Abstract
Radiotherapy is a standard treatment for advanced lung cancer, but resistance remains a significant cause of treatment failure. This study aimed to investigate lactate-associated genes to identify patients likely to benefit from radiotherapy. RNA-seq data from 99 patients with lung cancer who underwent radiotherapy were analyzed to identify differentially expressed genes (DEGs) between resistant and sensitive cases. Bioinformatics tools were used to assess the prognostic relevance of lactate-related genes, and a risk score model was develpoed based on these genes. Dysregulation of these genes in patients with lung cancer undergoing radiotherapy was validated through in vitro experiments. Molecular docking was used to explore potential radiosensitizers. The analysis identified 1482 DEGs, with enrichment analysis highlighting lactate metabolism pathways. A risk score model was constructed using the lactate-related genes ADAMTS3, FADS2, and RTBDN to classify patients into high- and low-risk subgroups. Functional enrichment analysis revealed the model's impact on DNA repair and tumor immunity. A nomogram was developed for clinical implementation. Wet lab experiments further confirmed these findings. In conclusion, a novel risk score model based on lactate-related genes was developed to predict radiotherapy outcomes in lung cancer. FADS2 was identified as a potential biomarker for predicting resistance to radiotherapy. This study is the first to examine the predictive value of lactate-related genes for radiotherapy efficacy in lung cancer, offering valuable insights for personalized treatment strategies to improve therapeutic outcomes.