Comprehensive analysis of a machine learning prognostic model for the interaction between mitochondrial function and lactylation in lung adenocarcinoma.

backgroundMitochondrial function and lactylation play important roles in the development of lung adenocarcinoma (LUAD). However, the impact of their interaction on the prognosis of LUAD still needs further investigation.

methodsA prognostic model was developed via machine learning algorithms. LUAD patients were grouped based on the median risk score, and the differences between groups in tumor biological characteristics, tumor immunity, and drug sensitivity were analyzed. Consensus clustering analysis was performed, and a nomogram survival prediction model was constructed.

resultsWe constructed an optimal prognostic model with 11 valuable signature genes. LUAD patients were classified into high- and low-risk groups based on the median risk score. The high-risk group was enriched in tumor proliferation-related pathways and glycolysis, while the low-risk group was associated with inflammation and immune response-related pathways. The high-risk group had higher tumor stemness, mutation burden, and poorer survival prognosis than the low-risk group. Immune microenvironment and drug sensitivity differed between the two groups. Furthermore, consensus clustering can divide LUAD patients into C1 and C2 subtypes, corresponding to low- and high-risk groups, respectively.

conclusionThis study provides a reliable prognostic model for the risk assessment of LUAD. It reveals the distinct biological characteristics and prognostic differences in LUAD patients with different clinical outcomes, providing a potential theoretical basis for personalized treatment.