Mapping metabolic reprogramming in lung and breast cancer through integrative bioinformatics

by Nosayba Al-Damook, Molham Sakkal, Mostafa Khair, Walaa K. Mousa, Rose Ghemrawi Metabolic reprogramming is central to cancer biology, enabling tumor cells to sustain rapid proliferation, resist stress, and adapt to therapy.

However, these alterations are highly heterogeneous across cancer types, and current treatments rarely exploit subtype-specific metabolic vulnerabilities. To address this gap, we developed a unified bioinformatics framework that integrates transcriptomic profiling (UALCAN), drug–gene interactions (DGIdb), gene–disease associations (Open Targets), pathway enrichment (Enrichr), and protein–protein interaction networks (STRING/Cytoscape).

This pipeline was applied to lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LSCC), breast cancer (BRCA), and metastatic breast tumors (MET500) to uncover cancer type–specific metabolic programs and prioritize translational targets. Our analysis revealed distinct signatures: LUAD showed glycolytic activation, LSCC coupled glycolysis with oxidative phosphorylation, BRCA favored anabolic and lipogenic pathways, and MET500 tumors adopted stress-adaptive states with elevated antioxidant and autophagy programs.

Integration of pharmacological evidence highlighted clinically actionable interactions between metabolic genes and FDA-approved drugs, including ASNS–asparaginase, DHODH–teriflunomide, and G6PD–rasburicase. Gene–disease associations further prioritized G6PD, SLC2A1, and TK1 as robust targets strongly linked to lung and breast cancers.