Radiation increases COL1A1, COL3A1, and COL1A2 expression in breast cancer

Yao, Guorong; Zhao, Kaiyue; Bao, Kaikai; Li, Jing

doi:10.1515/med-2022-0436

Cited by 19 publications

(14 citation statements)

References 25 publications

(40 reference statements)

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“…Specifically, there were four genes, namely PDGFRL, UCHL1, COL5A2, and COL3A1 that were common among all three-DEGs from the EMAT gene signature, the Zurich and the Stavanger cohort. Interestingly, all these genes have been implicated in promoting invasion and metastasis and associated with poor survival outcomes in breast cancer patients [32][33][34]. Previous studies also found that the expression levels of COL3A1 were higher in primary TNBC tumors compared to lymph node metastasis [35].…”

Section: Discussionmentioning

confidence: 97%

Distinct Gene Expression Profiles of Matched Primary and Metastatic Triple-Negative Breast Cancers

Kaur

Chandrashekar

Varga

et al. 2022

Cancers

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Background: Although triple-negative breast cancer (TNBC) is associated with an increased risk of recurrence and metastasis, the molecular mechanisms underlying metastasis in TNBC remain unknown. To identify transcriptional changes and genes regulating metastatic progression in TNBC, we compared the transcriptomic profiles of primary and matched metastatic tumors using massively parallel RNA sequencing. Methods: We performed gene expression profiling using formalin-fixed paraffin-embedded (FFPE) TNBC tissues of patients from two cohorts: the Zurich cohort (n = 31) and the Stavanger cohort (n = 5). Among the 31 patients in the Zurich cohort, 18 had primary TNBC tumors that did not metastasize, and 13 had primary tumors that metastasized (11 paired primary and locoregional recurrences). The Stavanger cohort included five matched primary and metastatic TNBC tumors. Significantly differentially expressed genes (DEGs; absolute fold change ≥2, p < 0.05) were identified and subjected to functional analyses. We investigated if there was any overlap between DEGs from both the cohorts with epithelial-to-mesenchymal-to-amoeboid transition (EMAT) gene signature. xCell was used to estimate relative fractions of 64 immune and stromal cell types in each RNA-seq sample. Results: In the Zurich cohort, we identified 1624 DEGs between primary TNBC tumors and matched metastatic lesions. xCell analysis revealed a significantly higher immune scores for metastatic lesions compared to paired primary tumors in the Zurich cohort. We also found significant upregulation of three MammaPrint signature genes (HRASLS, TGFB3 and RASSF7) in primary tumors that metastasized compared to primary tumors that remained metastasis-free. In the Stavanger cohort, we identified 818 DEGs between primary tumors and matched metastatic lesions. No significant differences in xCell immune scores were observed. We found that 21 and 14 DEGs from Zurich and Stavanger cohort, respectively, overlapped with the EMAT gene signature. In both cohorts, genes belonging to the MMP, FGF, and PDGFR families were upregulated in primary tumors compared to matched metastatic lesions. Conclusions: Our results suggest that distinct gene expression patterns exist between primary TNBCs and matched metastatic tumors. Further studies are warranted to explore whether these discrete expression profiles underlie or result from disease status.

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Section: Discussionmentioning

confidence: 97%

Distinct Gene Expression Profiles of Matched Primary and Metastatic Triple-Negative Breast Cancers

Kaur

Chandrashekar

Varga

et al. 2022

Cancers

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“…COL1A1 and COL6A3 have been shown to be signi cantly associated with overall survival and brain metastasis in breast cancer patients [20]. The study by Yao et al showed that COL1A1 and COL1A2 in breast cancer tissues were up-regulated by radiotherapy, and it might lead radiation-associated secondary non-breast diseases [21].…”

Section: Discussionmentioning

confidence: 99%

Exploring the key genes associated with breast cancer radiotherapy sensitivity based on the stromal-immune score and analysis of the WGCNA and ceRNA network

Sun

Zhang

et al. 2023

Preprint

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Background: Breast cancer is a highly malignant disease worldwide. Among the numerous treatment options for breast cancer, radiotherapy is one of the commonly used treatments, while there is currently no sufficient molecular biomarkers to predict prognosis and guide its application. The tumor microenvironment (TME) is an important factor affecting tumor biological function, and changes in its composition are equally relevant to tumor progression and prognosis during radiotherapy. Methods: Here, we performed bioinformatic analyses using data obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases to screen for molecular biomarkers related to TME that may influence radiotherapy sensitivity. By combining immune scores and stromal scores calculated as well as performing weighted co-expression network analysis (WGCNA), we identified key modules and hub genes to construct competing endogenous RNA (ceRNA) networks. Then, key pathways and genes were identified using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Results: Genes of the PI3K-AKT pathway in the blue module were significantly enriched. Among hub genes in the blue module, we further found that the expression levels of COL1A1, COL1A2, COL6A3, THBS2 and PDGFRB were negatively associated with radiotherapy sensitivity. Conclusion: These findings may provide new insights into the mechanisms of radiotherapy sensitivity in breast cancer patients, offering hope for the discovery of new therapeutic targets.

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“…COL1A1 and COL6A3 have been shown to be significantly associated with overall survival and brain metastasis in breast cancer patients [20]. The study by Yao et al showed that COL1A1 and COL1A2 in breast cancer tissues were up-regulated by radiotherapy, and it might lead radiation-associated secondary non-breast diseases [21].…”

Section: Discussionmentioning

confidence: 99%

Identification of key genes associated with breast cancer radiotherapy sensitivity based on the stromal-immune score and analysis of the WGCNA and ceRNA network

Sun

Zhang

et al. 2023

Preprint

View full text Add to dashboard Cite

Breast cancer is a highly malignant disease worldwide. Among the numerous treatment options for breast cancer, radiotherapy is one of the commonly used treatments, while there is currently no sufficient molecular biomarkers to predict prognosis and guide its application. The tumor microenvironment (TME) is an important factor affecting tumor biological function, and changes in its composition are equally relevant to tumor progression and prognosis during radiotherapy. Here, we performed bioinformatic analyses using data obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases to screen for molecular biomarkers related to TME that may influence radiotherapy sensitivity. By combining immune scores and stromal scores calculated as well as performing weighted co-expression network analysis (WGCNA), we identified key modules and hub genes to construct competing endogenous RNA (ceRNA) networks. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that genes of the PI3K-AKT pathway in the blue module were significantly enriched. Among hub genes in the blue module, we further found that the expression levels of COL1A1, COL1A2, COL6A3, THBS2 and PDGFRB were negatively associated with radiotherapy sensitivity. These findings may provide new insights into the mechanisms of radiotherapy sensitivity in breast cancer patients, offering hope for the discovery of new therapeutic targets.

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Radiation increases COL1A1, COL3A1, and COL1A2 expression in breast cancer

Cited by 19 publications

References 25 publications

Distinct Gene Expression Profiles of Matched Primary and Metastatic Triple-Negative Breast Cancers

Distinct Gene Expression Profiles of Matched Primary and Metastatic Triple-Negative Breast Cancers

Exploring the key genes associated with breast cancer radiotherapy sensitivity based on the stromal-immune score and analysis of the WGCNA and ceRNA network

Identification of key genes associated with breast cancer radiotherapy sensitivity based on the stromal-immune score and analysis of the WGCNA and ceRNA network

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