CC BY
15B-I-30
Diagnosis of glioma molecular markers in blood using spectroscopy and machine learning
O. Cherkasova12, A. Mankova3, M. Konnikova23, D. Vrazhnov4, Yu. Kistenev56, Y.Peng7,
A. Shkurinov23
1-Institute of Laser Physics of SB RAS, 15B Lavrentyev Ave., 630090 Novosibirsk, Russia 2- Institute on Laser and Information Technologies - Branch of the Federal Scientific Research Centre "Crystallography and Photonics" of RAS, 1 Svyatoozerskaya st., 140700 Shatura, Moscow Region, Russia 3 - Lomonosov Moscow State University, 1 Leninskiye Gory, 119991 Moscow, Russia, 4 - Institute of Atmospheric Optics, Siberian Branch of the RAS, pr. Akademicheskii 1, 634055 Tomsk, Russia 5 - Tomsk State University, 36 Lenin Ave., 634050 Tomsk, Russia 6 - Siberian State Medical University, 2 Moskovsky trakt, 634050 Tomsk, Russia 7 - University of Shanghai for Science and Technology, 516 Jungong Rd., 200093 Shanghai, R. P. China
e-mail: [email protected]
Gliomas are brain tumors with high rates of recurrence and mortality. Early diagnosis of gliomas may be achieved by detecting the molecular biomarkers in body fluids [1]. It has been recently shown that one of specific markers for glioma differential diagnostics are enantiomers of 2-hydroxyglutarate (L-2HG and D-2HG), presented in brain tissues and blood. Blood constituents can be measured by THz, IR and Raman spectroscopy [2-4]. In this work, we use these methods, combined with machine learning, to study mouse blood plasma during glioblastoma development.
U87 human glioblastoma in the SCID line mice was created following the method described in [5]. Animals were excluded from the experiment at 7-th, 14-th, 21-st and 28-th day after tumor cells transplantation. Magnetic resonance spectroscopy has been used to identify glioma molecular markers at different stages of tumor growth [6]. A detailed description of the spectrometers was presented in our previous papers [2, 5].The spectral data analysis was conducted by the Principal Component Analysis, and multidimensional scaling. The prognostic models were developed by the linear kernel Support vector machine, Random forests, and Gradient boosting methods [4, 7]. A novel approach based on the Raman and absorption spectroscopy for detection of glioma molecular markers in blood is discussed.
This work was supported by the Russian Foundation for Basic Research (grant # 19-52-55004), the Ministry of Science and Higher Education of the Russian Federation within the Agreement No. 07515-2019-1950, within the State assignment FSRC "Crystallography and Photonics" RAS; by the Interdisciplinary Scientific and Educational School of Moscow University "Photonic and Quantum Technologies. Digital Medicine". This work was supported by the Government of the Russian Federation (proposal no. №2020-220-08-2389 to support scientific research projects implemented under the supervision of leading scientists at Russian institutions of higher education).
[1] O. Cherkasova, Y. Peng, M. Konnikova, et al., "Diagnosis of Glioma Molecular Markers by Terahertz Technologies", Photonics, vol. 8(1), pp. 22, (2021).
[2] M. M. Nazarov, O. P. Cherkasova, E. N. Lazareva, et al., "A Complex Study of the Peculiarities of Blood Serum Absorption of Rats with Experimental Liver Cancer", Opt. Spectrosc, vol. 126, pp. 721-729, (2019).
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[4]. A. A. Mankova, O. P. Cherkasova, E. N. Lazareva, et al., "Study of Blood Serum in Rats with Transplanted Cholangiocarcinoma Using Raman Spectroscopy", Opt. Spectrosc, vol. 128(7), pp. 964-971, (2020)
[5] E. L. Zavjalov, I. A. Razumov, L. A. Gerlinskaya, A. V. Romashchenko, "In vivo MRI Visualization of U87 Glioblastoma Development Dynamics in the Model of Orthotopic Xenotransplantation to the SCID Mouse", Russ. J. Genet. Appl. Res., vol. 6 (4), pp. 448-453, (2016).
[6] Shevelev O.B., Seryapina A.A., Zavjalov E.L. et al. // Phytomedicine. 2018. V. 41. P. 1.
[7] Kistenev Y.V., Borisov A.V., Kuzmin D.A. et al. J. Biomed. Opt. 22(1), 017002 (2017).
