Unraveling lung cancer dynamics: a new metabolic signature improving the prediction of recurrence in resected lung adenocarcinoma

Background: Lung cancer is characterized by wide genetic, molecular, and phenotypic alterations that may challenge diagnosis and clinical decision-making. This heterogeneity often leads to variable responses to therapies, resulting in suboptimal outcomes for many patients. Recent advancements in omics technologies have enabled a deeper exploration of mechanisms driving tumor behavior and identification of specific molecular signatures. Tumor metabolic reprogramming, one of the hallmarks of cancer development, progression, and recurrence, represents a promising field of research. Methods: In this study, we developed a comprehensive metabolic signature using RNA-sequencing data from independent cohorts of patients diagnosed with stage I-III resectable lung adenocarcinoma (LUAD) to enhance patient stratification and prognostic accuracy. Results: We identified a novel prognostic signature “LMetSig” consisting of 10 metabolic genes that significantly stratified LUAD patients into high- and low-risk subgroups for disease-free survival (DFS). Cox regression analysis demonstrated that LMetSig is an independent prognostic biomarker for DFS. Among the LMetSig, TK1 gene emerged as a promising LUAD-specific biomarker. It was undetectable in normal tissue, showed variable expression in tumor samples and correlated with shorter DFS when expressed at high levels. Conclusion: Our findings suggest that LMetSig can significantly improve LUAD patients’ stratification alongside conventional pathological and clinical parameters. By distinguishing high-risk patients from those with more favorable prognosis, this approach has the potential for informing personalized treatment strategies and improving clinical decision-making.