Unveiling a Potential Prognostic Biomarker in KRAS-Mutant Lung Adenocarcinoma Using Weighted Gene Co-Expression Network Analysis

Background Globally, lung cancer is the leading cause of cancer-related deaths, primarily non-small cell lung cancer (NSCLC). Kirsten Rat Sarcoma Oncogene Homolog (KRAS) mutations are common in NSCLC and linked to a poor prognosis. Covalent inhibitors targeting KRAS-G12C mutation have improved treatment for some patients, but most KRAS-mutant lung adenocarcinoma (KRAS-MT LUAD) cases lack targeted therapies. More research is required to identify prognostic genes in KRAS-MT LUAD. Objective We aimed to identify hub genes within key co-expression gene network modules specifically associated with KRAS-MT LUAD. These hub genes hold the potential to serve as therapeutic targets or biomarkers, providing insights into the pathogenesis and prognosis of lung cancer. Methods We performed a comprehensive analysis on KRAS-MT LUAD using diverse data sources. This included TCGA project data for RNA-seq, clinical information, and somatic mutations, along with RNA-seq data for adjacent normal tissues. DESeq2 identified differentially expressed genes (DEGs), while weighted gene co-expression network analysis (WGCNA) revealed co-expression modules. Overlapping genes between DEGs and co-expression module with the highest significance were analyzed using gene set enrichment analysis (GSEA) and protein-protein interaction (PPI) network analysis. Hub genes were identified with the Maximal Clique Centrality (MCC) algorithm in Cytoscape. Prognostic significance was assessed through survival analysis and validated using the GSE72094 dataset from Gene Expression Omnibus (GEO) database. Results In KRAS-MT LUAD, 3,122 DEGs were found (2,131 up-regulated, 985 down-regulated). The blue module, among 25 co-expression modules from WGCNA, had the strongest correlation. 804 genes overlapped between DEGs and the blue module. Among twenty hub genes in the blue module, leucine-rich repeats containing G protein-coupled receptor 4 (LGR4) overexpression correlated with worse overall survival (OS) in KRAS-MT LUAD patients (P=0.012). The prognostic significance of LGR4 was confirmed using GSE72094, but surprisingly, the direction of the association was opposite to what was expected. Conclusion LGR4 is a potential prognostic biomarker in KRAS-MT LUAD. Contrasting associations in TCGA and GSE72094 datasets reveal the complexity of KRAS-MT LUAD. Further investigations are required to understand LGR4’s role in lung adenocarcinoma prognosis, especially in the context of KRAS mutations.