Constructing a sixteen lactate-related gene risk signature for LUAD to predict the prognosis and TME by machine learning.

Although it is the most common subtype of lung cancer in clinical practice, lung adenocarcinoma (LUAD) was proven to be associated with a poor prognosis. In recent years, lactate metabolism has been considered an important biological mechanism in lung cancer. However, the mechanisms of lactate-related genes in tumour microenvironment (TME) of LUAD are unknown. Lactate-related genes modification patterns in 701 LUAD samples from TCGA and GEO database were systematically assessed on the basis of 16 lactate-related genes (LRGs).We also identified the correlation of these clusters to the TME and immune cell infiltration in the TME. After unsupervised clustering analysis was performed, the LUAD samples were divided into three lactate-related gene phenotypes on the basis of 36 prognostic lactate-related genes (PLRGs). These molecular subtypes have different immune cell infiltration characteristics and pathway enrichment. Using LASSO, a 16-LRG risk signature was constructed. The lactate-related signature demonstrated a stable and accurate ability to predict the prognosis of LUAD in patients. Furthermore, the lactate-related signature demonstrated good predictive ability for immune infiltrating cells, tumour mutation burden, and response to immunotherapy. The 16-LRG risk signature was subsequently verified in the GSE50081 GEO cohort. This study revealed the significant clinical utility of the 16-LRG risk signature in the understanding the TME and prognosis of LUAD. The 16-LRG risk signature is conducive to understanding immune cell infiltration in the LUAD TME and contributes to the selection of more effective immunotherapy strategies.