Integrated Bioinformatics and RT-qPCR Validation Identify Candidate Biomarkers Associated with EGFR-TKI Resistance in EGFR-Mutant Lung Adenocarcinoma

Abstract

Background: Acquired resistance to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) remains a major challenge in the treatment of EGFR-mutant lung adenocarcinoma. Identifying biomarkers associated with resistance may improve understanding of the underlying molecular mechanisms. Objective: This study aimed to identify candidate biomarkers associated with EGFR-TKI resistance using integrated bioinformatics analysis and RT-qPCR validation. Methods: Two Gene Expression Omnibus (GEO) datasets (GSE123066 and GSE178755) were analysed to identify differentially expressed genes (DEGs). Overlapping DEGs were subjected to protein–protein interaction (PPI) network construction, Gene Ontology (GO), KEGG pathway enrichment, and Kaplan–Meier survival analyses. Selected candidate genes were subsequently validated by RT-qPCR in an independent clinical cohort. Results: A total of 76 overlapping DEGs were identified, including 52 upregulated genes enriched in cell proliferation, hypoxia response, EGFR signalling, and metabolic processes. KEGG analysis identified the PI3K–Akt signalling pathway as significantly enriched. Kaplan–Meier analysis showed that higher expression of TGFA, DDIT4, and SOX9 was associated with shorter progression-free survival. RT-qPCR validation demonstrated significantly increased SOX9 expression in the resistant group, whereas TGFA and DDIT4 showed no statistically significant differences. Conclusions: Integrated bioinformatics analysis identified TGFA, DDIT4, and SOX9 as candidate genes associated with EGFR-TKI resistance in EGFR-mutant lung adenocarcinoma. These findings provide a basis for further investigation of resistance-associated biomarkers, although larger independent cohorts and functional studies are required before clinical application.