Next Generation Sequencing Quantitative Analysis of altered expression of genes in BCa cells-induced PDGFRα+ITGA11+ CAFs and other CAFs
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1061559 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR27438537_1.fastq.gz | Fastqsanger.gz | |||
| SRR27438537_2.fastq.gz | Fastqsanger.gz | |||
| SRR27438538_1.fastq.gz | Fastqsanger.gz | |||
| SRR27438538_2.fastq.gz | Fastqsanger.gz | |||
| SRR27438539_1.fastq.gz | Fastqsanger.gz |
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Project description:Background: Breast (BCa) and prostate (PCa) cancers are hormone receptor (HR)-driven cancers. Estrogen receptor alpha (ERa) is overexpressed in 70% of diagnosed BCa patients and androgen receptor (AR) is overexpressed in 80-90% of diagnosed PCa patients. Thus, BCa and PCa patients are given therapy that reduces hormone levels or directly blocks HR activity; but most patients eventually develop treatment resistance. 15-30% of BCa patients and ≥ 30% of PCa patients that acquire treatment resistance develop tumors enriched in cancer cells with low or no HR accumulation. Furthermore, 15-20% of BCa patients and 10-20% of PCa patients are intrinsically HR-negative (HR-), and thus, have intrinsic resistance to therapy. We have previously reported that interleukin-1 (IL-1) inflammatory cytokine downregulates ERa and AR mRNA in HR-positive (HR+) BCa and PCa cell lines. Additionally, we had identified pro-survival proteins and processes upregulated by IL-1 in HR+ BCa and PCa cells, that are basally high in HR- BCa and PCa cells. Therefore, we hypothesize that IL-1 confers a conserved gene expression pattern in HR+ BCa and PCa cells that mimics conserved basal gene expression patterns in HR- BCa and PCa cells, to promote HR-independent survival and tumorigenicity. Methods: To identify changes in global gene expression we performed RNA sequencing (RNA-seq) for HR+ BCa and PCa cell lines exposed to IL-1 and for untreated HR- BCa and PCa cell lines. We confirmed expression patterns of select genes by RT-qPCR and used siRNA and/or drug inhibition to silence select genes in BCa and PCa cell lines. We also compared our gene expression data with publicly available data sets from hormone receptor-independent sublines. Finally, we performed Ingenuity Pathway Analysis (IPA) to identify signaling pathways encode by our RNA-seq data set. Results: We identified 350 genes in common between BCa and PCa cells that are induced or repressed by IL-1 in HR+ cells that are, respectively, basally high or low in HR- cells. Among these genes, we identified Sequestome-1 (SQSTM1/p62) and SRY (Sex-Determining Region Y)-Box 9 (SOX9) to be essential for survival of HR- BCa and PCa cell lines. Analysis of publicly available data indicates that p62 and SOX9 expression are elevated in hormone receptor-independent BCa and PCa sublines generated in vitro, suggesting that p62 and SOX9 have a role in acquired treatment resistance. We also assessed HR- cell line viability in response verteporfin, an FDA approved therapy for macular degeneration known to target p62, and we found that verteporfin is cytotoxic for HR- cells lines. Conclusions: Taken together, our 350 gene set can be used to identify novel therapeutic targets and/or biomarkers conserved among acquired (e.g. due to inflammation) or intrinsic hormone receptor-independent BCa and PCa.
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Project description:Stromal progenitor cells of bone marrow origin are non-hematopoietic cells that give rise to osteoblasts and adipocytes in the postnatal organism. Marrow stromal cells (also known as mesenchymal stem cells - MSCs) are currently being employed in a large number of clinical trials for regenerative purposes post in vitro expansion. However, the clinical outcome has been variable, which might in part be due to the heterogeneity of the cells and the lack of a defined cell product with a molecular signature that favors tissue regeneration. In this study, we determined the cellular heterogeneity of primary stromal cultures and examined how inter-donor variation in subpopulation composition contributes to the differentiation potential of primary cultures. We profiled 136,014 stromal progenitors from 26 donors and identified 5 subpopulations that were linked to distinct bone-related pathways and genetic traits of bone mineral density and morphology. Abundancy of one cluster characterized by high expression of ITGA11 (integrin alpha-11) and genes related to matrix function, collagen organization, and elevated expression upon lineage commitment was positively correlated with osteoblastic differentiation capacity in vitro. In addition, ITGA11 protein expression in progenitor cells was a predictive marker for matrix mineralization in vitro and ectopic bone formation in vivo. Sorting stromal progenitors into ITGA11high and ITGA11low cells established cultures with high and low osteoblastic differentiation potential and revealed transcriptional differences reflective of the subpopulation-specific signature, which was not affected by siRNA mediated knockdown of ITGA11 expression. Our findings corroborate the presence of an extensive donor-dependent cellular heterogeneity that persists in cultured stromal cells, and that ITGA11 can be employed as a marker for isolating cells with high bone-forming potential, a feature likely to benefit clinical trials of bone regeneration.
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Project description:Expression data of PDGFRα- and PDGFRα+ mesenchymal stem cells isolated from mouse periosteum
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Project description:Over the last two decades, tumor-derived RNA expression signatures have been developed for the two most commonly diagnosed tumors worldwide, namely prostate and breast tumors, in order to improve both outcome prediction and treatment decision-making. In this context, molecular signatures gained by main components of the tumor microenvironment, such as cancer-associated fibroblasts (CAFs), have been explored as prognostic and therapeutic tools. Nevertheless, a deeper understanding of the significance of CAFs-related gene signatures in breast and prostate cancers still remains to be disclosed. RNA sequencing technology (RNA-seq) was employed to profile and compare the transcriptome of CAFs isolated from patients affected by breast and prostate tumors. The differentially expressed genes (DEGs) characterizing breast and prostate CAFs were intersected with data from public datasets derived from bulk RNA-seq profiles of breast and prostate tumor patients. Pathway enrichment analyses allowed us to appreciate the biological significance of the DEGs. K-means clustering was applied to construct CAFs-related gene signatures specific for breast and prostate cancer and to stratify independent cohorts of patients into high and low gene expression clusters. Kaplan-Meier survival curves and log-rank tests were employed to predict differences in the outcome parameters of the clusters of patients. Decision-tree analysis was used to validate the clustering results and boosting calculations were then employed to improve the results obtained by the decision-tree algorithm. Data obtained in breast CAFs allowed us to assess a signature that includes 8 genes (ITGA11, THBS1, FN1, EMP1, ITGA2, FYN, SPP1, and EMP2) belonging to pro-metastatic signaling routes, such as the focal adhesion pathway. Survival analyses indicated that the cluster of breast cancer patients showing a high expression of the aforementioned genes displays worse clinical outcomes. Next, we identified a prostate CAFs-related signature that includes 11 genes (IL13RA2, GDF7, IL33, CXCL1, TNFRSF19, CXCL6, LIFR, CXCL5, IL7, TSLP, and TNFSF15) associated with immune responses. A low expression of these genes was predictive of poor survival rates in prostate cancer patients. The results obtained were significantly validated through a two-step approach, based on unsupervised (clustering) and supervised (classification) learning techniques, showing a high prediction accuracy (≥ 90%) in independent RNA-seq cohorts. We identified a huge heterogeneity in the transcriptional profile of CAFs derived from breast and prostate tumors. Of note, the two novel CAFs-related gene signatures might be considered as reliable prognostic indicators and valuable biomarkers for a better management of breast and prostate cancer patients.
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