Gene expression analysis signifies the association of inflammatory proteins with the development of endometriosis

Telomere-related genes as potential biomarkers to predict endometriosis and immune response: Development of a machine learning-based risk model

Introduction

Endometriosis (EM) is an aggressive, pleomorphic, and common gynecological disease. Its clinical presentation includes abnormal menstruation, dysmenorrhea, and infertility, which seriously affect the patient's quality of life. However, the pathogenesis underlying EM and associated regulatory genes are unknown.

Methods

Telomere-related genes (TRGs) were uploaded from TelNet. RNA-sequencing (RNA-seq) data of EM patients were obtained from three datasets (GSE5108, GSE23339, and GSE25628) in the GEO database, and a random forest approach was used to identify telomere signature genes and build nomogram prediction models. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis were used to identify the pathways involved in the action of the signature genes. Finally, the CAMP database was used to screen drugs for potential use in EM treatment.

Results

Fifteen total genes were screened as EM–telomere differentially expressed genes. Further screening by machine learning obtained six genes as characteristic predictive of EM. Immuno-infiltration analysis of the telomeric genes showed that expressions including macrophages and natural killer cells were significantly higher in cluster A. Further enrichment analysis showed that the differential genes were mainly enriched in biological pathways like cell cycle and extracellular matrix. Finally, the Connective Map database was used to screen 11 potential drugs for EM treatment.

Discussion

TRGs play a crucial role in EM development, and are associated with immune infiltration and act on multiple pathways, including the cell cycle. Telomere signature genes can be valuable predictive markers for EM.

Bioinformatics Analysis Identifies Molecular Markers Regulating Development and Progression of Endometriosis and Potential Therapeutic Drugs

Endometriosis, a common disease that presents as polymorphism, invasiveness, and extensiveness, with clinical manifestations including dysmenorrhea, infertility, and menstrual abnormalities, seriously affects quality of life in women. To date, its underlying etiological mechanism of action and the associated regulatory genes remain unclear. This study aimed to identify molecular markers and elucidate mechanisms underlying the development and progression of endometriosis. Specifically, we downloaded five microarray expression datasets, namely, GSE11691, GSE23339, GSE25628, GSE7305, and GSE105764, from the Gene Expression Omnibus (GEO) database. These datasets, obtained from endometriosis tissues, alongside normal controls, were subjected to in-depth bioinformatics analysis for identification of differentially expressed genes (DEGs), followed by analysis of their function and pathways via gene ontology (GO) and KEGG pathway enrichment analyses. Moreover, we constructed a protein–protein interaction (PPI) network to explore the hub genes and modules, and then applied machine learning algorithms support vector machine-recursive feature elimination and least absolute shrinkage and selection operator (LASSO) analysis to identify key genes. Furthermore, we adopted the CIBERSORTx algorithm to estimate levels of immune cell infiltration while the connective map (CMAP) database was used to identify potential therapeutic drugs in endometriosis. As a result, a total of 423 DEGs, namely, 233 and 190 upregulated and downregulated, were identified. On the other hand, a total of 1,733 PPIs were obtained from the PPI network. The DEGs were mainly enriched in immune-related mechanisms. Furthermore, machine learning and LASSO algorithms identified three key genes, namely, apelin receptor (APLNR), C–C motif chemokine ligand 21 (CCL21), and Fc fragment of IgG receptor IIa (FCGR2A). Furthermore, 16 small molecular compounds associated with endometriosis treatment were identified, and their mechanism of action was also revealed. Taken together, the findings of this study provide new insights into the molecular factors regulating occurrence and progression of endometriosis and its underlying mechanism of action. The identified therapeutic drugs and molecular markers may have clinical significance in early diagnosis of endometriosis.