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

Shogo Imanaka, Haruki Nakamura, Hiroshi Kobayashi
In: World Academy of Sciences Journal · 2019 · doi:10.3892/wasj.2019.25 · W2980341837
article OA: diamond CC0 ⤵ 1 in-corpus citation
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⚙ AI-generated summary by claude@2026-06, 2026-06-07 ⓘ

This review identified 274 differentially expressed genes specific to adenomyosis, with 50 further validated, suggesting roles for IGF1, OPN, KISS1, NCAM1, VCAN, L-selectin ligand, and ANXA2 in its pathophysiology.

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⚙ AI-generated deep summary by claude@2026-06, 2026-06-07 ⓘ

This review used a bioinformatics strategy to screen gene expression signatures and functional networks that could differentiate adenomyosis from endometriosis, drawing eligible microarray datasets from NCBI-GEO and conducting DEG analysis with GEO2R and functional enrichment using Gene Ontology/annotation tools. Across 3,119 adenomyosis-associated genes, 2,845 (91.2%) overlapped with endometriosis signatures, while 274 DEGs were identified as adenomyosis-specific, and 50 candidate genes had expression profiles confirmed in public repositories. Enriched pathways among the adenomyosis-specific DEGs included processes related to endometrial invasion, cell survival, wound healing, and scarring/fibrosis, with frequently highlighted candidate genes such as IGF1, OPN, KISS1, NCAM1, VCAN, L-selectin ligand, and ANXA2; a key limitation is that results are based on existing microarray datasets and the authors’ selected inclusion criteria/design/platform matching rather than new experimental validation. This paper is centrally about endometriosis and adenomyosis—specifically, it identifies adenomyosis-specific differentially expressed genes and functional networks to distinguish it from endometriosis.

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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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Condition tags

endometriosisadenomyosis

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Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

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