Computational identification of hub genes and bioactive compounds in breast cancer via meta-analysis of transcriptomic data, network pharmacology, molecular docking, and ADMET evaluation

Breast cancer remains the most common cancer among women globally, with limited effective therapies for aggressive subtypes like triple-negative breast cancer. Transcriptomic resources and rich phytochemical compounds offer opportunities to identify novel therapeutic targets and drug candidates. We conducted a transcriptomic meta-analysis of two GEO datasets (GSE54002, GSE42568) to identify overexpressed genes in breast cancer and computationally prioritize phytochemicals targeting the genes. Differential expression analysis and meta-analysis identified 77 consensus genes. Enrichment and protein–protein interaction network analysis revealed key lipid metabolic regulators. The hub genes were evaluated for prognostic value using TCGA-BRCA data. Molecular docking was performed on the potential proteins using 2,846 phytochemicals from the KEGG database. Ligands were screened for ADMET and drug-likeness using ADMETlab 3.0. Enrichment analysis revealed involvement of lipid metabolism pathways, including triglyceride catabolism and PPAR signaling. PCK1, PNPLA2, and LIPE emerged as central hub genes with PCK1 as an independent prognostic factor (HR: 1.09, p=0.037). Docking identified several phytochemicals, including Quadrigemine A and Ergocristine, with superior binding energies to the cocrystallized inhibitor. However, only eight ligands passed ADMET filtering, and not all fulfilled Lipinski’s Rule of Five. Substances such as 4,5-dicaffeoylquinic acid and baicalin showed prior anticancer activity, while others like Carnosifloside I remain unexplored. This study integrated transcriptomic meta-analysis, network analysis, and in silico screening to identify PCK1 as a metabolic target in breast cancer. Several phytochemicals demonstrate promise as computationally predicted PCK1 inhibitors, warranting further experimental validation. Lipid metabolism may be a viable strategy in breast cancer therapy.