796875,
0.00007748603820800781
] |
The Gene Set Enrichment Analysis was performed to detect coordinated expression of a priori-defined groups of genes within the tested samples. Gene sets are available at the Molecular Signatures Database . All significantly differentially expressed genes were previously ranked based on their log2FC between analyzed groups: high expression (Q3, 75th percentile) relative to low (Q1, 25th percentile) expression of HDAC2 or HDAC7. Groups of ranked genes were imported to GSEA, and the GSEAPreranked tool was run according to the following parameters: Dataset used in the original format (no collapse) and permutation number = 1000. The FDR <0.05 was used to correct for multiple comparisons and gene set sizes. For the CP5:BP collection, the resulting lists of terms for separately HDAC2 and HDAC7 genes were limited to processes for which data are available for all studied tumor types. We selected processes from the very top and bottom (measured as sum of NESs in each tumor) of the pre-ranked datasets. Both lists were intersected to obtain a final list for both genes combined. Resulting processes were manually annotated in an unbiased manner, based on the most relevant ancestry processes . This step resulted in classification into cell cycle, cell division, DNA repair, and immune process classes. For the CP2:CGP collection, we manually filtered out all terms which are specific and thus applicable only for individual tumor types and intersected the rest of processes for both HDAC2 and HDAC7 genes. The hallmarks collection is presented without any prior filtering.
|
[
"Kacper Maciejewski",
"Marek Giers",
"Urszula Oleksiewicz",
"Patrycja Czerwinska"
] |
https://doi.org/10.3390/ijms25147841
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11277355_p46
|
PMC11277355
|
sec[3]/sec[5]/p[0]
|
4.6. Immune and Stromal Infiltration
| 4.132813 |
biomedical
|
Study
|
[
0.99951171875,
0.00018298625946044922,
0.0001722574234008789
] |
[
0.99853515625,
0.00029921531677246094,
0.0008654594421386719,
0.0000641942024230957
] |
Immune infiltration was examined using the TIMER2.0 database , which allowed us to calculate correlations of gene expressions with immune infiltration levels in diverse cancer types using different algorithms. The purity-adjusted Spearman’s rho values across 11 cancer types calculated using the xCELL method were downloaded directly from TIMER2.0 . Based on single-sample GSEA, xCELL is a cell-type quantification method that, unlike other commonly used algorithms like CIBERSORT, is a gene-based marker approach rather than a deconvolutional approach. It outperforms other methods, allows predicting the highest number of cell types among available algorithms (up to 64), and is considered more robust for abundance analysis in contrast to deconvolutional methods .
|
[
"Kacper Maciejewski",
"Marek Giers",
"Urszula Oleksiewicz",
"Patrycja Czerwinska"
] |
https://doi.org/10.3390/ijms25147841
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11277355_p47
|
PMC11277355
|
sec[3]/sec[5]/p[1]
|
4.6. Immune and Stromal Infiltration
| 3.9375 |
biomedical
|
Study
|
[
0.99951171875,
0.00017499923706054688,
0.0002300739288330078
] |
[
0.99560546875,
0.0035858154296875,
0.0005559921264648438,
0.00011587142944335938
] |
ESTIMATE (estimation of stromal and immune cells in malignant tumor tissues using expression data) web-based software was employed to predict each tumor purity, level of stroma cells presence in tumor tissues, and level of tumor immune infiltration, using gene expression data .
|
[
"Kacper Maciejewski",
"Marek Giers",
"Urszula Oleksiewicz",
"Patrycja Czerwinska"
] |
https://doi.org/10.3390/ijms25147841
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11277355_p48
|
PMC11277355
|
sec[3]/sec[6]/p[0]
|
4.7. Immune Subtypes and Immunotherapy Results
| 4.046875 |
biomedical
|
Study
|
[
0.99951171875,
0.00016629695892333984,
0.00020897388458251953
] |
[
0.9990234375,
0.000270843505859375,
0.00045418739318847656,
0.00004792213439941406
] |
To investigate tumor–immune interactions, we used the TISIDB (tumor–immune system interaction database) web-based resource . We explored relationships between HDAC2 or HDAC7 and five immune subtypes: C1 (wound healing), C2 (IFN-gamma dominant), C3 (inflammatory), C4 (lymphocyte depleted), C6 (TGF-b dominant) in 11 cancer types.
|
[
"Kacper Maciejewski",
"Marek Giers",
"Urszula Oleksiewicz",
"Patrycja Czerwinska"
] |
https://doi.org/10.3390/ijms25147841
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999995 |
PMC11277355_p49
|
PMC11277355
|
sec[3]/sec[6]/p[1]
|
4.7. Immune Subtypes and Immunotherapy Results
| 3.988281 |
biomedical
|
Study
|
[
0.99951171875,
0.00014281272888183594,
0.00012409687042236328
] |
[
0.9970703125,
0.00151824951171875,
0.0013113021850585938,
0.00009435415267944336
] |
Additionally, the TIGER database was employed to explore the potential role of HDAC family members as biomarkers of immunotherapy response in solid tumors. The immunotherapy response module provides differential expression analysis, which uses bulk transcriptome data with immunotherapy clinical information to find differences in the expression of the gene in question between responders and non-responders to specific immunotherapies .
|
[
"Kacper Maciejewski",
"Marek Giers",
"Urszula Oleksiewicz",
"Patrycja Czerwinska"
] |
https://doi.org/10.3390/ijms25147841
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277355_p50
|
PMC11277355
|
sec[3]/sec[7]/p[0]
|
4.8. Statistical Analysis
| 1.814453 |
biomedical
|
Other
|
[
0.98974609375,
0.0007691383361816406,
0.009429931640625
] |
[
0.35546875,
0.6396484375,
0.00327301025390625,
0.0015735626220703125
] |
Statistical analyses were carried out with GraphPad Prism 8.0 (GraphPad Software, Inc., La Jolla, CA, USA), R 4.2.2 (R Foundation for Statistical Computing, Vienna, Austria) with ggplot2 and complexheatmap libraries for visualization, and Python 3.11 (The Python Software Foundation, Wilmington, Delaware) with Selenium library for data retrieval. Exact applied statistical tests are described in each figure description.
|
[
"Kacper Maciejewski",
"Marek Giers",
"Urszula Oleksiewicz",
"Patrycja Czerwinska"
] |
https://doi.org/10.3390/ijms25147841
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11277355_p51
|
PMC11277355
|
sec[4]/p[0]
|
5. Conclusions
| 4.132813 |
biomedical
|
Study
|
[
0.99951171875,
0.00022780895233154297,
0.00022339820861816406
] |
[
0.9990234375,
0.00014221668243408203,
0.0007610321044921875,
0.00005733966827392578
] |
Our research uncovered a significant association between cancer stemness and an elevated expression of class I HDAC family members, especially HDAC2, where the association was robust and universal regardless of the tested tumor type. On the other hand, the relation of class IIA HDAC members is significantly negative, with HDAC7 exhibiting the strongest ones. We further demonstrated for the first time the distinct trends of associations between class I and class IIA HDACs and anti-tumor immunity, with the expression of class I HDAC2 being negatively correlated and class IIA HDAC7 being positively correlated.
|
[
"Kacper Maciejewski",
"Marek Giers",
"Urszula Oleksiewicz",
"Patrycja Czerwinska"
] |
https://doi.org/10.3390/ijms25147841
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277355_p52
|
PMC11277355
|
sec[4]/p[1]
|
5. Conclusions
| 3.917969 |
biomedical
|
Study
|
[
0.9990234375,
0.00045371055603027344,
0.0002923011779785156
] |
[
0.66015625,
0.32275390625,
0.015625,
0.0012216567993164062
] |
We suggest that patients with stem cell-like, low-infiltrated solid tumors exhibiting significant upregulation of HDAC2 might benefit from the treatment with the combination of HDAC2-specific inhibitors and immunotherapy (i.e., immune checkpoint inhibitors).
|
[
"Kacper Maciejewski",
"Marek Giers",
"Urszula Oleksiewicz",
"Patrycja Czerwinska"
] |
https://doi.org/10.3390/ijms25147841
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277387_p0
|
PMC11277387
|
sec[0]/p[0]
|
Introduction
| 3.804688 |
biomedical
|
Study
|
[
0.94921875,
0.0003390312194824219,
0.050384521484375
] |
[
0.8681640625,
0.1033935546875,
0.0283660888671875,
0.0003032684326171875
] |
In recent decades, the concern about pollution, specifically the contamination of both land and air, has gained considerable attention due to heavy metals (HMs) accumulation in various environmental matrices . HMs have different sources, stemming from activities such as fuel combustion, industrial processes, mining, and erosion of building surfaces . Settled dust, which includes particles typically larger than 10 μm, plays a crucial role in the transport and deposition of these toxic elements onto the Earth's surface . As an indicator of air pollution, settled dust has to be monitored and assessed due to its capacity to harbor HMs contaminants and its contribution to environmental and health impacts .
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11277387_p1
|
PMC11277387
|
sec[0]/p[1]
|
Introduction
| 4.269531 |
biomedical
|
Study
|
[
0.99853515625,
0.0004143714904785156,
0.001220703125
] |
[
0.98828125,
0.00028133392333984375,
0.0111846923828125,
0.00009763240814208984
] |
HMs, when present in the atmosphere and soil, pose significant health risks to humans . These risks arise from toxic, non-biodegradable, and non-thermo-degradable nature of HMs, making them harmful to various organs, including the kidneys, liver, and bones upon exposure through ingestion, inhalation, or dermal contact [ , , ]. Several studies worldwide have highlighted the ecological and health risks associated with HMs contamination in environmental media, including settled dust and street dust. The findings a research conducted across various urban functional zones in Shijiazhuang, China, revealed a significant enrichment in levels of Copper (Cu), Zinc (Zn), Cadmium (Cd), and Lead (Pb) in the dust. There was also substantial concentration variations between distinct functional areas, pointing to their anthropogenic sources . A study on HMs pollution in Warsaw's street dust revealed a significant dependence on background values for calculating pollution indicators. Traffic-related pollution detection was more accurate with calculated indicators, while naturally elevated HMs concentrations led to underestimations. Aligning low concentrations with geogenic material better reflected pollution levels from moving vehicles . Furthermore, investigations across six cities in Ebinur Lake Basin, China, investigated the spatiotemporal distribution of HMs in dust fall during heating and non-heating periods. Results revealed higher HMs levels in non-heating period, except for Cd and Pb, which were the highest during heating period. Main sources identified were fuel combustion, soil and industrial dust, and traffic emissions . In research conducted in the vicinity of the former Aral Sea in the Republic of Uzbekistan, long-term exposure to saline dust and dust storms resulted in elevated levels of pollutant residues, and increased rates of illness and death, particularly for individuals with chronic diseases . The investigation into dust storms and deposition in Aral Sea region emphasized the pronounced impact on the southern area due to prevailing wind direction. Findings from monitoring seven sampling stations between 2003 and 2012 indicated a generally low average dust deposition, yet notable peaks during dust storms, exhibiting seasonal patterns with heightened health-related threshold exceedance in spring and summer. The composition of dust samples closely resembled that of near-surface soils, and concentrations of HMs were relatively low .
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11277387_p2
|
PMC11277387
|
sec[0]/p[2]
|
Introduction
| 1.99707 |
biomedical
|
Other
|
[
0.92431640625,
0.0009908676147460938,
0.07464599609375
] |
[
0.470458984375,
0.52587890625,
0.002063751220703125,
0.0013608932495117188
] |
In Baghdad, Iraq, Cd, Chromium (Cr), Zn, and Cu were found in dust samples, with implications for geoaccumulation and relative bioaccumulation .
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11277387_p3
|
PMC11277387
|
sec[0]/p[3]
|
Introduction
| 2.714844 |
biomedical
|
Study
|
[
0.970703125,
0.00042891502380371094,
0.02862548828125
] |
[
0.6337890625,
0.35107421875,
0.0141143798828125,
0.0006976127624511719
] |
Considering the multitude sources contributing to settled dust and its potential role in the dispersion of toxic elements, understanding the origins of HMs emissions in settled dust becomes paramount. The dearth of temporal trend surveys and the presence of high HMs concentrations in settled dust necessitate comprehensive evaluation of multi-source emissions and the associated health effects on both humans and ecosystem .
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277387_p4
|
PMC11277387
|
sec[0]/p[4]
|
Introduction
| 1.126953 |
other
|
Other
|
[
0.03662109375,
0.0007023811340332031,
0.962890625
] |
[
0.01324462890625,
0.9853515625,
0.0006852149963378906,
0.0005202293395996094
] |
The northwestern region of Iran, surrounding Lake Urmia (saltwater lake), faces environmental challenges due to a combination of factors, including agriculture, industrial activities, and drying of over 90 % of the lake bed, primarily caused by climate change . This region, covering an area of 80,000 km 2 and home to more than 8,000,000 people by 2021, is vulnerable to environmental contamination .
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11277387_p5
|
PMC11277387
|
sec[0]/p[5]
|
Introduction
| 1.74707 |
biomedical
|
Other
|
[
0.6220703125,
0.000995635986328125,
0.377197265625
] |
[
0.43408203125,
0.56298828125,
0.002216339111328125,
0.0008730888366699219
] |
In the northern regions of Lake Urmia, there are cities and villages with a total population of over 3,000,000. However, despite the crisis of Lake Urmia drying up, one of the world's largest saltwater lakes that is currently shrinking, comprehensive studies have not been conducted in this area. Identifying critical areas and concentrations of HMs is important, especially As, which has been identified with high levels in studies in other parts of the lake. However, the presence of HMs in the air and soil particles in this area poses a threat to the health of the residents.
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277387_p6
|
PMC11277387
|
sec[0]/p[6]
|
Introduction
| 4.042969 |
biomedical
|
Study
|
[
0.99365234375,
0.00036978721618652344,
0.005771636962890625
] |
[
0.99951171875,
0.0001499652862548828,
0.0001647472381591797,
0.00003629922866821289
] |
Despite the threats to both the environment and public health, there has been limited research addressing HMs contamination in soil and settled dust within the northern regions of the former Lake Urmia. Hence, this study endeavors to assess HMs (As, Cd, Cr, and Pb) levels in surface soil and settled dust near the northern vicinity of Lake Urmia. Previous research had highlighted elevated levels of As, Cd, Cr, and Pb in another section of the Lake Urmia area. Additionally, these metals have significant health implications, as evidenced by previous studies . Furthermore, a comprehensive examination of carcinogenic and non-carcinogenic risks linked to HMs exposure via inhalation, skin contact, and ingestion routes was conducted, employing advanced Monte Carlo simulation techniques.
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11277387_p7
|
PMC11277387
|
sec[1]/sec[0]/p[0]
|
Study area and sampling
| 1.680664 |
other
|
Study
|
[
0.294921875,
0.0014295578002929688,
0.70361328125
] |
[
0.96875,
0.0302886962890625,
0.00047016143798828125,
0.0005164146423339844
] |
This study was carried out in the northern regions of the former Lake Urmia in the northwestern of Iran., specifically spanning from Salmas city to Shabestar city at coordinates 38° 22ʹ N, 44° 44ʹ E . Meteorological data indicates that the area is primarily affected by humid winds from Atlantic and Mediterranean, resulting in chilly north winds and considerable winter snowfall. The mean annual temperature in this area is 12.6 °C, with temperature fluctuations ranging from −6 to 30 °C . Fig. 1 The study area map featuring sampling points, Shabestar city, Salmas cities, and the dried-up portion of Lake Urmia, exceeding 90 % of its original extent. Fig. 1
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11277387_p8
|
PMC11277387
|
sec[1]/sec[0]/p[1]
|
Study area and sampling
| 2.175781 |
other
|
Study
|
[
0.471923828125,
0.00144195556640625,
0.5263671875
] |
[
0.978515625,
0.021087646484375,
0.0003459453582763672,
0.0002677440643310547
] |
In March 2023, 30 samples of soil were gathered from this region, with three samples collected from each distinct area. The strategic sampling method was scheduled one week following the rainfall periods to minimize the potential impact of soil leaching and drainage on the concentrations of As, Cd, Cr, and Pb. Soil samples (0–30 cm) included collecting approximately 1 kg of soil (dry weight) as a composite sample from three subsamples situated approximately 10 m apart. Polyethylene bags were employed for sample collection and transportation to the laboratory for subsequent analyses.
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11277387_p9
|
PMC11277387
|
sec[1]/sec[0]/p[2]
|
Study area and sampling
| 3.544922 |
biomedical
|
Study
|
[
0.93017578125,
0.0006489753723144531,
0.0693359375
] |
[
0.9697265625,
0.0298004150390625,
0.0003592967987060547,
0.00015735626220703125
] |
Dust collection (30 samples) was done passively over a one-month period using a custom-designed sampler. The sampler comprised a standard electron microscope (SEM) stub base with a polycarbonate filter bed under a mesh protective cap featuring openings of 160 μm in diameter. It has two interconnected circular plates set 16 mm apart to facilitate air flow. A polycarbonate absorbent particle filter was affixed to the lower plate, providing protection against wind and rain. It was positioned at a height of 2 m from the ground at least 15 m away from any pollution source. The sampler collected particles through the combined forces of gravity, diffusion, and inertia. Zist Sepehr Bayhaq Company (accessible at https://zsbcompany.ir ) is the creator of this sampler. After installation, the samplers were left in place for three months before being retrieved and transported to the analytical laboratory for further processing.
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277387_p10
|
PMC11277387
|
sec[1]/sec[1]/p[0]
|
Sample preparation and chemical analysis
| 4.089844 |
biomedical
|
Study
|
[
0.99853515625,
0.0006699562072753906,
0.0008826255798339844
] |
[
0.97705078125,
0.021728515625,
0.0008158683776855469,
0.0002696514129638672
] |
Preparation and digestion of settled dust samples adhered to the standardized procedure outlined in USEPA [ , , ]. Initially, a Teflon beaker was employed for positioning the filter, followed by the addition of 10 mL of concentrated Suprapour nitric acid. The mixture was heated for 120 min on a hot plate at a temperature of 95 ± 5 °C until approximately 5 mL of the solution evaporated. Subsequently, 2 mL of ultrapure and 3 mL of H 2 O 2 (30 %) were introduced. The heating process continued until ebullition ceased. Additional aliquots of 1 mL H 2 O 2 (30 %) were added until no further change in the solution appearance was observed, the specified temperature was maintained for over 120 min until the solution volume reached 5 mL. In the final stage, 10 mL of concentrated hydrochloric acid was introduced into the solution and heated for 15 min.
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11277387_p11
|
PMC11277387
|
sec[1]/sec[1]/p[1]
|
Sample preparation and chemical analysis
| 3.03125 |
biomedical
|
Study
|
[
0.806640625,
0.0007967948913574219,
0.1925048828125
] |
[
0.99072265625,
0.00891876220703125,
0.0003097057342529297,
0.00011622905731201172
] |
The soil samples were then subjected to dehydration at 60 °C for 24 h, subsequently, the material underwent crushing and sieving through a 100-mesh (150 μm) sieve to simplify the determination of metal concentrations. The detection of HMs was conducted through the application of USEPA Method 3050B, as outlined in our earlier investigation [ , , ].
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277387_p12
|
PMC11277387
|
sec[1]/sec[1]/p[2]
|
Sample preparation and chemical analysis
| 4.109375 |
biomedical
|
Study
|
[
0.9990234375,
0.0001596212387084961,
0.0006203651428222656
] |
[
0.9990234375,
0.0005478858947753906,
0.00020492076873779297,
0.00003820657730102539
] |
HMs concentrations (specifically As, Cd, Cr, and Pb) were measured using ICP-AES, Arcus model, Germany. The method detection limit (MDL) was determined utilizing the standard deviation. The analytical method included examining samples demonstrating comparable retrieval rates to those from the field, achieving a recovery rate ranging between 83 and 96 percent of the original materials. Also, some of the analytical parameters for the ICP-AES were included a plasma gas flow rate of 15 L/min, an auxiliary gas flow rate of 1.5 L/min, a nebulizer gas flow rate of 0.7 L/min, RF power set to 1.2 kW, a wavelength range of 180–300 nm, a sample uptake delay of 15 s, and a sample uptake rate of 1 mL/min.
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11277387_p13
|
PMC11277387
|
sec[1]/sec[2]/p[0]
|
Quality assurance and quality control
| 4.078125 |
biomedical
|
Study
|
[
0.99853515625,
0.00040030479431152344,
0.0008983612060546875
] |
[
0.99609375,
0.0036792755126953125,
0.00032448768615722656,
0.0000871419906616211
] |
Quality control measures included running blank digest solutions and conducting periodic rechecks of calibration standards accuracy and precision after every 20 samples. Calibration curve quality control was based on verifying the coefficient of determination (r 2 ) in linear regression, with values exceeding 0.994 deemed acceptable. The MDL was calculated according to the USEPA-40 CFR part 136 recommendations, ensuring that the element value exceeded the predicted detection limit by 1–5 times and multiple measurements were taken.
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11277387_p14
|
PMC11277387
|
sec[1]/sec[2]/p[1]
|
Quality assurance and quality control
| 3.921875 |
biomedical
|
Study
|
[
0.998046875,
0.0002346038818359375,
0.0017404556274414062
] |
[
0.98193359375,
0.017730712890625,
0.00034117698669433594,
0.00012695789337158203
] |
This process entailed assigning the element value within a range of 1–5 times the estimated detection limit and performing multiple measurements. Following the calculation of the standard deviation, the MDL was determined using Formula 1 : (1) MDL = Student ’ s t value × standard deviation
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11277387_p15
|
PMC11277387
|
sec[1]/sec[2]/p[2]
|
Quality assurance and quality control
| 3.052734 |
biomedical
|
Study
|
[
0.99658203125,
0.00032329559326171875,
0.003143310546875
] |
[
0.9443359375,
0.054229736328125,
0.0011234283447265625,
0.0004363059997558594
] |
The detection limits for As, Cd, Cr, and Lead (Pb) were 0.5 ppm, 0.1 ppm, 1 ppm, and 1 ppm, respectively.
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277387_p16
|
PMC11277387
|
sec[1]/sec[3]/p[0]
|
Enrichment factors (EFs)
| 4.101563 |
biomedical
|
Study
|
[
0.99755859375,
0.00021779537200927734,
0.00199127197265625
] |
[
0.99951171875,
0.0004379749298095703,
0.0001970529556274414,
0.00003069639205932617
] |
EFs were employed to ascertain potential sources of HMs and assess the extent of contamination. The EF value was calculated using formula (2) : (2) E F = ( C n / C F e ) s a m p l e ( C n / C F e ) b a c k g r o u n d In this equation, C Fe denotes the iron concentration, and C n represents the quantity of HMs (mg/kg) in both the sampled site and the reference site. The contamination level of the samples was categorized based on EF values as follows: EF > 50 (very intense enrichment), 25 ≤ EF < 50 (very intense enrichment), 10 ≤ EF < 25 (extreme enrichment), 5 ≤ EF < 10 (relatively intense enrichment), 3 ≤ EF < 5 (moderate enrichment), 1 ≤ EF < 3 (slight enrichment), and EF < 1 (no enrichment).
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277387_p17
|
PMC11277387
|
sec[1]/sec[4]/p[0]
|
Potential ecological risk (PER) and health risk assessment
| 2.060547 |
biomedical
|
Study
|
[
0.90625,
0.0024662017822265625,
0.09130859375
] |
[
0.98876953125,
0.0099029541015625,
0.0009322166442871094,
0.0004658699035644531
] |
In this study, the PER was employed to assess the ecological risks associated with the targeted metals.
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999999 |
PMC11277387_p18
|
PMC11277387
|
sec[1]/sec[4]/p[1]
|
Potential ecological risk (PER) and health risk assessment
| 2.716797 |
biomedical
|
Study
|
[
0.97021484375,
0.00054168701171875,
0.0294189453125
] |
[
0.67333984375,
0.324462890625,
0.0014085769653320312,
0.0006732940673828125
] |
The PER was calculated using (3) , (4) , (5) : (3) P E R = ∑ i = 1 n E j i (4) E j i = T n i × CF j i (5) C f i = C i C n i
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.857139 |
PMC11277387_p19
|
PMC11277387
|
sec[1]/sec[4]/p[2]
|
Potential ecological risk (PER) and health risk assessment
| 3.353516 |
biomedical
|
Study
|
[
0.9248046875,
0.0003960132598876953,
0.07476806640625
] |
[
0.96435546875,
0.03436279296875,
0.0009417533874511719,
0.0001621246337890625
] |
Here, CF represents the pollution factor specific to each metal, C f i denotes the concentration of the target metal in the sample, and Cn is the background concentration of the target metal. Additionally, E j i stands for the ecological risk potential factor of each metal, T represents the toxicity response factor of each metal, and PER indicates the overall ecological risk potential, which is derived from the sum of the PER of all investigated metals. The details regarding the PER were outlined in our earlier investigation .
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277387_p20
|
PMC11277387
|
sec[1]/sec[4]/p[3]
|
Potential ecological risk (PER) and health risk assessment
| 3.958984 |
biomedical
|
Study
|
[
0.96484375,
0.0004229545593261719,
0.03466796875
] |
[
0.90966796875,
0.07489013671875,
0.014984130859375,
0.000209808349609375
] |
The classification of PER entails assessing the ecological risk potential factors for individual metals ( E j i ) and the overall PER. Each metal risk potential range ( E j i ) is segmented into various risk degrees. Furthermore, the PER as a whole is categorized into corresponding risk degree classifications. Metals exhibiting a risk potential below 40 are categorized as posing low risk. Those with risk potentials falling within the range of 40–80 are regarded as having moderate risk potential. Metals with risk potentials ranging from 80 to 160 are designated as being subject to investigation. The PER is considered high for metals ranging from 160 to 320, and anything surpassing 320 is labeled as severe. Similarly, the classifications for the overall PER are determined by evaluating the cumulative risk potential.
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11277387_p21
|
PMC11277387
|
sec[1]/sec[4]/sec[0]/p[0]
|
Exposure assessment
| 4.058594 |
biomedical
|
Study
|
[
0.9990234375,
0.00017118453979492188,
0.0009732246398925781
] |
[
0.9990234375,
0.0008864402770996094,
0.0001989603042602539,
0.00003987550735473633
] |
To assess human exposure to HMs present in surface soil dust, three exposure pathways were considered including ingestion, inhalation, and dermal absorption. The average daily doses (ADDs) for each pathway (ADDing, ADDinh, ADDderm) in mg/kg.day were calculated using the USEPA method : (6) A D D i n g = C s o i l × I n g R × E F × E D B W × A T × C F (7) A D D i n h = C s o i l × I n h R × E F × E D P E F × B W × A T (8) A D D d e r m = C s o i l × S A × A F s o i l A B S × E F × E D B W × A T × C F
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11277387_p22
|
PMC11277387
|
sec[1]/sec[4]/sec[0]/p[1]
|
Exposure assessment
| 4.03125 |
biomedical
|
Study
|
[
0.9990234375,
0.00015163421630859375,
0.0008525848388671875
] |
[
0.99853515625,
0.00125885009765625,
0.0001957416534423828,
0.00005036592483520508
] |
The coefficients for these equations are detailed in our previous study . Non-carcinogenic risk assessment involved computing the hazard quotient (HQ) for individual elements and the hazard index (HI) representing the cumulative HQ across three exposure pathways. (9) H Q = A D D / R f (10) H I = ∑ H Q = H Q i n g + H Q i n h + H Q d e r m
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277387_p23
|
PMC11277387
|
sec[1]/sec[4]/sec[0]/p[2]
|
Exposure assessment
| 3.191406 |
biomedical
|
Other
|
[
0.998046875,
0.00032639503479003906,
0.0015211105346679688
] |
[
0.284912109375,
0.705078125,
0.0091552734375,
0.0008111000061035156
] |
HQ or HI values exceeding 1 suggest the possibility of non-carcinogenic effects, whereas HQ or HI values below 1 indicate a safe threshold for future exposure.
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277387_p24
|
PMC11277387
|
sec[1]/sec[4]/sec[0]/p[3]
|
Exposure assessment
| 3.916016 |
biomedical
|
Study
|
[
0.99853515625,
0.0002455711364746094,
0.001117706298828125
] |
[
0.96875,
0.030029296875,
0.0009045600891113281,
0.0002009868621826172
] |
The calculation of the cancer risk linked to prospective exposure involved determining the excess lifetime cancer risk (ELCR) using Formula 11 . (11) E L C R = A D D × C S F (12) ∑ ELCR = E L C R i n g + E L C R i n h + E L C R d e r m
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277387_p25
|
PMC11277387
|
sec[1]/sec[4]/sec[0]/p[4]
|
Exposure assessment
| 3.660156 |
biomedical
|
Study
|
[
0.9970703125,
0.00017762184143066406,
0.0028362274169921875
] |
[
0.78857421875,
0.201171875,
0.0097198486328125,
0.0003752708435058594
] |
Based on the ELCR association, the Cancer Slope Factor (CSF) serves as the cancer quality factor, with its specific values referenced from relevant sources for the involved variables. Total ELCR values ranging from 10^-6 to 10^-4 suggest an acceptable or manageable risk. Values below 10^-6 are considered negligible, while those exceeding 10^-4 indicate a significant cancer risk for humans .
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999999 |
PMC11277387_p26
|
PMC11277387
|
sec[1]/sec[4]/sec[1]/p[0]
|
Monte Carlo simulations and sensitivity analysis
| 4.011719 |
biomedical
|
Study
|
[
0.9990234375,
0.0001996755599975586,
0.0009145736694335938
] |
[
0.9990234375,
0.0007610321044921875,
0.00028395652770996094,
0.00004553794860839844
] |
A Monte Carlo simulation method using Crystal Ball software (version 11.1.2.4, Oracle, Inc., USA) was employed for sensitivity analysis, involving 1000 repetitions. This stochastic model aims to mitigate uncertainty in risk estimation compared to deterministic methods that rely on single-point variables. The advantage of this method lies in its ability to demonstrate the impact of each parameter on the estimated risk values; parameters with higher coefficients exert more significant effects .
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277387_p27
|
PMC11277387
|
sec[1]/sec[5]/p[0]
|
Statistical analysis
| 3.628906 |
biomedical
|
Study
|
[
0.99462890625,
0.0002777576446533203,
0.00495147705078125
] |
[
0.998046875,
0.0018310546875,
0.00029659271240234375,
0.00006073713302612305
] |
Statistical treatment of data collected at sampling stations and their interrelationships involved the use of descriptive statistics, encompassing metrics like minimum, maximum, percentile, median, mean, standard deviation, and correlation analysis using Stata 14 software. Additionally, spatial analysis was conducted using ArcGIS 10.1, employing the kriging interpolation method to generate independent raster layers, which were then visualized and analyzed.
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999995 |
PMC11277387_p28
|
PMC11277387
|
sec[2]/sec[0]/p[0]
|
Heavy metal levels
| 4.175781 |
biomedical
|
Study
|
[
0.99267578125,
0.0004761219024658203,
0.00699615478515625
] |
[
0.99951171875,
0.0001354217529296875,
0.0002880096435546875,
0.00003409385681152344
] |
Table 1 reveals that the mean concentrations of As, Cd, Cr, and Pb in soil samples were 10, 0.24, 84, and 22 mg/kg, respectively. The ranking of HMs was determined as Cr > Pb > As > Cd. Additionally, the mean concentrations of As, Cd, Cr, and Pb in settled dust particle samples were 9, 4, 6, and 2 mg/kg, respectively. The ranking of HMs in settled dust particles was determined as As > Cr > Cd > Pb. The concentrations of HMs in settled dust particles were lower than the recommended values for environmental protection agency in Iran. However, in soil samples, 23 % of the sampling sites exceeded the recommended range for Cr, 6 % for Pb, and 3 % for As. Table 2 shows HMs levels compared to the other areas of the world. According to the results, As levels were lower than those observed in studies from Canada , Mongolia , and Kerman (Iran) . Likewise, the levels of Cd, Cr, and Pb were below those reported in studies from Canada and Germany . However, Cd levels in settled dust were higher, and Mongolian studies indicated even greater Cd levels in settled dust. This discrepancy may be related to road traffic and transportation activities. Table 1 Statistical descriptive of HMs in soil (n: 30) and settled dust (n: 30) samples in the study area (μg/L). Table 1 Element Soil (n: 30) Settled dust (n: 30) As Cd Cr Pb Fe As Cd Cr Pb Fe Mean 10 0.24 83 22 2.9 % 9 4 6 2.3 3 % Std. deviation 7 0.03 47 41 0.4 % 2 1 1 0.4 0.3 % Median 10 0.24 67 11 2.9 % 10 4 6 2.3 2.9 % Max 44 0.29 220 211 3.8 % 13 6 9 3.2 3.8 % Min 2.50 0.15 37 6 2.1 % 5 3 4 1.7 2.2 % CV 0.72 0.13 0.57 1.85 0.14 0.24 0.23 0.19 0.18 0.12 Table 2 Heavy metal contents in some areas of the world and comparison with soil quality recommendations (mg/kg). Table 2 Element As Cd Cr Pb Fe Type Source Mean 10 0.24 83 22 2.9 % Soil This study 9 4 6 2.3 3 % Settled dust Background soil 4.1 0.2 43 6 % 2.9 soil Dried bed Urmia Lake 12.1 0.5 38 10 1.8 % Bed of Lake Urmia Background of Isfahan, Iran 0.26 85 28 2.6 % soil Background of Turkey 8 0.2 93 33 2.6 % soil Background of Germany N.D 1.50 45 171 soil Iran-EPA guidelines 18 1 110 50 – Agricultural land Canadian soil 12 1.4 64 70 Agricultural land Ulaanbaatar, Mongolia 16 4.8 70 51 – Settled dust Kerman, Iran 10.9 0.34 28 45 Road Dust Earth's soil 5 0.11 70 29 3.2 % Earth's crust 1 0.6 0.1 14 4.1 %
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277387_p29
|
PMC11277387
|
sec[2]/sec[0]/p[1]
|
Heavy metal levels
| 1.956055 |
biomedical
|
Study
|
[
0.62890625,
0.0012044906616210938,
0.369873046875
] |
[
0.9453125,
0.05340576171875,
0.0006494522094726562,
0.00047516822814941406
] |
The concentrations of As, Pb, and Cr in settled dust particles in the northern region were lower than those in the surrounding areas of the lake. It could be due to the presence of water in the lake bed near the northern regions, as previous reports indicate that areas extending to the west and east of the lake had higher concentrations due to the dryness of the lake bed in those areas .
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11277387_p30
|
PMC11277387
|
sec[2]/sec[0]/p[2]
|
Heavy metal levels
| 4.1875 |
biomedical
|
Study
|
[
0.99609375,
0.00047397613525390625,
0.0035400390625
] |
[
0.99951171875,
0.0001195669174194336,
0.0003020763397216797,
0.000036776065826416016
] |
The present study computed low coefficient of variance (CV) values (CV < 1) for all soil sampling sites in relation to HMs except for Pb. The highest CV was observed in soil samples, particularly for Pb (1.85), As (0.72), Cr (0.57), and Cd (0.13). This indicates a much greater variability in Pb concentrations across the sampling sites, which can skew the overall analysis and interpretation of HMs distribution. The variability of HMs in both soil and settled dust samples can be assessed using CV. For settled dust samples, all CV values were under 0.5, with As having the highest CV of 0.24. The CV values falling within the range of 21 %–50 % indicate moderate variability, while those between 50 % and 100 % suggest a higher degree of variability, and CV values surpassing 100 % indicate severe variability . A study conducted in four cities (Balkhash, Ust-Kamenogorsk, Ridder, and Shymkent) in Kazakhstan aimed to determine soil contamination levels. The CV for metals such as Pb, Cd, Cu, Zn, and Cr ranged from 35.4 to 282.3 % across all cities. Kazakhstan, due to the consequences of the drying up of Aral Sea and anthropogenic activities, is very similar to arid studies . Research undertaken in Hangzhou, China, revealed that soil concentrations of As, Cd, Cr, and Pb met environmental standards, yet As and Cd surpassed background levels due to human influence. The CV for these metals was below 1. As and Cd exhibited higher concentrations with a CV of 0.5 < CV < 1, suggesting non-uniformity. These outcomes were in line with those of the present investigation although some metal origins in our study were linked to the desiccated lake bed, contributing to soil and air pollution through dust dispersion .
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11277387_p31
|
PMC11277387
|
sec[2]/sec[0]/p[3]
|
Heavy metal levels
| 1.999023 |
other
|
Study
|
[
0.326416015625,
0.0011453628540039062,
0.67236328125
] |
[
0.87646484375,
0.1199951171875,
0.0029277801513671875,
0.000797271728515625
] |
A distinct study focusing on sediments from the Vistula River and surrounding soils in Poland, situated in the heart of Europe, found elevated levels of HMs in the sediments relative to the soil. This underscores the significance of addressing the wider ramifications of climate change, which go beyond immediate issues like droughts and water scarcity, potentially affecting extensive environmental regions and jeopardizing biodiversity. The ongoing drought trend in the Middle East, akin to the outcomes of this research, implies similar repercussions for drying lakes like Lake Urmia and the Aral Sea .
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277387_p32
|
PMC11277387
|
sec[2]/sec[1]/p[0]
|
Spatial distribution and source apportionment of HMs in soil and settled dust samples
| 4.144531 |
biomedical
|
Study
|
[
0.98291015625,
0.00041103363037109375,
0.01690673828125
] |
[
0.99951171875,
0.0002601146697998047,
0.00019598007202148438,
0.00003129243850708008
] |
According to USEPA recommendations, the common method for determining the sources of metal emissions in soil and air sinks is principal component analysis (PCA). In this study, the results of PCA analysis are shown in Fig. S1 and details are presented in Table 3 . Three main groups were obtained for soil samples and sedimented dust particles, including the first group of As and Pb, the second group of Cd, and the third group of Cr. Generally, for Pb and As, vehicular traffic and the lakebed substrate as Aeolian dust are potential sources; for Cd, agricultural activities; and for Cr, natural background and soil origins of the region may be responsible. These origins are mentioned in other studies [ , , , ]. Table 3 Principal component analysis of heavy metal in soil and atmospheric settled dust. Table 3 Variable Component in soil Component in settled dust 1 2 3 1 2 3 As 0.62 −0.13 0.11 0.55 −0.06 −0.62 Cd 0.22 0.93 0.27 0.46 −0.49 0.68 Cr −0.42 0.14 0.86 0.35 0.86 0.33 Pb 0.58 0.30 0.39 0.59 −0.05 0.16 Eigenvalue 2.09 0.96 0.79 2.2 0.87 0.56 Variance% 52 24 19 56 21 11 Cumulative variances% 52 76 96 56 78 92
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277387_p33
|
PMC11277387
|
sec[2]/sec[1]/p[1]
|
Spatial distribution and source apportionment of HMs in soil and settled dust samples
| 3.808594 |
biomedical
|
Study
|
[
0.970703125,
0.00046896934509277344,
0.029052734375
] |
[
0.99951171875,
0.00041675567626953125,
0.0001531839370727539,
0.00004214048385620117
] |
The correlation between HMs is another method for identifying emission sources. The Spearman correlation matrix in Fig. 2 (a and b) illustrates the relationships among arsenic As, Cd, Cr, and Pb. In soil and settled dust samples, the distribution of HMs was irregular due to weak correlations, except for a relatively strong correlation between As and Pb. Overall, significant correlations between HMs in soil and settled dust particles were not found (R > 0.9, P-value <0.05). However, a correlation between As and Pb in both soil and air particles sinks indicated similar emission sources. These findings were confirmed by PCA . Fig. 2 Spearman correlation coefficients between HMs values in soil and settled dust samples (*P value <0.05, ** P value <0.01). Fig. 2
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999995 |
PMC11277387_p34
|
PMC11277387
|
sec[2]/sec[1]/p[2]
|
Spatial distribution and source apportionment of HMs in soil and settled dust samples
| 4.171875 |
biomedical
|
Study
|
[
0.9892578125,
0.0005388259887695312,
0.01032257080078125
] |
[
0.99951171875,
0.0001596212387084961,
0.00023829936981201172,
0.000037610530853271484
] |
In order to delve deeper into the spatial variances of metals and determine their origins, we employed Ordinary Kriging. This technique facilitated the representation of these variances through the generation of color-coded raster maps. Rooted in a stochastic framework, this method highlights regions with elevated concentrations using vivid red markings, while regions with lower concentrations are depicted with green hues. The map illustrating the zoning of HMs in the soil is presented in Fig. S2 . These maps exhibit analogous dispersion patterns, with the eastern portion of the study area indicating areas of heightened activity. Different factors contribute to soil contamination including dust, the arid lakebed substrate, agricultural practices, vehicular traffic, and industrial activities. Spatial analysis revealed different dispersion patterns for Cd and Cr settled dust . This disparity can be attributed to their distinct emission sources, while Pb and As displayed congruent distribution patterns in settled dust. The sediment substrate of the lake may have an effect in this regard . Hotspots for Pb and As were identified in the eastern sectors of the study area, notably in Shabestar city. An essential consideration regarding pollution distribution is the predominant wind direction, which blows from west to east in this vicinity and potentially carries pollutants towards Shabestar. Previous studies have cited pesticides and chemical fertilizers as sources of Cd. The source of Cr in this study may originate from natural sources such as lithogenic components and soil parent materials, consistent with prior studies [ , , ]. However, As and Pb likely stem from transit routes and the dry lakebed substrate .
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277387_p35
|
PMC11277387
|
sec[2]/sec[2]/p[0]
|
EFs assessment
| 1.892578 |
biomedical
|
Study
|
[
0.505859375,
0.0011959075927734375,
0.4931640625
] |
[
0.958984375,
0.0396728515625,
0.0006747245788574219,
0.0004425048828125
] |
Exploring soil pollution often involves utilizing EFs as a method to pinpoint potential sources of HMs, whether originating from natural occurrences or external human activities. In Fig. 3 , box plots illustrate the descriptive statistics of EF for four HMs. Fig. 3 Descriptive statistic of enrichment factor values in soil and settled dust samples. Fig. 3
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999994 |
PMC11277387_p36
|
PMC11277387
|
sec[2]/sec[2]/p[1]
|
EFs assessment
| 4.082031 |
biomedical
|
Study
|
[
0.9931640625,
0.0003299713134765625,
0.006561279296875
] |
[
0.99951171875,
0.00022113323211669922,
0.00021564960479736328,
0.000032961368560791016
] |
The mean EF values for As, Cd, Cr and Pb were 2.3, 4.2, 1.6, and 3.8, respectively, in settled dust samples, and 2.49, 1.19, 1.94, and 3.73 in soil samples. As and Cr in settled dust samples were characterized as undergoing slight enrichment, while Cd (in 83 % of sampled areas) and Pb (in 76 % of sampled areas) were labeled as experiencing moderate enrichment. Soil samples display similar patterns; Pb (in 13 % of sampled areas), As (in 20 % of sampled areas), and Cr (in 16 % of sampled areas) fall under the moderate enrichment classification. Cd is categorized as experiencing slight enrichment. Additionally, human activities such as road traffic and dust from the dry lakebed could contribute to the increased EFs. These findings have been noted in previous studies conducted in Iran and other countries [ , , ].
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277387_p37
|
PMC11277387
|
sec[2]/sec[3]/p[0]
|
PER index
| 4.171875 |
biomedical
|
Study
|
[
0.99560546875,
0.0002732276916503906,
0.004150390625
] |
[
0.99951171875,
0.00026988983154296875,
0.00022029876708984375,
0.000030219554901123047
] |
As a deterministic method for determining the sensitivity of nature and organisms to the toxicity of HMs, the PER is commonly used in soil and airborne particle studies . The PER index ( E j i ) for As, Cd, Cr, and Pb, along with separate PER for soil and airborne particles, is presented in Table 4 . The E j i values, along with the toxicity classification in soil, were identified as Cd > As > Pb > Cr, and in settled dust particles as Cd > Pb > As > Cr. Table 4 Ecological risk ( E j i ) of each HMs and index (n: 30). Table 4 Sampling zones (S.Z) E j i PER Soil samples Settled dust samples As Cd Cr Pb As Cd Cr Pb Soil Settled dust Mean 25 36 4 19 2.3 13.0 0.3 12 83 27 St. deviation 18 5 2 34 0.6 3.1 0.1 2.2 50 5 Max 108 44 10 176 3.2 18.0 0.5 16 326 37 Min 6 23 2 5 1.2 9.0 0.2 9 49 20 E j i <40 97 % (S.Z) 90 % (S.Z) 100 % (S.Z) 93 % (S.Z) 100 % (S.Z) 100 % (S.Z) 100 % (S.Z) 100 % (S.Z) 40≤ E j i <80 10 % (S.Z) 80≤ E j i <160 3 % (S.Z) 7 % (S.Z) PER <150 93 % (S.Z) 100 % (S.Z) 300≤ PER ≤600 7 % (S.Z)
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277387_p38
|
PMC11277387
|
sec[2]/sec[3]/p[1]
|
PER index
| 3.820313 |
biomedical
|
Study
|
[
0.9404296875,
0.0006742477416992188,
0.058868408203125
] |
[
0.9990234375,
0.0005402565002441406,
0.00018513202667236328,
0.000053882598876953125
] |
The highest ecological risk index for Cd and Pb was observed in 7 % of the sampling areas, and considerable E j i values were classified at considerable risk. These areas are aligned with the eastern regions of the study area, as indicated in the zoning map . Airborne particle samples were classified as low-risk E j i . The E j i levels for As, Cd, and Cr were categorized as "negligible" with low risk. Previous studies have reported high risk relative to Cd . The total pollution index PER in 7 % of the soil sampling areas was identified as severely polluted. These areas are situated in the eastern region of the study area, as depicted in zoning map in Fig. 4 . However, both central and eastern regions exhibited higher sensitivity to HMs in sedimented dust particles. Ultimately, based on the ecological risk index calculated for HMs in sampling areas, low risk (PER <150) was estimated in 93 % of the sampling locations. Fig. 4 Spatial distribution of potential ecological risk in north area of Lake Urmi Fig. 4
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277387_p39
|
PMC11277387
|
sec[2]/sec[4]/p[0]
|
Health risk assessment and sensitivity analysis
| 4.15625 |
biomedical
|
Study
|
[
0.99951171875,
0.00025010108947753906,
0.0003674030303955078
] |
[
0.99951171875,
0.0001804828643798828,
0.0004341602325439453,
0.0000464320182800293
] |
Table 5 illustrates the non-carcinogenic and lifetime carcinogenic risks associated with dermal, inhalation, and ingestion exposures based on deterministic modeling. The gastrointestinal route is the main exposure pathway to humans for HMs. Non-carcinogenic risks, with HQ < 1, were deemed non-hazardous in all areas. However, Pb and As had HQ values higher than Cd and Cr, warranting attention. These findings are consistent with previous studies . Table 5 Carcinogenic and non-carcinogenic cancer risks. Table 5 Exposure source Hazard quotient As Cd Cru Pb Total metals Soil mean 8.86E-03 6.05E-05 2.47E-03 1.42E-03 1.26E-02 SD 6.25E-03 7.93E-06 1.39E-03 2.62E-03 Settled dust mean 8.11E-04 1.10E-04 1.92E-05 1.49E-04 1.08E-03 SD 1.96 E −04 2.61E-05 3.79E-06 2.82E-05 Cancer risk Total pathways Soil mean 3.98E-06 3.77E-07 1.10E-05 4.88E-08 1.50E-05 SD 2.94E-06 4.16E-08 6.20E-06 8.75E-08 Settled dust mean 3.77E-07 6.79E-07 8.55E-08 6.97E-09 1.15E-06 SD 9.13E-08 1.61E-07 1.69E-08 1.11E-08
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277387_p40
|
PMC11277387
|
sec[2]/sec[4]/p[1]
|
Health risk assessment and sensitivity analysis
| 3.699219 |
biomedical
|
Study
|
[
0.98193359375,
0.00043201446533203125,
0.01776123046875
] |
[
0.9990234375,
0.0007920265197753906,
0.00022327899932861328,
0.000060558319091796875
] |
In soil samples, the mean ELCR values for Cd, As, Cr, and Pb through all exposure pathways were within the acceptable risk range (<10 −5 ). However, Cr in soil was higher than the other HMs. The average gastrointestinal ELCR for As, Cd, and Cr in soil was Cr > As > Cd > Pb, and in airborne particles was Cd > As > Cr > Pb. As and Cr posed significant risks, which is consistent with findings of studies conducted around the lake .
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277387_p41
|
PMC11277387
|
sec[2]/sec[4]/p[2]
|
Health risk assessment and sensitivity analysis
| 2.019531 |
biomedical
|
Study
|
[
0.96875,
0.001667022705078125,
0.02960205078125
] |
[
0.8193359375,
0.173583984375,
0.005146026611328125,
0.0018711090087890625
] |
The cumulative carcinogenic risk of the four metals in soil and airborne particles was less than.
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277387_p42
|
PMC11277387
|
sec[2]/sec[4]/p[3]
|
Health risk assessment and sensitivity analysis
| 4.121094 |
biomedical
|
Study
|
[
0.99951171875,
0.00023865699768066406,
0.00033855438232421875
] |
[
0.99951171875,
0.00011473894119262695,
0.0002982616424560547,
0.00005429983139038086
] |
10 −3, which was higher than similar studies, indicating the need for further control and supplementary studies. Despite being transported through dermal, oral, and respiratory routes, As posed minimal risks [ , , ].Mont Carlo simulation as a stochastic model confirms these results. Sensitivity analysis was utilized to determine the significant role of the most influential factor in potential carcinogenicity associated with HMs exposure. In this stochastic model, uncertainty has been incorporated to predict the ELCR, and the values of mean, standard deviation, 95th percentile, and 5th percentile are detailed in Fig. S4 . Sensitivity analysis indicated that As concentration in soil and particles, Cd and Cr IngR, and Pb exposure duration in soil with exposure frequency in settled dust were the most important factors in carcinogenic risk. These results may vary due to the uncertainty between regions and day-to-day variability. The results showed an inverse correlation in exposure pathways and carcinogenicity. Previous studies have confirmed this finding .
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999995 |
PMC11277387_p43
|
PMC11277387
|
sec[2]/sec[4]/sec[0]/p[0]
|
Strengths and weaknesses
| 4.042969 |
biomedical
|
Study
|
[
0.99755859375,
0.0002219676971435547,
0.0023403167724609375
] |
[
0.99853515625,
0.0003044605255126953,
0.0009179115295410156,
0.00004410743713378906
] |
The extensive collection and analysis of data on heavy metal concentrations in soil and settled dust samples, along with the use of advanced statistical methods such PCA and Ordinary Kriging for spatial distribution assessment, and the detailed health risk evaluation considering sensitivity analysis, provide a solid foundation for understanding the environmental and health effects of heavy metal contamination. However, there are weaknesses that need to be addressed. This study requires supplementary research. The variability in heavy metal distribution, particularly for Pb, needs more in-depth analysis to clarify data inconsistencies. Comparisons with recommended environmental standards and findings from other regions should be more detailed in supplementary studies to strengthen the study's context. Ensuring rigorous rechecking and validation of results against existing literature will be essential to confirm the accuracy and reliability of the conclusions in the future.
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11277387_p44
|
PMC11277387
|
sec[2]/sec[4]/sec[1]/p[0]
|
Suggestions for future research
| 4.03125 |
biomedical
|
Review
|
[
0.9921875,
0.0009937286376953125,
0.0068817138671875
] |
[
0.1273193359375,
0.007770538330078125,
0.86474609375,
0.0003292560577392578
] |
• Investigate the long-term trends of HMs concentrations in soil and settled dust particles to assess temporal variations and potential impacts of climate change. • Conduct detailed source apportionment studies using advanced techniques such as receptor modeling to identify specific anthropogenic and natural sources of HMs pollution. • Explore the potential health effects of chronic exposure to HMs in vulnerable populations, such as children and pregnant women, through epidemiological studies. • Assess the effectiveness of current pollution control measures and remediation strategies in mitigating HMs contamination in soil and air sinks. • Investigate the role of emerging pollutants and their interactions with HMs in influencing environmental and human health outcomes. • Explore the impact of land-use changes and urbanization on HMs pollution patterns to inform sustainable land management practices. • Investigate the fate and transport mechanisms of HMs in different environmental compartments, including soil, water, and biota, to better understand their environmental behavior. • Conduct interdisciplinary studies integrating environmental science, public health, and social sciences to develop holistic approaches for addressing HMs pollution and its societal impacts. • Explore innovative technologies and monitoring techniques for real-time detection and assessment of HMs pollution in soil and air sinks. • Collaborate with stakeholders, including local communities, policymakers, and industry partners, to develop and implement integrated pollution management strategies aimed at reducing HMs exposure and protecting human and environmental health.
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11277387_p45
|
PMC11277387
|
sec[3]/p[0]
|
.Conclusion
| 4.101563 |
biomedical
|
Study
|
[
0.9951171875,
0.0005292892456054688,
0.00457763671875
] |
[
0.99951171875,
0.0001360177993774414,
0.00022554397583007812,
0.00004661083221435547
] |
This study provides valuable insights into the levels, distribution, and potential risks associated with heavy metal pollution in soil and settled dust particles in the study area. The findings reveal that Cr, Pb, As, and Cd are among the prevalent HMs, with varying concentrations observed in different environmental compartments. Notably, while settled dust particles generally exhibit lower HMs concentrations compared to soil samples, certain areas still exceed recommended limits, particularly for Cr, Pb, and As in soil.
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11277387_p46
|
PMC11277387
|
sec[3]/p[1]
|
.Conclusion
| 3.001953 |
biomedical
|
Study
|
[
0.95849609375,
0.0008826255798339844,
0.040802001953125
] |
[
0.9951171875,
0.00402069091796875,
0.0005559921264648438,
0.0001475811004638672
] |
The spatial distribution analysis highlights the influence of local environmental factors, such as proximity to water bodies, on HMs concentrations, emphasizing the need for comprehensive understanding and monitoring pollution sources. Moreover, the low CV observed in settled dust samples suggests relatively uniform contamination patterns, contrasting with the higher variability observed in soil samples.
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11277387_p47
|
PMC11277387
|
sec[3]/p[2]
|
.Conclusion
| 2.056641 |
biomedical
|
Other
|
[
0.61669921875,
0.0011072158813476562,
0.382080078125
] |
[
0.06451416015625,
0.931640625,
0.0032825469970703125,
0.0005173683166503906
] |
Further investigation into the sources of HMs pollution, including anthropogenic activities and natural processes, is essential for developing targeted mitigation strategies. Advanced techniques such as PCA and ordinary kriging can aid in identifying emission sources and spatial variability, informing pollution control measures and land management practices.
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999994 |
PMC11277387_p48
|
PMC11277387
|
sec[3]/p[3]
|
.Conclusion
| 2.802734 |
biomedical
|
Study
|
[
0.990234375,
0.0006990432739257812,
0.0089874267578125
] |
[
0.8447265625,
0.137451171875,
0.0170440673828125,
0.0009307861328125
] |
Health risk assessments underscore the importance of considering exposure pathways and cumulative risks associated with HMs contamination. Although non-carcinogenic risks were generally deemed non-hazardous, attention is warranted for Cr and As due to their higher HQ values.
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11277387_p49
|
PMC11277387
|
sec[3]/p[4]
|
.Conclusion
| 3.423828 |
biomedical
|
Study
|
[
0.94287109375,
0.0005369186401367188,
0.05670166015625
] |
[
0.99755859375,
0.002040863037109375,
0.0003712177276611328,
0.0000966191291809082
] |
Finally, this study employs advanced techniques like PCA and ordinary kriging to provide innovative insights into the spatial distribution and sources of HMs pollution. By integrating comprehensive health risk assessments, it offers a novel understanding of contamination patterns in soil and settled dust particles. These findings inform targeted mitigation strategies and effective pollution management practices.
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999999 |
PMC11277387_p50
|
PMC11277387
|
sec[3]/p[5]
|
.Conclusion
| 2.804688 |
biomedical
|
Other
|
[
0.92822265625,
0.0006251335144042969,
0.0714111328125
] |
[
0.074951171875,
0.89892578125,
0.0256195068359375,
0.0005283355712890625
] |
To address the gaps identified in this study and contribute to effective pollution management, future research endeavors should focus on long-term trends, source apportionment, health effects, and innovative monitoring technologies. Collaboration among stakeholders, interdisciplinary research approaches, and community engagement will be crucial in developing sustainable solutions to mitigate heavy metal pollution and safeguard human and environmental health.
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11277387_p51
|
PMC11277387
|
sec[4]/p[0]
|
Data availability
| 0.800293 |
other
|
Other
|
[
0.1405029296875,
0.00298309326171875,
0.8564453125
] |
[
0.004604339599609375,
0.9931640625,
0.0016813278198242188,
0.0007853507995605469
] |
All the relevant data are included in the manuscript and the supplementary document. No separate repository is attached.
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11277387_p52
|
PMC11277387
|
sec[5]/p[0]
|
CRediT authorship contribution statement
| 0.969727 |
other
|
Other
|
[
0.1063232421875,
0.002994537353515625,
0.890625
] |
[
0.0031986236572265625,
0.99609375,
0.00033211708068847656,
0.00039076805114746094
] |
Saeed Hosseinpoor: Methodology, Formal analysis. Shiva Habibi: Writing – original draft, Investigation. Amir Mohammadi: Writing – review & editing, Writing – original draft, Project administration.
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999995 |
PMC11277387_p53
|
PMC11277387
|
sec[6]/p[0]
|
Declaration of competing interest
| 0.981934 |
other
|
Other
|
[
0.004878997802734375,
0.0006570816040039062,
0.99462890625
] |
[
0.0019664764404296875,
0.99658203125,
0.0006356239318847656,
0.0005822181701660156
] |
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
|
[
"Saeed Hosseinpoor",
"Shiva Habibi",
"Amir Mohammadi"
] |
https://doi.org/10.1016/j.heliyon.2024.e34198
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277410_p0
|
PMC11277410
|
sec[0]/p[0]
|
Introduction
| 3.884766 |
biomedical
|
Review
|
[
0.99853515625,
0.0007534027099609375,
0.0008225440979003906
] |
[
0.132080078125,
0.21142578125,
0.6552734375,
0.0014066696166992188
] |
Cancer continues to be a leading cause of death globally, necessitating the development of innovative strategies for treating its complex challenges . Tumors consist of a diverse population of cells, including a subgroup called cancer stem cells (CSCs), which have significant roles in tumor initiation, progression, resistance to therapy, and recurrence .
|
[
"Behrooz Johari",
"Shabnam Tavangar-Roosta",
"Mahmoud Gharbavi",
"Ali Sharafi",
"Saeed Kaboli",
"Hamed Rezaeejam"
] |
https://doi.org/10.1016/j.heliyon.2024.e34096
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11277410_p1
|
PMC11277410
|
sec[0]/p[1]
|
Introduction
| 3.958984 |
biomedical
|
Study
|
[
0.99951171875,
0.00011610984802246094,
0.00028967857360839844
] |
[
0.96044921875,
0.019287109375,
0.0201416015625,
0.0002562999725341797
] |
Notably, the NTERA-2 cancer stem-like cell line, which exhibits pluripotent characteristics similar to CSCs , serves as a relevant model for studying mechanisms related to CSCs and potential therapies . CSC activities are controlled by many intracellular and extracellular factors; these factors can be used as drug targets for the treatment of cancer .
|
[
"Behrooz Johari",
"Shabnam Tavangar-Roosta",
"Mahmoud Gharbavi",
"Ali Sharafi",
"Saeed Kaboli",
"Hamed Rezaeejam"
] |
https://doi.org/10.1016/j.heliyon.2024.e34096
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999995 |
PMC11277410_p2
|
PMC11277410
|
sec[0]/p[2]
|
Introduction
| 4.042969 |
biomedical
|
Study
|
[
0.99951171875,
0.00018298625946044922,
0.0004718303680419922
] |
[
0.64501953125,
0.25537109375,
0.0986328125,
0.0010395050048828125
] |
Transcription factors Oct4, Sox2, and Nanog are implicated in maintaining pluripotency and are also involved in the development of tumors . Oct4 (POU5F1) is one of the first transcription factors expressed in the fetus. Sox-2 is a member of the SRY family of HMG-box transcription factors and is expressed in many tumors. Both Oct4 and Sox2 are essential for self-renewal of stem cells, normal or cancer cells .
|
[
"Behrooz Johari",
"Shabnam Tavangar-Roosta",
"Mahmoud Gharbavi",
"Ali Sharafi",
"Saeed Kaboli",
"Hamed Rezaeejam"
] |
https://doi.org/10.1016/j.heliyon.2024.e34096
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277410_p3
|
PMC11277410
|
sec[0]/p[3]
|
Introduction
| 4.042969 |
biomedical
|
Study
|
[
0.99951171875,
0.00018477439880371094,
0.00022864341735839844
] |
[
0.89208984375,
0.0022602081298828125,
0.1051025390625,
0.0002930164337158203
] |
Conventional therapies such as surgery, chemotherapy, and radiation therapy have been widely used in many cancer treatments. Despite being widely used in clinics, radiation therapy has limitations, including treatment resistance and harmful effects on healthy tissues . Along with conventional remedies, targeted therapy strategies including transcription factor decoy oligodeoxynucleotides (TFD) demonstrated promising opportunities for cancer treatment by inhibiting transcription factors in cancer cells . Moreover, targeted inhibition of Sox2 and Oct4 transcription factors using decoy ODNs modulates the stemness properties of mouse embryonic stem cells .
|
[
"Behrooz Johari",
"Shabnam Tavangar-Roosta",
"Mahmoud Gharbavi",
"Ali Sharafi",
"Saeed Kaboli",
"Hamed Rezaeejam"
] |
https://doi.org/10.1016/j.heliyon.2024.e34096
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999995 |
PMC11277410_p4
|
PMC11277410
|
sec[0]/p[4]
|
Introduction
| 4.101563 |
biomedical
|
Study
|
[
0.99951171875,
0.00023090839385986328,
0.00020825862884521484
] |
[
0.99072265625,
0.00032901763916015625,
0.00893402099609375,
0.0001017451286315918
] |
In recent years, niosomes are a type of novel drug delivery system that can be used to carry both amphiphilic and lipophilic drugs. They are non-ionic surfactant-based vesicles that offer several advantages over liposomes, including stability, low toxicity, and lower cost. However, current approaches have limitations in maximizing the efficiency of drug delivery and targeting specific cellular pathways . Niosomes have been extensively studied as a drug carrier for various medications, including anticancer and anti-infective agents. Different synthesis approaches have been explored to enhance the drug delivery capacity of niosomes, such as proniosomes, discomes, and aspasomes. Moreover, niosomes have been shown to have potential in targeted ocular, topical, and parenteral administration. The use of 3D printing technology allows for precise control over the size, shape, and composition of niosomes, which can enhance their drug-delivery capabilities . In our previous study, the obtained results confirm that niosomes nanocarriers containing Nanog decoy ODNs can potentially suppress the metastatic ability of glioblastoma cells .
|
[
"Behrooz Johari",
"Shabnam Tavangar-Roosta",
"Mahmoud Gharbavi",
"Ali Sharafi",
"Saeed Kaboli",
"Hamed Rezaeejam"
] |
https://doi.org/10.1016/j.heliyon.2024.e34096
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11277410_p5
|
PMC11277410
|
sec[0]/p[5]
|
Introduction
| 4.113281 |
biomedical
|
Study
|
[
0.99951171875,
0.00015354156494140625,
0.00019073486328125
] |
[
0.99853515625,
0.000560760498046875,
0.0006623268127441406,
0.000057756900787353516
] |
Moreover, although zinc nanoparticles (ZnNPs) demonstrate encouraging properties as radiosensitizers and cytotoxic agents against cancer cells, there are still challenges in effectively incorporating them into drug delivery systems and directing them toward CSCs . In this particular context, we propose an innovative methodology to specifically target NTERA-2 cancer stem-like cells by utilizing a hybrid nanocarrier system consisting of niosomes and zinc nanoparticles that encapsulate decoy oligodeoxynucleotides (ODNs) that target Sox2 and Oct4 transcription factors .
|
[
"Behrooz Johari",
"Shabnam Tavangar-Roosta",
"Mahmoud Gharbavi",
"Ali Sharafi",
"Saeed Kaboli",
"Hamed Rezaeejam"
] |
https://doi.org/10.1016/j.heliyon.2024.e34096
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11277410_p6
|
PMC11277410
|
sec[0]/p[6]
|
Introduction
| 4.117188 |
biomedical
|
Study
|
[
0.99951171875,
0.0003528594970703125,
0.00015497207641601562
] |
[
0.99462890625,
0.0015869140625,
0.0037288665771484375,
0.00016772747039794922
] |
The objective of this approach is to overcome the limitations of conventional therapies and existing drug delivery systems by simultaneously targeting CSC-related pathways and enhancing therapeutic effectiveness while minimizing unwanted effects on non-targeted cells. By combining the advantages of niosomes and ZnNPs, our approach capitalizes on their respective strengths while mitigating their weaknesses, thus presenting a promising strategy for CSC-targeted cancer therapy. Additionally, through the evaluation of the antitumor effects of this hybrid nanocarrier system under X-ray exposure conditions, we aim to gain insight into its potential as a combination therapy approach, which could provide new perspectives on overcoming treatment resistance and improving patient outcomes in cancer therapy.
|
[
"Behrooz Johari",
"Shabnam Tavangar-Roosta",
"Mahmoud Gharbavi",
"Ali Sharafi",
"Saeed Kaboli",
"Hamed Rezaeejam"
] |
https://doi.org/10.1016/j.heliyon.2024.e34096
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11277410_p7
|
PMC11277410
|
sec[1]/sec[0]/p[0]
|
Reagents and materials
| 2.021484 |
biomedical
|
Other
|
[
0.99658203125,
0.0005373954772949219,
0.002941131591796875
] |
[
0.156494140625,
0.84033203125,
0.0018377304077148438,
0.001434326171875
] |
Reagents used for the synthesis of nanocarrier systems: BSA , sorbitan monooleate or span80 , polyoxyethylene sorbitan monooleate or tween 80 , cholesterol (CAS.57-88-5), chloroform (Sigma Aldrich Co.), methanol (Sigma Aldrich Co.), and acetone (Emertat Co., Iran), Zn SO 4 (Sigma-Aldrich Co., USA), ODNs (Bioneer Inc. Daejeon, Korea). l -Glutamine (Merck; CAS 56-85-9), Dulbecco's modified eagle's medium (DMEM) , fetal bovine serum (FBS) (ES-020-B), MTT , penicillin−streptomycin , trypsin-EDTA (Sigma-Aldrich), phosphate-buffered saline (PBS) (was prepared in the laboratory), Annexin V-FITC/PI kit (Sigma, USA), PI (Sigma−Aldrich), dimethyl sulfoxide (DMSO), cell culture plates (SPL Life Sciences).
|
[
"Behrooz Johari",
"Shabnam Tavangar-Roosta",
"Mahmoud Gharbavi",
"Ali Sharafi",
"Saeed Kaboli",
"Hamed Rezaeejam"
] |
https://doi.org/10.1016/j.heliyon.2024.e34096
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11277410_p8
|
PMC11277410
|
sec[1]/sec[1]/sec[0]/p[0]
|
Cell culture
| 3.955078 |
biomedical
|
Study
|
[
0.99951171875,
0.00022912025451660156,
0.0003237724304199219
] |
[
0.93310546875,
0.0654296875,
0.0010671615600585938,
0.0004935264587402344
] |
The NTERA-2 cell line was purchased from the Iranian Biological Resource Center. NTERA-2 cancer cell line was cultured in DMEM medium containing 4.5 g/L glucose, 2 mM Glutamine, 10 % Fetal Bovine Serum (FBS), 100 units/mL penicillin, and 100 μg/mL streptomycin at 37 °C in a humidified atmosphere with 5 % CO 2 incubator.
|
[
"Behrooz Johari",
"Shabnam Tavangar-Roosta",
"Mahmoud Gharbavi",
"Ali Sharafi",
"Saeed Kaboli",
"Hamed Rezaeejam"
] |
https://doi.org/10.1016/j.heliyon.2024.e34096
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277410_p9
|
PMC11277410
|
sec[1]/sec[1]/sec[1]/p[0]
|
Sox2-Oct4 decoy and scrambled ODNs design
| 4.15625 |
biomedical
|
Study
|
[
0.99951171875,
0.00017333030700683594,
0.00027680397033691406
] |
[
0.99072265625,
0.008758544921875,
0.00048351287841796875,
0.0001418590545654297
] |
Sox2-Oct4 decoy ODNs synthesized according to the promoter region of the related Homo sapience Sox2 gene . Sox2-Oct4 decoy (DEC) and scrambled (SCR) ODNs sequences for synthesis ordered to Bioneer Inc (Korea). Also, sequences of SCR were designed by making mutations in the core binding site of Sox2-Oct4 ODN. Phosphorothioate (PS) modifications at 3′ and 5′ of ODNs sequence can protect ODNs from nucleases. Cy3 fluorescent dye was added at the 3′ terminus of ODNs to investigate the cell uptake efficiency of nanocarrier-containing ODNs into cells. In the sequence of designed ODNs, the core binding site, PS modifications, and mutations are shown by boldface, an asterisk (*), and underlining/italics, respectively.
|
[
"Behrooz Johari",
"Shabnam Tavangar-Roosta",
"Mahmoud Gharbavi",
"Ali Sharafi",
"Saeed Kaboli",
"Hamed Rezaeejam"
] |
https://doi.org/10.1016/j.heliyon.2024.e34096
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11277410_p10
|
PMC11277410
|
sec[1]/sec[1]/sec[1]/p[1]
|
Sox2-Oct4 decoy and scrambled ODNs design
| 2.46875 |
biomedical
|
Other
|
[
0.98876953125,
0.00115966796875,
0.01027679443359375
] |
[
0.09307861328125,
0.9013671875,
0.004638671875,
0.0011301040649414062
] |
Decoy ODNs sequences:
|
[
"Behrooz Johari",
"Shabnam Tavangar-Roosta",
"Mahmoud Gharbavi",
"Ali Sharafi",
"Saeed Kaboli",
"Hamed Rezaeejam"
] |
https://doi.org/10.1016/j.heliyon.2024.e34096
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
es
| 0.999995 |
PMC11277410_p11
|
PMC11277410
|
sec[1]/sec[1]/sec[1]/p[2]
|
Sox2-Oct4 decoy and scrambled ODNs design
| 2.675781 |
biomedical
|
Other
|
[
0.9892578125,
0.0011739730834960938,
0.009521484375
] |
[
0.0848388671875,
0.91357421875,
0.0007524490356445312,
0.0007963180541992188
] |
Forward: [5′‐G*CC ATTGT AATGCAATGT ATTGT GAT*G‐3′]
|
[
"Behrooz Johari",
"Shabnam Tavangar-Roosta",
"Mahmoud Gharbavi",
"Ali Sharafi",
"Saeed Kaboli",
"Hamed Rezaeejam"
] |
https://doi.org/10.1016/j.heliyon.2024.e34096
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
de
| 0.571424 |
PMC11277410_p12
|
PMC11277410
|
sec[1]/sec[1]/sec[1]/p[3]
|
Sox2-Oct4 decoy and scrambled ODNs design
| 2.333984 |
biomedical
|
Other
|
[
0.9853515625,
0.001728057861328125,
0.01311492919921875
] |
[
0.05072021484375,
0.94775390625,
0.0007147789001464844,
0.0009694099426269531
] |
Reverse: [3′‐C*GGTAACATTACGTTACATAACACTA*C‐5′]
|
[
"Behrooz Johari",
"Shabnam Tavangar-Roosta",
"Mahmoud Gharbavi",
"Ali Sharafi",
"Saeed Kaboli",
"Hamed Rezaeejam"
] |
https://doi.org/10.1016/j.heliyon.2024.e34096
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
nl
| 0.999992 |
PMC11277410_p13
|
PMC11277410
|
sec[1]/sec[1]/sec[1]/p[4]
|
Sox2-Oct4 decoy and scrambled ODNs design
| 2.121094 |
biomedical
|
Other
|
[
0.984375,
0.001422882080078125,
0.01442718505859375
] |
[
0.11248779296875,
0.88232421875,
0.0039520263671875,
0.0013341903686523438
] |
SCR ODNs sequences:
|
[
"Behrooz Johari",
"Shabnam Tavangar-Roosta",
"Mahmoud Gharbavi",
"Ali Sharafi",
"Saeed Kaboli",
"Hamed Rezaeejam"
] |
https://doi.org/10.1016/j.heliyon.2024.e34096
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
es
| 0.999994 |
PMC11277410_p14
|
PMC11277410
|
sec[1]/sec[1]/sec[1]/p[5]
|
Sox2-Oct4 decoy and scrambled ODNs design
| 2.703125 |
biomedical
|
Other
|
[
0.9912109375,
0.00102996826171875,
0.0079498291015625
] |
[
0.08966064453125,
0.90869140625,
0.0007662773132324219,
0.0007772445678710938
] |
Forward: [5′‐G*CCA GGC TAATGCAATGTA GGC TGAT*G‐3′]
|
[
"Behrooz Johari",
"Shabnam Tavangar-Roosta",
"Mahmoud Gharbavi",
"Ali Sharafi",
"Saeed Kaboli",
"Hamed Rezaeejam"
] |
https://doi.org/10.1016/j.heliyon.2024.e34096
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
de
| 0.711145 |
PMC11277410_p15
|
PMC11277410
|
sec[1]/sec[1]/sec[1]/p[6]
|
Sox2-Oct4 decoy and scrambled ODNs design
| 2.333984 |
biomedical
|
Other
|
[
0.98583984375,
0.001590728759765625,
0.012451171875
] |
[
0.051849365234375,
0.9462890625,
0.0007410049438476562,
0.0009627342224121094
] |
Reverse: [3′‐C*GGTCCGATTACGTTACATCCGACTA*C‐5′]
|
[
"Behrooz Johari",
"Shabnam Tavangar-Roosta",
"Mahmoud Gharbavi",
"Ali Sharafi",
"Saeed Kaboli",
"Hamed Rezaeejam"
] |
https://doi.org/10.1016/j.heliyon.2024.e34096
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
nl
| 0.561825 |
PMC11277410_p16
|
PMC11277410
|
sec[1]/sec[1]/sec[2]/p[0]
|
Biosynthesis of ZnNPs
| 3.945313 |
biomedical
|
Study
|
[
0.9990234375,
0.0004336833953857422,
0.00064849853515625
] |
[
0.91015625,
0.08868408203125,
0.0007524490356445312,
0.0005183219909667969
] |
To synthesize ZnNPs, 4 % w/v of BSA was dissolved in ultrapure water under vigorous stirring. Then, 0.002 % w/v of ZnSO 4 was added to the solution. After 20 min, 300 μL of NaOH 1 N was gently added drop by drop, and the temperature suddenly increased to 90 °C. The solution was left to react for 2 h, during which the color of the solution changed from colorless to yellow .
|
[
"Behrooz Johari",
"Shabnam Tavangar-Roosta",
"Mahmoud Gharbavi",
"Ali Sharafi",
"Saeed Kaboli",
"Hamed Rezaeejam"
] |
https://doi.org/10.1016/j.heliyon.2024.e34096
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11277410_p17
|
PMC11277410
|
sec[1]/sec[1]/sec[3]/p[0]
|
Preparation of nanocarriers system
| 4.160156 |
biomedical
|
Study
|
[
0.99951171875,
0.0002694129943847656,
0.00020492076873779297
] |
[
0.99853515625,
0.0008106231689453125,
0.00036644935607910156,
0.00007832050323486328
] |
The nanocarriers were prepared using a thin-film hydration method to synthesize niosomes . First, 6 % w/v of each surfactant mixture tween 80 & span 80 was mixed in a round-bottomed flask. Then, 0.01 % w/v of cholesterol was added to dissolve the mixture, and chloroform was added. The chloroform was removed by the rotary evaporator at 150 rcf under reduced pressure at 60 °C, resulting in the formation of a thin lipid layer. For the synthesis of NISM@BSA, 0.02 % w/v of BSA was dissolved in ultrapure water with constant stirring (150 rcf ) for 15 min at room temperature. In two other formulations, ODNs were added to the BSA solution and stirred (150 rcf ) for 75 min, after which the solution was added to the lipid layer slowly drop by drop (NISM@BSA-SCR, NISM@BSA-DEC). To synthesize NISM@BSA-Zn, a solution of ZnNPs (20 % v/v) was added to the BSA solution, and the resulting solution was slowly added drop by drop into the thin lipid layer. The mixture was then vigorously sonicated for 30 s with a sonicate (repeated three separate times, each time lasting 30 s). A similar procedure was followed for the NISM@BSA-SCR-Zn and NISM@BSA-DEC-Zn groups, with the addition of ZnNPs solution followed by the addition of ODNs.
|
[
"Behrooz Johari",
"Shabnam Tavangar-Roosta",
"Mahmoud Gharbavi",
"Ali Sharafi",
"Saeed Kaboli",
"Hamed Rezaeejam"
] |
https://doi.org/10.1016/j.heliyon.2024.e34096
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999999 |
PMC11277410_p18
|
PMC11277410
|
sec[1]/sec[1]/sec[4]/p[0]
|
Physicochemical characterization of NPs
| 4.214844 |
biomedical
|
Study
|
[
0.99951171875,
0.00028133392333984375,
0.0001837015151977539
] |
[
0.9990234375,
0.0003154277801513672,
0.0005769729614257812,
0.00008147954940795898
] |
The physicochemical properties of the nanocarriers were characterized using DLS, FT-IR, and FESEM techniques. FT-IR spectroscopy (Bruker, Tensor 27, Biotage, Germany) was used to determine the chemical structure of BSA, NISM, NISM@B, ZnNPs, and NISM@BSA-Zn. To prepare potassium bromide (KBr) disks, 10 % of the sample and 90 % KBr were mixed and mechanically ground, and then passed in the plate form (pressure, 10 Ton) . DLS was used to determine the polydispersity index (PDI), average hydrodynamic size of nanoparticles, and zeta potential using the Nano-Zeta sizer apparatus (Malvern Instruments, Worcestershire, UK, model Nano ZS). For each sample, 0.5 mL was diluted with ultrapure water and decanted into a Malvern sample vial before being analyzed for PDI, hydrodynamic diameter, and zeta potential. FESEM was used to determine the morphology and size of the nanoparticles. Gold was used to coat the samples, and FESEM (MIRA TESCAN, Czech Republic) was operated at an acceleration voltage of 15 kV and a scale of 35 KX magnification. The decoy ODNs entrapment efficiency (EE%) of the nanocarriers was calculated by diluting the nanocarriers in phosphate buffer and centrifuging them to collect free ODNs (repeated three times). The amount of free ODNs decoy was quantified using Nanodrop at a wavelength of 260 nm . The ODN entrapment efficiency (% EE) was calculated using the following formula: % Entrapment Efficiency ( % EE ) = Total decoy ODNs − Free decoy ODNs Total decoy ODNs × 100
|
[
"Behrooz Johari",
"Shabnam Tavangar-Roosta",
"Mahmoud Gharbavi",
"Ali Sharafi",
"Saeed Kaboli",
"Hamed Rezaeejam"
] |
https://doi.org/10.1016/j.heliyon.2024.e34096
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277410_p19
|
PMC11277410
|
sec[1]/sec[1]/sec[5]/p[0]
|
ODNs release study
| 4.085938 |
biomedical
|
Study
|
[
0.99951171875,
0.00021529197692871094,
0.000202178955078125
] |
[
0.99951171875,
0.00034046173095703125,
0.00025916099548339844,
0.00005537271499633789
] |
To assess the release behavior of ODNs from the nanocarrier systems (NISM@BSA-SCR, NISM@BSA-SCR-Zn, NISM@BSA-DEC, NISM@BSA-DEC-Zn), purified NISM@BSA-DEC or NISM@BSA-SCR in 1.5 mL of PBS was dispersed at pH 7.4 and 5.8, and placed in an incubator shaker maintained at 120 rpm and 37 °C. At predefined time intervals, the release medium was withdrawn and replaced with an equal amount of fresh release medium. The amount of released decoy ODN was quantified using spectrophotometry by a Nanodrop (Wilmington, USA) .
|
[
"Behrooz Johari",
"Shabnam Tavangar-Roosta",
"Mahmoud Gharbavi",
"Ali Sharafi",
"Saeed Kaboli",
"Hamed Rezaeejam"
] |
https://doi.org/10.1016/j.heliyon.2024.e34096
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11277410_p20
|
PMC11277410
|
sec[1]/sec[1]/sec[6]/p[0]
|
ODNs release kinetic estimation
| 4.066406 |
biomedical
|
Study
|
[
0.99951171875,
0.00021398067474365234,
0.0003113746643066406
] |
[
0.99951171875,
0.0002906322479248047,
0.0002453327178955078,
0.00004607439041137695
] |
The in vitro release of ODNs was analyzed by fitting the release data to various kinetic models to assess the release mechanism and kinetics. The kinetic model was chosen based on the lowest values of the mean squared error (MSE) and Akaike's Information Criterion (AIC) .
|
[
"Behrooz Johari",
"Shabnam Tavangar-Roosta",
"Mahmoud Gharbavi",
"Ali Sharafi",
"Saeed Kaboli",
"Hamed Rezaeejam"
] |
https://doi.org/10.1016/j.heliyon.2024.e34096
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
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