Abstract
PURPOSE: Epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) are the first-line therapy for patients with lung adenocarcinoma (LUAD) harboring activating EGFR mutations. However, the emergence of drug resistance to EGFR-TKIs remains a critical obstacle for successful treatment and is associated with poor patient outcomes. The overarching objective of this study is to apply bioinformatics tools to gain insights into the mechanisms underlying resistance to EGFR-TKIs and develop a robust predictive model. METHODS: The genes associated with gefitinib resistance in the LUAD cell Gene Expression Omnibus (GEO) database were identified using gene chip expression data. Functional enrichment analysis, gene set enrichment analysis (GSEA), and immune infiltration analysis were performed to comprehensively explore the mechanism of gefitinib resistance. Furthermore, a GRRG_score was constructed by integrating genes related to LUAD prognosis from The Cancer Genome Atlas (TCGA) database with the screened Gefitinib Resistant Related differentially expressed genes (GRRDEGs) using the Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression analyses. Furthermore, we conducted an in-depth analysis of the tumor microenvironment (TME) features and their association with immune infiltration between different GRRG_score groups. A prognostic model for LUAD was developed based on the GRRG_score and validated. The HPA database was used to validate protein expression. The CTR-DB database was utilized to validate the results of drug therapy prediction based on the relevant genes. RESULTS: A total of 110 differentially expression genes were identified. Pathway enrichment analysis of DEGs showed that the differentially expressed genes were mainly enriched in Mucin type O-glycan biosynthesis, Cytokine-cytokine receptor interaction, Sphingolipid metabolism. Gene set enrichment analysis showed that biological processes strongly correlated with gefitinib resistance were cell proliferation and immune-related pathways, EPITHELIAL_MESENCHYMAL_TRANSITION, APICAL_SURFACE, and APICAL_JUNCTION were highly expressed in the drug-resistant group; KRAS_SIGNALING_DN, HYPOXIA, and HEDGEHOG_SIGNALING were highly expressed in the drug-resistant group. The GRRG_score was constructed based on the expression levels of 13 genes, including HSPA2, ATP8B3, SPOCK1, EIF6, NUP62CL, BCAR3, PCSK9, NT5E, FLNC, KRT8, FSCN1, ANGPTL4, and ID1. We further screened and validated two key genes, namely, NUP62CL and KRT8, which exhibited predictive value for both prognosis and drug resistance. CONCLUSIONS: Our study identified several novel GRRDEGs and provided insight into the underlying mechanisms of gefitinib resistance in LUAD. Our results have implications for developing more effective treatment strategies and prognostic models for LUAD patients.