Identification of Functional lncRNAs Associated With Ovarian Endometriosis Based on a ceRNA Network

Jian Bai, Bo Wang, Tian Wang, Wu Ren
Frontiers in genetics · 2021 · vol. 12 , pp. 534054 · doi:10.3389/fgene.2021.534054 · PMID:33584822 · W3191677838
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⚙ AI-generated summary by claude@2026-06, 2026-06-07 ⓘ

This bioinformatics study constructed a ceRNA network from differentially expressed RNAs in ovarian endometriosis samples, identifying H19, GS1-358P8.4, and RP11-96D1.10 as key associated lncRNAs.

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

This study used RNA-seq data from eight paired ectopic and eutopic endometrial samples from patients with ovarian endometriosis to identify differentially expressed mRNAs, lncRNAs, and miRNAs, and then constructed an ovarian endometriosis-related competing endogenous RNA (ceRNA) network using StarBase interaction data plus a hypergeometric test to select dysregulated lncRNA–mRNA pairs. Across EC vs EU samples, the authors reported 4,213 mRNAs, 1,474 lncRNAs, and 221 miRNAs as differentially expressed, yielding 18,064 dysregulated lncRNA–mRNA pairs in the ceRNA network. Topological analysis and a random walk with restart approach using DisGeNET seed genes prioritized H19 and GS1-358P8.4 as hub lncRNAs and RP11-96D1.10 as an additional high-scoring lncRNA, with enrichment based on lncRNA–mRNA correlation as a functional assessment; a key limitation is that all results are derived from bioinformatics and require further experimental validation to define biological functions. This paper is centrally about endometriosis — it identifies functional ovarian endometriosis-associated lncRNAs (H19, GS1-358P8.4, and RP11-96D1.10) via an lncRNA–miRNA–mRNA ceRNA network.

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Abstract

BACKGROUND: Endometriosis is a common gynecological disease affecting women of reproductive age; however, the mechanisms underlying this condition are not fully clear. The aim of this study was to identify functional long non-coding RNAs (lncRNAs) associated with ovarian endometriosis for potential use as biomarkers and therapeutic targets. METHODS: RNA-seq profiles of paired ectopic (EC) and eutopic (EU) endometrial samples from patients with ovarian endometriosis were downloaded from the publicly available Gene Expression Omnibus (GEO) database. Bioinformatics algorithms were used to construct a network of ovarian endometriosis-related competing endogenous RNAs (ceRNAs) and to detect functional lncRNAs. RESULTS: A total of 4,213 mRNAs, 1,474 lncRNAs, and 221 miRNAs were identified as being differentially expressed between EC and EU samples, and an ovarian endometriosis-related ceRNA network was constructed through analysis of these differentially expressed RNAs. H19 and GS1-358P8.4 were identified as key ovarian endometriosis-related lncRNAs through topological feature analysis, and RP11-96D1.10 was identified using a random walk with restart algorithm. CONCLUSION: Based on bioinformatics analysis of a ceRNA network, we identified the lncRNAs H19, GS1-358P8.4, and RP11-96D1.10 as being strongly associated with ovarian endometriosis. These three lncRNAs hold potential as targets for medical therapy and as diagnostic biomarkers. Further studies are needed to elucidate the detailed biological function of these lncRNAs in the pathogenesis of endometriosis.

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endometriosis

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