Construction of prognostic risk models and identification and verify cation of biomarkers related to lactic acid metabolism and carbohydrate metabolism in lung adenocarcinoma

Lung adenocarcinoma (LUAD) has a high incidence and poor prognosis. Carbohydrate and lactic acid metabolism drive tumor growth, highlighting the need to identify prognostic genes in these pathways for LUAD diagnosis and treatment. We conducted differentially expressed analysis in the Cancer Genome Atlas (TCGA)-LUAD, applied Spearman analysis to correlate carbohydrate and lactic acid metabolism genes and identified key model genes using weighted gene co-expression network analysis. Overlapping differential expression genes (DEGs), correlated genes, and key model genes yielded candidates. Hub genes were screened by molecular complex detection, prognostic genes by univariate Cox, and least absolute shrinkage and selection operator, constructing a risk model. TCGA-LUAD samples were stratified by median risk score. Finally, the prognostic genes’ impact on LUAD’s immune microenvironment was analyzed, and their expression in clinical samples was validated through real-time quantitative polymerase chain reaction. From 7,252 DEGs, 189 correlated genes, and 3,900 key model genes, 20 candidates emerged. Nine hub genes led to five prognostic genes: ADH1A, Alcohol dehydrogenase 1B (ADH1B), ALDOA, ENO1, and ACSS3. In addition, nine immune cells differed significantly between risk groups, with CD4 T, monocytes, and mast cells positively correlating with ADH1B. ADH1A, ADH1B, and ACSS3 were downregulated, contrasting ALDOA and ENO1. Consistent results were obtained by protein mapping. Eventually, ADH1A, ADH1B, ALDOA, and ENO1 expression trends in clinical samples were similarly consistent with the dataset. In summary, five prognostic genes related to carbohydrate and lactic acid metabolism (ADH1A, ADH1B, ALDOA, ENO1, and ACSS3) associated with LUAD have been identified. This model serves as an effective predictive tool and offers a new perspective for studying tumor metabolism.