Open Access

Bioinformatics strategy for the screening of key genes to differentiate adenomyosis from endometriosis (Review)

  • Authors:
    • Shogo Imanaka
    • Haruki Nakamura
    • Hiroshi Kobayashi
  • View Affiliations

  • Published online on: October 15, 2019     https://doi.org/10.3892/wasj.2019.25
  • Pages: 203-218
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Abstract

Adenomyosis is one of the most common gynecological diseases worldwide. Despite intense efforts to elucidate the pathogenesis, the molecular mechanisms responsible for adenomyosis are not yet fully understood. The aim of this review was to screen candidate functional networks and identify key genes to differentiate adenomyosis from endometriosis. A search was conducted between 2000 and 2018 through the English language literature (online MEDLINE PubMed database) using the key words, adenomyosis combined with endometriosis, differentially expressed genes (DEGs) and functional networks. Eligible microarray datasets were collected from the NCBI Gene Expression Omnibus (GEO) database, NCBI‑GEO (http://www.ncbi.nlm.nih.gov/geo/). The mRNA and/or protein expression of adenomyosis candidate genes was confirmed by the published data (https://www.ncbi.nlm.nih.gov/pubmed). The comprehensive gene expression analysis based on the NCBI‑GEO database revealed the candidate DEGs and functional networks. Among a total of 3,119 genes collected from the adenomyosis signatures, 2,845 genes (91.2%) overlapped in the endometriosis signatures. A total of 274 DEGs were identified to be specific to adenomyotic lesions. Among these, we selected 50 genes whose expression profiles have been published in a public repository. Although the two conditions may share common pathways, a set of DEGs, including insulin‑like growth factor 1 (IGF1), osteopontin (OPN), KiSS‑1 metastasis suppressor (KISS1), neural cell adhesion molecule 1 (NCAM1, also known as CD56), versican (VCAN), L‑selectin ligand and Annexin A2, (ANXA2), have been suggested to play a pivotal role in the pathophysiology of adenomyosis. The enriched functions and pathways of the DEGs include the process of endometrial invasion, cell survival, wound healing, scarring and fibrosis. On the whole, in this review, we present a comprehensive gene expression analysis based on NCBI‑GEO database to identify adenomyosis candidate genes. DEGs and functional networks help us to understand the molecular mechanisms underlying the pathogenesis of adenomyosis, and provide candidate targets for the diagnosis and treatment of adenomyosis.

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September 2019
Volume 1 Issue 5

Print ISSN: 2632-2900
Online ISSN:2632-2919

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APA
Imanaka, S., Nakamura, H., & Kobayashi, H. (2019). Bioinformatics strategy for the screening of key genes to differentiate adenomyosis from endometriosis (Review). World Academy of Sciences Journal, 1, 203-218. https://doi.org/10.3892/wasj.2019.25
MLA
Imanaka, S., Nakamura, H., Kobayashi, H."Bioinformatics strategy for the screening of key genes to differentiate adenomyosis from endometriosis (Review)". World Academy of Sciences Journal 1.5 (2019): 203-218.
Chicago
Imanaka, S., Nakamura, H., Kobayashi, H."Bioinformatics strategy for the screening of key genes to differentiate adenomyosis from endometriosis (Review)". World Academy of Sciences Journal 1, no. 5 (2019): 203-218. https://doi.org/10.3892/wasj.2019.25