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Background <p>Lung adenocarcinoma (LUAD) represents a significant proportion of lung cancer cases, with immunotherapy serving as a cornerstone of treatment. Although immunotherapy has advanced significantly, the prognosis for patients in advanced stages remains suboptimal. Lactylation, a newly discovered metabolic pathway, has been recognized as a critical mechanism driving the initiation and development of many cancers, including LUAD. Using machine learning, this research aims to pinpoint lactylation-related lncRNAs (LRLs) that could serve as prognostic markers in LUAD, shedding light on tumor development and novel therapeutic avenues.</p> Results <p>Four LRLs (AC090115.1, AC092718.4, AC092718.6, and AL451064.1) were incorporated into a prognostic model. This model successfully categorized patients into high-risk and low-risk groups, revealing differences in prognosis. Patients in the low-risk group demonstrated a better prognosis, while the other category showed aggressive tumor characteristics and reduced responses to immunotherapy.</p> Conclusions <p>This study developed an association between LRLs and outcomes in lung adenocarcinoma, suggesting that they can be used as a prognostic marker and therapeutic target. The observed differences in their expression between tumor and normal tissues further underline their significance in LUAD pathogenesis. By advancing the understanding of LUAD biology, this predictive model provides a novel platform for precision medicine, paving the way for personalized therapeutic strategies and improved prognostic accuracy for LUAD patients.</p>

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Lactylation-related lncRNAs can forecast prognosis and immune response of lung adenocarcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月22日

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  • Le Zhou,
  • Zhongjiang Lan,
  • Bo Han,
  • Yinjiao Yuan,
  • Zhuowei Dong,
  • Yijing Shi,
  • Ruinian Zheng

摘要

Background

Lung adenocarcinoma (LUAD) represents a significant proportion of lung cancer cases, with immunotherapy serving as a cornerstone of treatment. Although immunotherapy has advanced significantly, the prognosis for patients in advanced stages remains suboptimal. Lactylation, a newly discovered metabolic pathway, has been recognized as a critical mechanism driving the initiation and development of many cancers, including LUAD. Using machine learning, this research aims to pinpoint lactylation-related lncRNAs (LRLs) that could serve as prognostic markers in LUAD, shedding light on tumor development and novel therapeutic avenues.

Results

Four LRLs (AC090115.1, AC092718.4, AC092718.6, and AL451064.1) were incorporated into a prognostic model. This model successfully categorized patients into high-risk and low-risk groups, revealing differences in prognosis. Patients in the low-risk group demonstrated a better prognosis, while the other category showed aggressive tumor characteristics and reduced responses to immunotherapy.

Conclusions

This study developed an association between LRLs and outcomes in lung adenocarcinoma, suggesting that they can be used as a prognostic marker and therapeutic target. The observed differences in their expression between tumor and normal tissues further underline their significance in LUAD pathogenesis. By advancing the understanding of LUAD biology, this predictive model provides a novel platform for precision medicine, paving the way for personalized therapeutic strategies and improved prognostic accuracy for LUAD patients.