Abstract
PURPOSE: Non-small cell lung carcinoma (NSCLC) is a leading cause of cancer-related mortality worldwide, highlighting the urgent need for early detection and targeted therapies. While lipopolysaccharide (LPS) metabolism and circadian rhythm disruption are emerging as important factors in cancer progression, their specific roles in NSCLC remain poorly understood. METHODS: We integrated multiple GEO datasets to identify NSCLC-associated differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) identified key gene modules, followed by functional enrichment analysis. A hybrid machine learning approach combining Lasso regression and random forest was used to identify hub genes. Immune infiltration analysis evaluated associations with the tumor microenvironment, and diagnostic performance was validated in an independent cohort. Functional roles of the candidate gene CACNA2D2 were assessed through gain- and loss-of-function experiments in A549 cells, evaluating viability, proliferation, migration, and invasion. RESULTS: We identified 889 significant DEGs enriched in inflammatory and immune-related pathways. WGCNA revealed the magenta module as highly associated with NSCLC, involved in angiogenesis and extracellular matrix organization. Machine learning identified nine hub genes (CACNA2D2, ASPA, LRRN3, ABCA6, TNFSF12, AHNAK, TACC1, ID4, TSLP) showing excellent diagnostic performance (AUC: 0.832-0.906). These genes correlated significantly with immune cell infiltration patterns. Functional validation established CACNA2D2 as a tumor suppressor, where its depletion enhanced malignant phenotypes while overexpression suppressed them. CONCLUSION: This study identifies a novel gene signature linked to LPS metabolism and circadian disruption in NSCLC, with validated diagnostic utility and implications for tumor immune regulation. CACNA2D2 emerges as a key tumor suppressor, offering insights for early detection and targeted therapy development.