Stage-specific biomarkers in triple-negative breast cancer: Preliminary findings from bioinformatics approaches

BackgroundIntegrated bioinformatics approaches were used to identify stage-specific candidate genes and potential drug targets in triple-negative breast cancer (TNBC).MethodsMicroarray (164 early-stage, 33 advanced-stage, and 53 normal samples) and RNA-seq (113 normal, 163 early-stage, and 30 advanced-stage TNBC samples) datasets were analyzed. Differentially expressed genes (DEGs) were identified, followed by co-expression analysis using Weighted Gene Co-expression Network Analysis (WGCNA) and protein-protein interaction analysis using the STRING database. miRNA co-regulation was evaluated using multiMiR and TCGA correlation analyses. Candidate genes were validated using UALCAN and immunohistochemistry data. Molecular docking assessed potential therapeutic agents.ResultsNovel stage-specific candidate biomarkers were identified, including DNAJC6, SKP2, MOCOS, and NCAPD2 in early-stage TNBC, and F11R, FOXO6, PPP4C, and TMEM51 in advanced-stage TNBC. UALCAN analysis confirmed the dysregulation of these genes across 23 additional malignancies. STRING-based network analysis revealed stage-specific protein-protein interactions, including SKP2-SKP1 in early-stage and F11R-TJP1 in advanced-stage TNBC. miRNA co-regulation distinguished early-stage TNBC through PI3K-AKT-related pathways and advanced-stage TNBC through tumor progression-associated pathways. Docking-based drug repurposing highlighted conventional agents (e.g., doxorubicin) and potential novel candidates (e.g., sunitinib).ConclusionThis study identifies novel stage-specific gene candidates and suggests repurposable drugs for TNBC, supporting progression-specific targeted therapeutic strategies.