Open Access

A gene interaction network‑based method to measure the common and heterogeneous mechanisms of gynecological cancer

  • Authors:
    • Mingyuan Wang
    • Liping Li
    • Jinglan Liu
    • Jinjin Wang
  • View Affiliations

  • Published online on: May 3, 2018     https://doi.org/10.3892/mmr.2018.8961
  • Pages: 230-242
  • Copyright: © Wang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Gynecological malignancies are a leading cause of mortality in the female population. The present study intended to identify the association between three severe types of gynecological cancer, specifically ovarian cancer, cervical cancer and endometrial cancer, and to identify the connective driver genes, microRNAs (miRNAs) and biological processes associated with these types of gynecological cancer. In the present study, individual driver genes for each type of cancer were identified using integrated analysis of multiple microarray data. Gene Ontology (GO) has been used widely in functional annotation and enrichment analysis. In the present study, GO enrichment analysis revealed a number of common biological processes involved in gynecological cancer, including ‘cell cycle’ and ‘regulation of macromolecule metabolism’. Kyoto Encyclopedia of Genes and Genomes pathway analysis is a resource for understanding the high‑level functions and utilities of a biological system from molecular‑level information. In the present study, the most common pathway was ‘cell cycle’. A protein‑protein interaction network was constructed to identify a hub of connective genes, including minichromosome maintenance complex component 2 (MCM2), matrix metalloproteinase 2 (MMP2), collagen type I α1 chain (COL1A1) and Jun proto‑oncogene AP‑1 transcription factor subunit (JUN). Survival analysis revealed that the expression of MCM2, MMP2, COL1A1 and JUN was associated with the prognosis of the aforementioned gynecological cancer types. By constructing an miRNA‑driver gene network, let‑7 targeted the majority of the driver genes. In conclusion, the present study demonstrated a connection model across three types of gynecological cancer, which was useful in identifying potential diagnostic markers and novel therapeutic targets, in addition to in aiding the prediction of the development of cancer as it progresses.
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July-2018
Volume 18 Issue 1

Print ISSN: 1791-2997
Online ISSN:1791-3004

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Copy and paste a formatted citation
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Spandidos Publications style
Wang M, Li L, Liu J and Wang J: A gene interaction network‑based method to measure the common and heterogeneous mechanisms of gynecological cancer. Mol Med Rep 18: 230-242, 2018.
APA
Wang, M., Li, L., Liu, J., & Wang, J. (2018). A gene interaction network‑based method to measure the common and heterogeneous mechanisms of gynecological cancer. Molecular Medicine Reports, 18, 230-242. https://doi.org/10.3892/mmr.2018.8961
MLA
Wang, M., Li, L., Liu, J., Wang, J."A gene interaction network‑based method to measure the common and heterogeneous mechanisms of gynecological cancer". Molecular Medicine Reports 18.1 (2018): 230-242.
Chicago
Wang, M., Li, L., Liu, J., Wang, J."A gene interaction network‑based method to measure the common and heterogeneous mechanisms of gynecological cancer". Molecular Medicine Reports 18, no. 1 (2018): 230-242. https://doi.org/10.3892/mmr.2018.8961