Biomarkers and potential function analysis of triple-negative breast cancer screening based on bioinformatics.

Abstract

This study aims to identify and validate potential endogenous biomarkers for triple-negative breast cancer (TNBC). 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. Our findings provide new insights into TNBC pathogenesis and potential therapeutic strategies, with important clinical implications for further understanding TNBC mechanisms and developing innovative treatments.