Immune Profiling and Drug Sensitivity in LUAD: Insights from m5C-Related Ferroptosis Gene Analysis

Background Recent research highlights the pivotal role of 5-methylcytosine (m5C) modification and ferroptosis in the progression of various cancers. However, the prognostic value of m5C-related ferroptosis genes in lung adenocarcinoma (LUAD) remains unclear. This study aims to establish a prognostic framework centered on m5C-related ferroptosis genes to improve the accuracy of prognosis prediction in LUAD patients, thereby optimizing targeted therapeutic strategies. Methods The mRNA expression profiles, along with clinicopathological information of LUAD patients were obtained from The Cancer Genome Atlas (TCGA). Differential expression and weighted gene co-expression network analysis (WGCNA) identified prognosis-related modules. Ferroptosis-related genes and m5C regulators were integrated to identify m5C-associated ferroptosis genes. Machine learning and Cox regression were applied to construct a prognostic model. Finally, functional enrichment, immune infiltration, and drug sensitivity analyses were performed. Results Two key gene modules significantly correlated with LUAD prognosis were identified, yielding 29 m5C-related ferroptosis genes. Ten hub genes were selected via machine learning, and the prognostic risk model achieved strong predictive performance. Immune infiltration profiling revealed close associations between the model and tumor microenvironment features. Among the hub genes, SLC2A1, RRM2, and KIF20A were markedly overexpressed in tumor tissues and associated with poor survival. Notably, SLC2A1 expression correlated with enhanced immunotherapy response. Drug sensitivity analysis and molecular docking identified nine small-molecule compounds with favorable binding affinities to SLC2A1. Conclusions This study delineates the critical prognostic significance of m5C-related ferroptosis genes in LUAD and establishes a clinically relevant prognostic model. The identification of candidate SLC2A1 inhibitors offers promising avenues for targeted therapy and personalized treatment strategies in LUAD.