Integrative Transcriptomic Analysis Identifies ADIPOQ, LIPE, LEP, SLC2A4, and LPL as Prognostic Metabolic Markers in Breast Cancer

Background: Breast cancer (BRCA) is a highly heterogeneous disease, posing significant challenges in prognosis. Despite therapeutic advancements, recurrence and metastasis remain major obstacles, underscoring the need for novel prognostic markers. This study aims to identify key metabolic regulators that may serve as potential biomarkers to improve risk stratification and treatment strategies. Methods: RNA expression data from 208 tumor and 25 normal breast tissue samples were sourced from the GDC database and analyzed using R-based pipelines for differential expression analysis, Weighted Gene Co-expression Network Analysis (WGCNA), pathway enrichment, and survival analysis. Results: Differential expression analysis identified 1,663 dysregulated genes and WGCNA identified 59 gene modules, with the “tan” module (302 genes) significantly associated with tumor status. Cross-referencing WGCNA and differential expression results identified 254 commonly downregulated genes enriched in lipid metabolism pathways. Kaplan-Meier survival analysis highlighted five key genes ( ADIPOQ, LIPE, LEP, SLC2A4 , and LPL ) significantly associated with poor prognosis. The downregulation of these five genes suggests a metabolic shift in breast cancer, linking lipid and glucose metabolism dysregulation to tumor progression and poorer survival outcomes. Conclusion: This study identified a distinct metabolic signature in BRCA, characterized by altered lipid and glucose metabolism leading to disease progression and possibly implying poor prognosis. The identified genes may serve as novel prognostic biomarkers with potential therapeutic implications, warranting further clinical validation to enhance risk stratification and treatment strategies.