Integrative Transcriptomic and Docking Analysis of Coffee Bioactives Targeting CCNA2, AKT1, and CDK2 in TNBC
TL;DR
It is suggested that caffeic acid is a promising multitarget candidate for TNBC therapy and warrants further experimental validation.
Abstract
Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype characterized by high proliferation rates, poor prognosis, and limited therapeutic options. Coffee-derived bioactive compounds have demonstrated potential anticancer properties, but their interactions with key TNBC-associated targets remain insufficiently understood. Therefore, this study aimed to identify molecular biomarkers and therapeutic targets in TNBC and evaluate the potential of major coffee bioactive compounds using an integrative in silico approach. Five Gene Expression Omnibus (GEO) datasets (GSE38959, GSE186102, GSE65194, GSE45827, and GSE7904) were analyzed to identify differentially expressed genes (DEGs) using |log2FC| ≥ 1 and adjusted p-value < 0.05. Protein–protein interaction analysis, machine-learning validation, Kaplan–Meier survival analysis, chemogenomic mapping, molecular docking, and ADMET prediction were subsequently performed. A total of 917 overlapping DEGs were identified, with CCNA2 emerging as a key hub gene associated with poor prognosis. Machine-learning validation achieved 97.7% accuracy and an AUC of 0.964. Chemogenomic analysis prioritized AKT1, CDK2, and CCNA2 as therapeutic targets. Molecular docking revealed favorable interactions of caffeic acid and chlorogenic acid, with caffeic acid showing the most consistent multitarget affinity (−5.94 to −6.96 kcal/mol). These findings suggest that caffeic acid is a promising multitarget candidate for TNBC therapy and warrants further experimental validation.