ArticleJournal of translational medicine2026
Unraveling lung cancer dynamics: a new metabolic signature improving the prediction of recurrence in resected lung adenocarcinoma.
Article in Journal of translational medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundLung 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.
methodsIn 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.
resultsWe 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.
conclusionOur 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.
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