Computational genomics study reveals a succinylation-based prognostic signature in lung adenocarcinoma, highlighting novel biomarkers for immune stratification and personalized therapy.
Key Points
To systematically evaluate the prognostic value and immunological significance of succinylation-related genes in lung adenocarcinoma.
Analyzed transcriptomic and clinical records from The Cancer Genome Atlas using weighted gene co-expression network analysis and differential gene expression analysis.
Developed machine learning prognostic models evaluated with Shapley Additive Explanations interpretability, profiling immune infiltration, checkpoint levels, and predicted drug responses.
Validated the protein expression of the top-ranked signature gene, lactate dehydrogenase A (LDHA), using immunohistochemistry on lung adenocarcinoma tissue samples.
Identified 156 succinylation-related differentially expressed genes, with 34 genes showing significant associations with overall survival.
Constructed a prognostic signature that stratified patients into high- and low-risk cohorts exhibiting distinct survival durations, immune microenvironment patterns, and drug sensitivities.
Identified LDHA as the primary predictive contributor in the model and confirmed its elevated expression in lung adenocarcinoma tissues via immunohistochemistry.