Razi University

AbstractBackground: Lipid metabolic reprogramming is a majorcharacteristic of breast cancer; however, its patterns across molecularsubtypes and the underlying regulatory networks remain incompletely understood.This study aimed to identify key genes and regulatory networks associated withlipid metabolism and evaluate gene expression patterns related to survivalacross different breast cancer subtypes.Methods: First, a comprehensive gene list associatedwith lipid metabolism was compiled using specialized databases and relevantresources. Gene expression data from breast cancer cell lines representingLuminal A, Luminal B, HER2-positive, and triple-negative breast cancer (TNBC),along with a normal cell line, were obtained and analyzed through pairwisecomparisons. Differentially expressed genes and genes shared across comparisonswere identified and filtered based on the lipid metabolism-related gene list.Selected genes were subjected to functional and network analyses.Protein–protein interaction (PPI) and regulatory networks involvingtranscription factors (TFs), miRNAs, and lncRNAs were constructed and analyzedto identify central nodes and recurrent genes. Based on the network analyses, aset of candidate genes was selected for survival evaluation. The prognosticperformance of different gene combinations was assessed using gene expressionand clinical data from breast cancer patients based on overall survival (OS)and distant metastasis-free survival (DMFS).Results: Integrated expression and network analysesidentified a nine-gene core comprising PPARA, SREBF1, PLPP2, LPIN1, PLPP3,GPAM, ESR1, PPARGC1A, and PLD2 as a recurrent lipid metabolism-related geneset. PPI, TF, miRNA, and lncRNA network analyses revealed interactions betweenthese genes and central nodes, as well as potential regulatory factors,highlighting a multilayer regulatory network involvinggene–protein–TF–miRNA–lncRNA interactions. Functional enrichment analysisdemonstrated significant enrichment of shared genes in major lipid metabolicpathways, including glycerophospholipid, glycerolipid, phospholipid, andsphingolipid metabolism. Among 255 possible gene combinations evaluated in thesurvival analysis, a four-gene signature comprising SREBF1, PLPP2, PLPP3, andPPARGC1A showed the best performance for both OS and DMFS.Conclusion: This study identified a core lipidmetabolism-related gene set and a multilayer regulatory network across