| Autor: |
Starodubtseva NL; V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia.; Department of Chemical Physics, Moscow Institute of Physics and Technology, 141700 Moscow, Russia., Tokareva AO; V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia., Rodionov VV; V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia., Brzhozovskiy AG; V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia.; Laboratory of Omics Technologies and Big Data for Personalized Medicine and Health, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia., Bugrova AE; V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia.; Emanuel Institute of Biochemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia., Chagovets VV; V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia., Kometova VV; V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia., Kukaev EN; V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia.; V.L. Talrose Institute for Energy Problems of Chemical Physics, N.N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia., Soares NC; Department of Medicinal Chemistry, College of Pharmacy, University of Sharjah, Sharjah 27272, United Arab Emirates., Kovalev GI; Laboratory of Omics Technologies and Big Data for Personalized Medicine and Health, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia., Kononikhin AS; V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia.; Laboratory of Omics Technologies and Big Data for Personalized Medicine and Health, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia., Frankevich VE; V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia.; Laboratory of Translational Medicine, Siberian State Medical University, 634050 Tomsk, Russia., Nikolaev EN; Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia., Sukhikh GT; V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia. |
| Abstrakt: |
Metastasis is a serious and often life-threatening condition, representing the leading cause of death among women with breast cancer (BC). Although the current clinical classification of BC is well-established, the addition of minimally invasive laboratory tests based on peripheral blood biomarkers that reflect pathological changes in the body is of utmost importance. In the current study, the serum proteome and lipidome profiles for 50 BC patients with (25) and without (25) metastasis were studied. Targeted proteomic analysis for concertation measurements of 125 proteins in the serum was performed via liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM MS) using the BAK 125 kit (MRM Proteomics Inc., Victoria, BC, Canada). Untargeted label-free lipidomic analysis was performed using liquid chromatography coupled to tandem mass-spectrometry (LC-MS/MS), in both positive and negative ion modes. Finally, 87 serum proteins and 295 lipids were quantified and showed a moderate correlation with tumor grade, histological and biological subtypes, and the number of lymph node metastases. Two highly accurate classifiers that enabled distinguishing between metastatic and non-metastatic BC were developed based on proteomic (accuracy 90%) and lipidomic (accuracy 80%) features. The best classifier (91% sensitivity, 89% specificity, AUC = 0.92) for BC metastasis diagnostics was based on logistic regression and the serum levels of 11 proteins: alpha-2-macroglobulin, coagulation factor XII, adiponectin, leucine-rich alpha-2-glycoprotein, alpha-2-HS-glycoprotein, Ig mu chain C region, apolipoprotein C-IV, carbonic anhydrase 1, apolipoprotein A-II, apolipoprotein C-II and alpha-1-acid glycoprotein 1. |
