Application value of a prognostic risk model based on lactate-related genes in non-small cell lung cancer
Jinhua Bao1, Jun Gu2,3, Tanao Ji4
1Department of Respiratory and Critical Care Medicine, Rudong County Hospital of Traditional Chinese Medicine, Nantong, China.
Background:
Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality worldwide. Despite therapeutic advances, the outcomes of patients with advanced disease are still poor. This study was conducted to identify pathogenic and prognostic lactate-related genes (LRGs) in NSCLC to improve clinical treatment and prognosis.
Methods:
Consensus clustering was applied to The Cancer Genome Atlas (TCGA) training cohort to delineate subtypes associated with lactate metabolism. On this basis, we established a prognostic signature of LRGs with least absolute shrinkage and selection operator (LASSO) Cox regression and assessed its predictive value in independent Gene Expression Omnibus (GEO) datasets. To clarify the mechanisms related to the model's ability, we examined immune infiltration, estimated responses to immunotherapy, and performed single-cell analysis. In addition, the transcriptional levels of the prognostic genes were validated via real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) in lung cancer cell lines and compared with alveolar epithelial cells (AECs), thereby strengthening the reliability of the model.
Results:
Two distinct lactate-associated clusters were identified in NSCLC. On this basis, we established an eight-gene prognostic model that stratified patients into high- and low-risk groups with markedly different survival outcomes. The model achieved strong predictive accuracy, with areas under the curve of 0.693, 0.707, and 0.704 for 1-, 3-, and 5-year survival in the training cohort, and its robustness was further confirmed in external validation datasets. In addition, the lactate risk score showed significant associations with the tumor microenvironment (TME) and revealed clear differences in responsiveness to immunotherapy. Single-cell analysis provided further insight into cell type-specific patterns of lactate metabolism. Finally, RT-qPCR validation of most prognostic genes confirmed the reliability of the model.
Conclusions:
We developed an LRG prognostic model for NSCLC, which offers novel insights for both diagnostic assessment and treatment strategies.
