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

• Published in: BMC cancer Vol. 25; no. 1; pp. 844 - 12
• Main Authors: Zhang, Junbao, Zhang, Zhihan, Liu, Yuying, Hou, Yanyi, Pang, Ruifang, Wang, Yuenan, Xu, Ping
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 08.05.2025; BioMed Central; BMC
• Subjects: Adenocarcinoma; Adenocarcinoma of Lung - blood; Adenocarcinoma of Lung - diagnosis; Adenocarcinoma of Lung - metabolism; Adenocarcinoma of Lung - pathology; Adult; Aged; Analysis; Biomarkers; Biomarkers, Tumor - blood; Biomarkers, Tumor - metabolism; Care and treatment; Diagnosis; Female; High-resolution mass spectrometry; Humans; Invasiveness; Lung adenocarcinoma; Lung cancer; Lung Neoplasms - blood; Lung Neoplasms - diagnosis; Lung Neoplasms - metabolism; Lung Neoplasms - pathology; Lung nodules; Male; Malignancy; Mass spectrometry; Mass Spectrometry - methods; Mass spectroscopy; Medical screening; Metabolites; Metabolomics; Metabolomics - methods; Metastases; Middle Aged; Mortality; Multiple Pulmonary Nodules - diagnosis; Multiple Pulmonary Nodules - metabolism; Multiple Pulmonary Nodules - pathology; Multivariate analysis; Noninvasive metabolomic model; Prediction models; Risk factors; Solitary Pulmonary Nodule - diagnosis; Solitary Pulmonary Nodule - metabolism; Solitary Pulmonary Nodule - pathology; Tumor staging; China; Metabolomics; High-resolution mass spectrometry; Lung adenocarcinoma; Lung nodules; Noninvasive metabolomic model
• ISSN: 1471-2407; 1471-2407
• DOI: 10.1186/s12885-025-14253-2

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