Успехи молекулярной онкологии (Sep 2020)

Differential expression of microRNAs and their target genes in cervical intraepithelial neoplasias of varying severity

  • T. A. Dimitriadi,
  • D. V. Burtsev,
  • E. A. Dzhenkova,
  • D. S. Kutilin

DOI
https://doi.org/10.17650/2313-805X-2020-7-2-47-61
Journal volume & issue
Vol. 7, no. 2
pp. 47 – 61

Abstract

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Background. Currently, little is known about the specific microRNAs involved in the development of cervical intraepithelial neoplasia (CIN1, 2, 3) and the transition to cancer in situ (CIS). Our meta-analysis allowed us to isolate 8 microRNAs (hsa-miR-1246, hsa-miR- 145-5p, hsa-miR-196b-5p, hsa-miR-34a-5p, hsa-miR-20a-5p, hsa-miR-21-5p, hsa-miR-375-5p, hsa-miR-96-5p) with potential significance in the progression of precancerous diseases to cervical cancer. Objective: to analyze the expression features of hsa-miR-1246, hsa-miR-145-5p, hsa-miR-196b-5p, hsa-miR-34a-5p, hsa-miR-20a-5p, hsa-miR-21-5p, hsa-miR-375-5p, hsa-miR-96-5p and their target genes, as well as genes associated with them in common signaling pathways in the tissues of the cervix in patients with CIN1–3 and CIS. Materials and methods. To assess the expression level of microRNA and matrixRNA, the quantitative polymerase chain reaction in real time method was used. Data analysis was carried out in the Python programming language using the SciPy library. Search for target genes was performed using the TarPmiR algorithm and the overrepresentation of microRNAs in signaling pathways (Over-Representation Analysis) was analyzed. To identify genes associated with target genes in common signaling pathways, GIANT (Genome-scale Integrated Analysis of gene Networks in Tissues) and network integration with several associations algorithms were used. Results. For microRNAs miR-145, miR-196b, miR-34a, miR-20a, miR-21, miR-375 and miR-96 a decrease in expression was found in the subgroup of patients with CIS, while for 4 microRNAs (miR-145, miR-34a, miR-20a and miR-375), an increase in the expression level was found for CIN1, 2. The detected features of microRNA expression in subgroups of patients with CIN1–3 and CIS also affected the expression of their target genes (CDKN2A, MKI67, TOP2A and CD82), as well as the genes associated with them in common signaling pathways (PGK1, THBS4 (TSP4) and ECM1). Conclusion. Thus, the study revealed that each degree of CIN is characterized by its own specific molecular profile – the differential expression of microRNAs, their target genes and the genes associated with them in the general signaling pathways.

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