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Vol 55(2021) N 4 p. 548-554; DOI 10.1134/S0026893321020175  O.I. Brovkina1,2*, I.V. Pronina1, L.A. Uroshlev1,3, M.V. Fridman3, V.I. Loginov1, T.P. Kazubskaya4, D.O. Utkin4, N.E. Kushlinskii4, E.A. Braga1 Identification of Novel Differentially Expressing Long Non- Coding RNAs with Oncogenic Potential 1Research Institute of General Pathology and Pathophysiology, Moscow, 125315 Russia2Federal Scientific and Clinical Center for Specialized Types of Medical Aid and Medical Technologies of FMBA of Russia, Moscow, 115682 Russia 3Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, 117971 Russia 4Blokhin National Medical Research Center of Oncology of the Ministry of Health, Russian Federation, Moscow, 115478 Russia *brov.olia@gmail.com Received - 2020-11-17; Revised - 2020-12-05; Accepted - 2020-12-07 Recently, a wealth of data have been accumulating on the role of long non-coding RNAs (lncRNAs) in the fine-tuning of mRNA expression. Four new lncRNAs, namely, TMEM92-AS1, FAM222A-AS, TXLNB, and lnc-CCL28, were identified as differentially expressed in ovarian tumors using deep machine learning. The levels of lnc-CCL28 transcripts in both tumors and normal tissue samples were sufficient for further analysis by RT-PCR. In addition, the promising ovarian cancer biomarkers, lncRNAs LINC00152, NEAT 1 and SNHG17 were added to RT-PCR analysis. For the first time, an increase in the level of lnc-CCL28 and SNHG 17 lncRNAs was found in ovarian tumors, and the overexpression of LINC00152 and NEAT1 was confirmed. It seems that lnc-CCL28 is involved in carcinogenesis and, in particular, in ovarian cancer progression. Overexpression of LINC00152 and lnc-CCL28 was significantly associated with the later stages and metastasis. long noncoding RNAs, deep machine learning, differential expression, oncogenic potential, prognostic marker, lnc-CCL28 |
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