ArticleAnalytical cellular pathology (Amsterdam)2022
Weighted Gene Coexpression Network Analysis Identifies TBC1D10C as a New Prognostic Biomarker for Breast Cancer.
Article in Analytical cellular pathology (Amsterdam), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Identification and validation of an intratumor heterogeneity-related prognostic signature in triple-negative breast cancer: a study based on integrative machine learning and single-cell sequencing.Breast cancer research : BCR · 2026Article
- TIGIT, as a potential immune checkpoint target for immunotherapy of breast cancer.Medical oncology (Northwood, London, England) · 2025Review
- The role of LRRC15-SCG5 in ECM protein binding as a prognostic signature for urothelial carcinoma.American journal of cancer research · 2025Article
- Role of vascular endothelial growth factor D in lung adenocarcinoma immunotherapy response.American journal of translational research · 2024Article
- EBV-Associated Hub Genes as Potential Biomarkers for Predicting the Prognosis of Nasopharyngeal Carcinoma.Viruses · 2023Article
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Authors and funding
7 authors.
Funding
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Abstract
Background: Immune checkpoint inhibitors are a promising therapeutic strategy for breast cancer (BRCA) patients. The tumor microenvironment (TME) can downregulate the immune response to cancer therapy. Our study is aimed at finding a TME-related biomarker to identify patients who might respond to immunotherapy. Method: We downloaded raw data from several databases including TCGA and MDACC to identify TME hub genes associated with overall survival (OS) and the progression-free interval (PFI) by WGCNA. Correlations between hub genes and either tumor-infiltrating immune cells or immune checkpoints were conducted by ssGSEA. Result: TME-related green and black modules were selected by WGCNA to further screen hub genes. Random forest and univariate and multivariate Cox regressions were applied to screen hub genes (MYO1G, TBC1D10C, SELPLG, and LRRC15) and construct a nomogram to predict the survival of BRCA patients. The Conclusion: We constructed a TME-related gene signature model to predict the survival probability of BRCA patients. We also identified a hub gene, TBC1D10C, which was correlated with both OS and the PFI and had a high positive association with tumor-infiltrating immune cells and common immune checkpoints. TBC1D10C may be a new biomarker to select patients who may benefit from immunotherapy.
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