Evidence map›Paper›PMID 40340585›Full record

ArticleBMC cancer2025

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

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

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In one paragraph

Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Junbao Zhang *Department of Pulmonary and Critical Care Medicine, Peking University Shenzhen Hospital, Shenzhen, 518034, Guangdong Province, People's Republic of China.
Zhihan Zhang *Department of Pulmonary and Critical Care Medicine, Peking University Shenzhen Hospital, Shenzhen, 518034, Guangdong Province, People's Republic of China.
Yuying LiuDepartment of Pulmonary and Critical Care Medicine, Peking University Shenzhen Hospital, Shenzhen, 518034, Guangdong Province, People's Republic of China.
Yanyi HouDepartment of Pulmonary and Critical Care Medicine, Peking University Shenzhen Hospital, Shenzhen, 518034, Guangdong Province, People's Republic of China.
Ruifang PangDepartment of Pulmonary and Critical Care Medicine, Peking University Shenzhen Hospital, Shenzhen, 518034, Guangdong Province, People's Republic of China.
Yuenan WangDepartment of Therapeutic Radiology, Yale University School of Medicine, New Haven, USA. yuenan.wang@yale.edu.
Ping XuDepartment of Pulmonary and Critical Care Medicine, Peking University Shenzhen Hospital, Shenzhen, 518034, Guangdong Province, People's Republic of China. ping-xu@hotmail.com.

Funding

General Program for Clinical Research at Peking University Shenzhen Hospital No. LCYJ2021022Natural Science Foundation of Guangdong Province 2023A1515012460Shenzhen Science and technology innovation Commission foundation JCYJ20210324105411031
6 · The paper itself

Abstract

backgroundIncreasing 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.

methodsUntargeted 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.

resultsThree 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).

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

Indexed as

Adenocarcinoma of LungBiomarkers, TumorLung NeoplasmsMetabolomicsMultiple Pulmonary NodulesSolitary Pulmonary NoduleAdultAgedFemaleHumansMaleMass SpectrometryMiddle AgedBiomarkers, TumorHigh-resolution mass spectrometryLung adenocarcinomaLung nodulesMetabolomicsNoninvasive metabolomic model

Identifiers

PMID40340585
PMCPMC12063296

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