Bioinformatics-based identification of glycolysis-related signatures associated with drug resistance and prognosis in lung adenocarcinoma

Abstract

Background Drug resistance and poor clinical outcomes in lung adenocarcinoma (LUAD) necessitate robust biomarkers for personalized therapy. Glycolysis reprogramming is a hallmark of cancer, but its clinical utility remains incompletely defined. Methods We integrated TCGA and GEO transcriptomic data with Weighted gene co−expression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression to construct a glycolysis−related prognostic signature. A nomogram combining the risk score with clinicopathological factors was developed. Drug sensitivity was predicted using the pRRophetic algorithm. qRT−PCR and xenograft models using A549 and cisplatin−resistant A549/DDP cells validated the expression of candidate genes. Results Patients stratified by glycolysis-related risk scores exhibited significantly distinct survival outcomes, and the glycolysis-based signature functioned as an independent prognostic factor for overall survival in LUAD. The nomogram demonstrated robust predictive performance and effectively estimated patient sensitivity to three commonly used conventional chemotherapeutic agents. In vitro and in vivo studies using A549 cells and their cisplatin-resistant derivative A549/DDP revealed aberrant expression of VIPR1, ADRB2, RXFP1, PDGFB, WNT3A, and SPRY1 in the resistant cell line and in corresponding xenograft tumor tissues. These findings suggest that glycolytic activity is closely associated with both drug resistance and clinical prognosis in LUAD. Conclusions This study identifies a glycolysis-related gene signature with demonstrable utility for prognostic stratification and therapeutic response prediction in LUAD. The proposed integrative model holds promise for enhancing precision treatment decision-making through optimized risk assessment and rational selection of chemotherapeutic regimens.