Targeted metabolomic profiling as a tool for diagnostics of patients with non-small-cell lung cancer.

Autor: Shestakova KM; World-Class Research Center Digital Biodesign and Personalized Healthcare, I.M. Sechenov First Moscow State Medical University, Moscow, Russia, 119435., Moskaleva NE; World-Class Research Center Digital Biodesign and Personalized Healthcare, I.M. Sechenov First Moscow State Medical University, Moscow, Russia, 119435., Boldin AA; Laboratory of Pharmacokinetics and Metabolomic Analysis, Institute of Translational Medicine and Biotechnology, I.M. Sechenov First Moscow Medical University, Moscow, Russia, 119435.; I.M. Sechenov First Moscow State Medical University, Moscow, Russia, 119435., Rezvanov PM; Laboratory of Pharmacokinetics and Metabolomic Analysis, Institute of Translational Medicine and Biotechnology, I.M. Sechenov First Moscow Medical University, Moscow, Russia, 119435.; I.M. Sechenov First Moscow State Medical University, Moscow, Russia, 119435., Shestopalov AV; Pirogov Russian National Research Medical University, Moscow, Russia, 117997., Rumyantsev SA; Pirogov Russian National Research Medical University, Moscow, Russia, 117997., Zlatnik EY; National Medical Research Centre for Oncology (Rostov-On-Don, Russia), 14 Liniya, 63, Rostov-on-Don, Russia, 344019., Novikova IA; National Medical Research Centre for Oncology (Rostov-On-Don, Russia), 14 Liniya, 63, Rostov-on-Don, Russia, 344019., Sagakyants AB; National Medical Research Centre for Oncology (Rostov-On-Don, Russia), 14 Liniya, 63, Rostov-on-Don, Russia, 344019., Timofeeva SV; National Medical Research Centre for Oncology (Rostov-On-Don, Russia), 14 Liniya, 63, Rostov-on-Don, Russia, 344019., Simonov Y; Laboratory of Pharmacokinetics and Metabolomic Analysis, Institute of Translational Medicine and Biotechnology, I.M. Sechenov First Moscow Medical University, Moscow, Russia, 119435., Baskhanova SN; World-Class Research Center Digital Biodesign and Personalized Healthcare, I.M. Sechenov First Moscow State Medical University, Moscow, Russia, 119435., Tobolkina E; Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, 1206, Geneva 4, Switzerland. elena.tobolkina@unige.ch., Rudaz S; Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, 1206, Geneva 4, Switzerland., Appolonova SA; Laboratory of Pharmacokinetics and Metabolomic Analysis, Institute of Translational Medicine and Biotechnology, I.M. Sechenov First Moscow Medical University, Moscow, Russia, 119435.; I.M. Sechenov First Moscow State Medical University, Moscow, Russia, 119435.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2023 Jul 08; Vol. 13 (1), pp. 11072. Date of Electronic Publication: 2023 Jul 08.
DOI: 10.1038/s41598-023-38140-7
Abstrakt: Lung cancer is referred to as the second most common cancer worldwide and is mainly associated with complex diagnostics and the absence of personalized therapy. Metabolomics may provide significant insights into the improvement of lung cancer diagnostics through identification of the specific biomarkers or biomarker panels that characterize the pathological state of the patient. We performed targeted metabolomic profiling of plasma samples from individuals with non-small cell lung cancer (NSLC, n = 100) and individuals without any cancer or chronic pathologies (n = 100) to identify the relationship between plasma endogenous metabolites and NSLC by means of modern comprehensive bioinformatics tools, including univariate analysis, multivariate analysis, partial correlation network analysis and machine learning. Through the comparison of metabolomic profiles of patients with NSCLC and noncancer individuals, we identified significant alterations in the concentration levels of metabolites mainly related to tryptophan metabolism, the TCA cycle, the urea cycle and lipid metabolism. Additionally, partial correlation network analysis revealed new ratios of the metabolites that significantly distinguished the considered groups of participants. Using the identified significantly altered metabolites and their ratios, we developed a machine learning classification model with an ROC AUC value equal to 0.96. The developed machine learning lung cancer model may serve as a prototype of the approach for the in-time diagnostics of lung cancer that in the future may be introduced in routine clinical use. Overall, we have demonstrated that the combination of metabolomics and up-to-date bioinformatics can be used as a potential tool for proper diagnostics of patients with NSCLC.
(© 2023. The Author(s).)
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje