ArticleJournal, genetic engineering & biotechnology2025
Exploration of crucial stromal risk genes associated with prognostic significance and chemotherapeutic opportunities in invasive ductal breast carcinoma.
Article in Journal, genetic engineering & biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- BRCAGenie: A machine learning-driven 43-gene polygenic risk score model for precision prediction of breast cancer survival.Journal of translational medicine · 2025Article
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5 authors.
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Abstract
backgroundFew studies revealed that stromal genes regulate the tumor microenvironment (TME). However, identification of key-risk genes in the invasive ductal breast carcinoma-associated stroma (IDBCS) and their associations with the prediction of risk group remains lacking.
methodsThis study used the GSE9014, GSE10797, GSE8977, GSE33692, and TGGA BRCA datasets. We explored the differentially expressed transcriptional markers, hub genes, gene modules, and enriched KEGG pathways. We employed a variety of algorithms, such as the log-rank test, the LASSO-cox model, the univariate regression model, and the multivariate regression model, to predict prognostic-risk genes and the prognostic-risk model. Finally, we employed a molecular docking-based study to explore the interaction of sensitive drugs with prognostic-risk genes.
resultsIn comparing IDBCS and normal stroma, we discovered 1472 upregulated genes and 1400 downregulated genes (combined ES > 0585 and adjusted p-value < 0.05). The hub genes enrich cancer, immunity, and cellular signaling pathways. We explored the 12 key risk genes (ADAM8, CD86, CSRP1, DCTN2, EPHA1, GALNT10, IGFBP6, MIA, MMP11, RBM22, SLC39A4, and SYT2) in the IDBCS to identify the high-risk group and low-risk group patients. The high-risk group had a lower survival rate, and the constructed ROC curves evaluated the validity of the risk model. Expression validation and diagnostic efficacy revealed that the key stromal risk genes are consistently deregulated in the high-risk group and high stromal samples of the TCGA BRCA cohort. The expression of crucial risk genes, including CD86, CSRP1, EPHA1, GALNT10, IGFBP6, MIA, and RBM22 are associated with drug resistance and drug sensitivity. Finally, a molecular docking study explored several sensitive drugs (such as QL-XII-61, THZ-2-49, AZ628, NG-25, lapatinib, dasatinib, SB590885, and dabrafenib) interacted with these essential risk genes through hydrogen bonds and other chemical interactions.
conclusionsExploring essential prognostic-risk genes and their association with the prognosis, diagnostic efficacy, and risk-group prediction may provide substantial clues for targeting the breast cancer stromal key-risk genes.
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