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Background <p>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 <i>omics</i> 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.</p> Methods <p>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.</p> Results <p>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.</p> Conclusion <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月10日

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  • Francesca Jacobs,
  • Lorenzo Manganaro,
  • Lorenzo D’Ambrosio,
  • Davide Corà,
  • Martina Olivero,
  • Francesca Napoli,
  • Marco De Filippis,
  • Valeria Cetoretta,
  • Edoardo Garbo,
  • Teresa Mele,
  • Maddalena Arigoni,
  • Eugenia R. Zanella,
  • Sushant Parab,
  • H. M. Waqas Munir,
  • Francesca Picca,
  • Riccardo Taulli,
  • Francesca Bersani,
  • Alessandra Merlini,
  • Raffaele Calogero,
  • Livio Trusolino,
  • Luca Primo,
  • Luisella Righi,
  • Marco Volante,
  • Francesco Leo,
  • Enrico Ruffini,
  • Mauro Papotti,
  • Federico Bussolino,
  • Silvia Novello,
  • Giorgio V. Scagliotti,
  • Paolo Bironzo,
  • Gabriella Doronzo

摘要

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.