Identification of Potential Molecular Mechanism Related to Infertile Endometriosis

Xiushen Li; Li Guo; Weiwen Zhang; Junli He; Lisha Ai; Chengwei Yu; Hao Wang; Weizheng Liang

doi:10.3389/fvets.2022.845709

Identification of Potential Molecular Mechanism Related to Infertile Endometriosis

Front Vet Sci. 2022 Mar 28:9:845709. doi: 10.3389/fvets.2022.845709. eCollection 2022.

Authors

Xiushen Li^{1

2

3}, Li Guo⁴, Weiwen Zhang¹, Junli He⁵, Lisha Ai⁶, Chengwei Yu^{7

8}, Hao Wang^{1

2

3}, Weizheng Liang⁵

Affiliations

¹ Department of Obstetrics and Gynecology, Shenzhen University General Hospital, Shenzhen, China.
² Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen, China.
³ Shenzhen Key Laboratory, Shenzhen University General Hospital, Shenzhen, China.
⁴ School of Pharmaceutical Sciences, Health Science Center, Shenzhen University, Shenzhen, China.
⁵ Department of Pediatrics, Shenzhen University General Hospital, Shenzhen, China.
⁶ Department of Teaching and Research, Shenzhen University General Hospital, Shenzhen, China.
⁷ School of Future Technology, University of Chinese Academy of Sciences, Beijing, China.
⁸ Chinese Academy of Sciences (CAS) Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.

Abstract

Objectives: In this research, we aim to explore the bioinformatic mechanism of infertile endometriosis in order to identify new treatment targets and molecular mechanism.

Methods: The Gene Expression Omnibus (GEO) database was used to download MRNA sequencing data from infertile endometriosis patients. The "limma" package in R software was used to find differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was used to classify genes into modules, further obtained the correlation coefficient between the modules and infertility endometriosis. The intersection genes of the most disease-related modular genes and DEGs are called gene set 1. To clarify the molecular mechanisms and potential therapeutic targets for infertile endometriosis, we used Gene Ontology (GO), Kyoto Gene and Genome Encyclopedia (KEGG) enrichment, Protein-Protein Interaction (PPI) networks, and Gene Set Enrichment Analysis (GSEA) on these intersecting genes. We identified lncRNAs and miRNAs linked with infertility and created competing endogenous RNAs (ceRNA) regulation networks using the Human MicroRNA Disease Database (HMDD), mirTarBase database, and LncRNA Disease database.

Results: Firstly, WGCNA enrichment analysis was used to examine the infertile endometriosis dataset GSE120103, and we discovered that the Meorangered1 module was the most significantly related with infertile endometriosis. The intersection genes were mostly enriched in the metabolism of different amino acids, the cGMP-PKG signaling pathway, and the cAMP signaling pathway according to KEGG enrichment analysis. The Meorangered1 module genes and DEGs were then subjected to bioinformatic analysis. The hub genes in the PPI network were performed KEGG enrichment analysis, and the results were consistent with the intersection gene analysis. Finally, we used the database to identify 13 miRNAs and two lncRNAs linked to infertility in order to create the ceRNA regulatory network linked to infertile endometriosis.

Conclusion: In this study, we used a bioinformatics approach for the first time to identify amino acid metabolism as a possible major cause of infertility in patients with endometriosis and to provide potential targets for the diagnosis and treatment of these patients.

Keywords: GEO; bioinformatics; ceRNA; infertile endometriosis; molecular mechanism.