Metabolomic differentiation of benign vs malignant pulmonary nodules with high specificity via high-resolution mass spectrometry analysis of patient sera

Yao Yao, Xueping Wang, Jian Guan, Chuanbo Xie, Hui Zhang, Jing Yang, Yao Luo, Lili Chen, Mingyue Zhao, Bitao Huo, Tiantian Yu, Wenhua Lu, Qiao Liu, Hongli Du, Yuying Liu, Peng Huang, Tiangang Luan (), Wanli Liu () and Yumin Hu ()
Additional contact information
Yao Yao: Sun Yat-sen University
Xueping Wang: Sun Yat-sen University Cancer Center
Jian Guan: The First Affiliated Hospital of Sun Yat-sen University
Chuanbo Xie: Sun Yat-sen University Cancer Center
Hui Zhang: Sun Yat-sen University Cancer Center
Jing Yang: Sun Yat-sen University Cancer Center
Yao Luo: Sun Yat-sen University Cancer Center
Lili Chen: The First Affiliated Hospital of Sun Yat-sen University
Mingyue Zhao: Sun Yat-sen University Cancer Center
Bitao Huo: Sun Yat-sen University Cancer Center
Tiantian Yu: Zhongshan School of Medicine, Sun Yat-sen University
Wenhua Lu: Sun Yat-sen University Cancer Center
Qiao Liu: Sun Yat-sen University Cancer Center
Hongli Du: South China University of Technology
Yuying Liu: Sun Yat-sen University Cancer Center
Peng Huang: Sun Yat-sen University Cancer Center
Tiangang Luan: Sun Yat-sen University
Wanli Liu: Sun Yat-sen University Cancer Center
Yumin Hu: Sun Yat-sen University Cancer Center

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract Differential diagnosis of pulmonary nodules detected by computed tomography (CT) remains a challenge in clinical practice. Here, we characterize the global metabolomes of 480 serum samples including healthy controls, benign pulmonary nodules, and stage I lung adenocarcinoma. The adenocarcinoma demonstrates a distinct metabolomic signature, whereas benign nodules and healthy controls share major similarities in metabolomic profiles. A panel of 27 metabolites is identified in the discovery cohort (n = 306) to distinguish between benign and malignant nodules. The discriminant model achieves an AUC of 0.915 and 0.945 in the internal validation (n = 104) and external validation cohort (n = 111), respectively. Pathway analysis reveals elevation in glycolytic metabolites associated with decreased tryptophan in serum of lung adenocarcinoma vs benign nodules and healthy controls, and demonstrates that uptake of tryptophan promotes glycolysis in lung cancer cells. Our study highlights the value of the serum metabolite biomarkers in risk assessment of pulmonary nodules detected by CT screening.

Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.nature.com/articles/s41467-023-37875-1 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37875-1

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-023-37875-1

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().