Analysis of characteristic genes and ceRNA regulation mechanism of endometriosis based on full transcriptional sequencing - Xie, Chengmao; Yin, Ziran; Liu, Yong (Frontiers in Genetics, 2022)
Background: Endometriosis is a common gynecological disorder that usually causes infertility, pelvic pain and ovarian masses. This study aimed to mine the characteristic genes of endometriosis, explore the regulatory mechanism and potential therapeutic drugs based on whole transcriptome sequencing data and resource from public databases, providing a theoretical basis for the diagnosis and treatment of endometriosis. Methods: Differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) were used to identify the endometriosis-related differentially expressed genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted by ‘clusterProfiler’ R package. Then, characteristic genes for endometriosis were identified by the least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) algorithm. The expression of characteristic genes was verified by quantitative reverse transcription polymerase chain reaction (qRT-PCR). The receiver operating characteristic (ROC) curve was used to evaluate the discriminatory ability of characteristic genes. We assessed the abundance of infiltrating immune cells in each sample using MCP-counter and ImmuCellAI algorithms. The competitive endogenous RNA (ceRNA) regulatory network of characteristic genes was created by Cytoscape and potential targeting drugs were obtained in the CTD database. Results: 44 endometriosis-related differentially expressed genes were obtained from GSE25628 and the own dataset. Subsequently, LASSO and SVM-RFE algorithms identified 4 characteristic genes, namely ACLY, PTGFR, ADH1B, and MYOM1. The results of RT-PCR were consistent with those of sequencing. The result of ROC curves indicated that the characteristic genes had powerful ability in distinguishing EC samples from EU samples. Infiltrating immune cells analysis suggested that there was a certain difference in immune microenvironment between EC and EU samples. The characteristic genes were significantly correlated with specific differential immune cells between EC and EU samples. Then, a ceRNA regulatory network of characteristic genes containing 111 nodes and 150 edges was constructed. Finally, a gene-compound network containing 274 nodes and 115 edges was generated. Conclusion: Comprehensive bioinformatic analysis was used to identify characteristic genes, and explore ceRNA regulatory network and potential therapeutic agents for endometriosis. Altogether, these findings provide new insights into the diagnosis and treatment of endometriosis.