AUTHOR=Li Xiushen , Guo Li , Zhang Weiwen , He Junli , Ai Lisha , Yu Chengwei , Wang Hao , Liang Weizheng 

TITLE=Identification of Potential Molecular Mechanism Related to Infertile Endometriosis

JOURNAL=Frontiers in Veterinary Science

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2022.845709

DOI=10.3389/fvets.2022.845709

ISSN=2297-1769

ABSTRACT=Objectives: In this research, we aim to explore the bioinformatic mechanism of infertile endometriosis in order to identify new treatment targets and research directions.
Methods: The mRNA sequencing data of infertile endometriosis patients is downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes are identified using the "limma" R package. Through Weighted Gene Co-Expression Network Analysis (WGCNA), the genes are divided into different modules and the correlation between the modules and infertile endometriosis is calculated. Gene set 1 represents the intersection of differentially expressed genes (DEGs) and the module most relevant to the disease. The following methods can be used for further exploration of the molecular mechanism of endometriosis leading to infertility: Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, Protein Protein Interaction Network (PPI) network, and Gene Set Enrichment Analysis (GSEA). Using the Human MicroRNA (miRNA) Disease Database (HMDD) and the Mitbase database, we identify miRNA related to infertility and identify their potential targets mRNAs, and then build a miRNA-mRNA regulatory network after intersecting with gene set 1.
Results: The GSE120103 infertile endometriosis data set was analyzed through WGCNA enrichment analysis, and we discovered the orangered1 module had the strongest correlation with infertile endometriosis. The ORangered1 module gene and DEGs are intersected and analyzed by related bioinformatics. KEGG enrichment analysis found that its modular genes were mainly enriched in the metabolism of a variety of amino acids, such as cGMP−PKG signaling pathway, cAMP signaling pathway, and so on. The results of the biological analysis of the hub genes of the PPI network are basically consistent. The miRNA-mRNA regulation network of infertile endometriosis was eventually established after the discovery of 15 miRNA linked to infertility.
Conclusion: For the first time, we use bioinformatics tools to identify the hub genes and pathways in infertile endometriosis, and explore the pathogenesis of infertile endometriosis. The findings of this study could aid in the diagnosis and treatment of infertile endometriosis patients.