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pmid:40179432
Biomarkers and potential function analysis of triple-negative breast cancer screening based on bioinformatics.
Xing Chen · Xiaodan Tan · Zhe Peng · Xiaoli Wang · Wenjia Guo · Dan Li · Yang Yang · Duanfang Zhou · Lin Chen
Cancer biomarkers : section A of Disease markers · 2025 doi:10.1177/18758592241308738
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Experiment
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Data
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Analysis
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Execution
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Complete file
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TNBC microarray data (GSE38959, GSE53752) were retrieved from the Gene Expression Omnibus (GEO) database, and principal component analysis (PCA) was performed to evaluate the reliability of the data. The microarray datasets were merged, and differentially expressed genes (DEGs) were identified using R software. Functional enrichment analysis of the DEGs was conducted using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The most disease-relevant module was identified through Weighted Gene Co-expression Network Analysis (WGCNA), and genes within this module were intersected with the DEGs. The intersecting genes underwent Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis to minimize errors and identify TNBC-specific genes. Sensitivity and survival analyses were performed on the identified specific genes. There were 10 TNBC-specific genes identified: RRM2, DEPDC1, FIGF, TACC3, E2F1, CDO1, DST, MCM4, CHEK1, and PLSCR4. RT-qPCR analysis showed significant upregulation of CDO1, MCM4, DEPDC1, RRM2, and E2F1 in MDA-MB-231, CAL-148, and MFM-223 compared to MCF-10A. 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