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Background <p>Increasing pulmonary nodule presentations in lung adenocarcinoma patients reveal diagnostic limitations of CT-based invasiveness assessment. The critical unmet need lies in developing non-invasive biomarkers differentiating invasive adenocarcinoma from premalignant lesions and benign nodules, while characterizing metabolic trajectory from health to metastatic disease.</p> Methods <p>Untargeted metabolomics analyzed plasma samples from 102 subjects stratified into four cohorts: confirmed adenocarcinoma (<i>n</i> = 35), benign nodules (<i>n</i> = 22), precursor lesions (<i>n</i> = 24), and healthy controls (<i>n</i> = 21). Multivariate analysis identified discriminative metabolites for constructing an infiltration prediction model.</p> Results <p>Three diagnostic groups exhibited distinct metabolic profiles. Hexaethylene glycol, tetraethylene glycol, and Met-Thr showed stage-dependent concentration gradients. Progressive malignancy correlated with elevated levels of 41 metabolites. An eight-metabolite panel achieved AUC 0.933 (0.873–0.994) in distinguishing precursors from early malignancies, sustained through internal validation (AUC 0.934, 0.905–0.966).</p> Conclusions <p>Met-Thr depletion inversely correlates with malignancy progression, while eight-metabolite signatures demonstrate diagnostic potential for preoperative infiltration assessment in nodular adenocarcinoma.</p>

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Metabolic characteristics of benign and malignant pulmonary nodules and establishment of invasive lung adenocarcinoma model by high-resolution mass spectrometry

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月8日

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  • Junbao Zhang,
  • Zhihan Zhang,
  • Yuying Liu,
  • Yanyi Hou,
  • Ruifang Pang,
  • Yuenan Wang,
  • Ping Xu

摘要

Background

Increasing pulmonary nodule presentations in lung adenocarcinoma patients reveal diagnostic limitations of CT-based invasiveness assessment. The critical unmet need lies in developing non-invasive biomarkers differentiating invasive adenocarcinoma from premalignant lesions and benign nodules, while characterizing metabolic trajectory from health to metastatic disease.

Methods

Untargeted metabolomics analyzed plasma samples from 102 subjects stratified into four cohorts: confirmed adenocarcinoma (n = 35), benign nodules (n = 22), precursor lesions (n = 24), and healthy controls (n = 21). Multivariate analysis identified discriminative metabolites for constructing an infiltration prediction model.

Results

Three diagnostic groups exhibited distinct metabolic profiles. Hexaethylene glycol, tetraethylene glycol, and Met-Thr showed stage-dependent concentration gradients. Progressive malignancy correlated with elevated levels of 41 metabolites. An eight-metabolite panel achieved AUC 0.933 (0.873–0.994) in distinguishing precursors from early malignancies, sustained through internal validation (AUC 0.934, 0.905–0.966).

Conclusions

Met-Thr depletion inversely correlates with malignancy progression, while eight-metabolite signatures demonstrate diagnostic potential for preoperative infiltration assessment in nodular adenocarcinoma.