Abstract

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.

Details

Title
Targeted metabolomic profiling as a tool for diagnostics of patients with non-small-cell lung cancer
Author
Shestakova, Ksenia M. 1 ; Moskaleva, Natalia E. 1 ; Boldin, Andrey A. 2 ; Rezvanov, Pavel M. 2 ; Shestopalov, Alexandr V. 3 ; Rumyantsev, Sergey A. 3 ; Zlatnik, Elena Yu. 4 ; Novikova, Inna A. 4 ; Sagakyants, Alexander B. 4 ; Timofeeva, Sofya V. 4 ; Simonov, Yuriy 5 ; Baskhanova, Sabina N. 1 ; Tobolkina, Elena 6 ; Rudaz, Serge 6 ; Appolonova, Svetlana A. 2 

 I.M. Sechenov First Moscow State Medical University, World-Class Research Center Digital Biodesign and Personalized Healthcare, Moscow, Russia (GRID:grid.448878.f) (ISNI:0000 0001 2288 8774) 
 I.M. Sechenov First Moscow Medical University, Laboratory of Pharmacokinetics and Metabolomic Analysis, Institute of Translational Medicine and Biotechnology, Moscow, Russia (GRID:grid.448878.f) (ISNI:0000 0001 2288 8774); I.M. Sechenov First Moscow State Medical University, Moscow, Russia (GRID:grid.448878.f) (ISNI:0000 0001 2288 8774) 
 Pirogov Russian National Research Medical University, Moscow, Russia (GRID:grid.78028.35) (ISNI:0000 0000 9559 0613) 
 National Medical Research Centre for Oncology (Rostov-On-Don, Russia), Rostov-on-Don, Russia (GRID:grid.482632.9) (ISNI:0000 0004 0620 1591) 
 I.M. Sechenov First Moscow Medical University, Laboratory of Pharmacokinetics and Metabolomic Analysis, Institute of Translational Medicine and Biotechnology, Moscow, Russia (GRID:grid.448878.f) (ISNI:0000 0001 2288 8774) 
 University of Geneva, Institute of Pharmaceutical Sciences of Western Switzerland, Geneva 4, Switzerland (GRID:grid.8591.5) (ISNI:0000 0001 2322 4988) 
Pages
11072
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2834541876
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.