Telomere-related genes as potential biomarkers to predict endometriosis and immune response: Development of a machine learning-based risk model - Zhang, He; Kong, Weimin; Xie, Yunkai; Zhao, Xiaoling; Luo, Dan; Chen, Shuning; Pan, Zhendong (Frontiers in Medicine, 2023)
Background: Endometriosis (EM) is an aggressive, pleomorphic, and common gynecological disease. The clinical manifestations include abnormal menstruation, dysmenorrhea, and infertility, which seriously affect the quality of life of women. The underlying pathogenesis of EM and the associated regulatory genes are unknown. Methods: We obtained telomere-related genes (TRGs) from TelNet. RNA-seq data of EM patients were obtained from three datasets, GSE5108, GSE23339, and GSE25628, in the GEO database, and a random forest (RF) approach was used to identify telomere signature genes and build nomogram prediction models. GO, KEGG, and GSEA enrichment analyses 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 the treatment of endometriosis. Results: The results showed that a total of 15 genes were screened as EM-telomere difference genes. After further screening by machine learning, six genes were obtained as characteristic predictive genes for EM. The results of immuno-infiltration analysis of the telomeric genes showed that the expression of macrophages, natural killer cells, etc. was significantly higher in cluster A. Further enrichment analysis results showed that the differential genes were mainly enriched in biological pathways such as cell cycle, extracellular matrix, etc. Finally, the cmap database was used to screen 11 potential drugs for the treatment of EM. Conclusions: Telomere-related genes play a crucial role in the development of endometriosis, being associated with immune infiltration and acting on multiple pathways, including the cell cycle. Telomere signature genes can be valuable predictive markers for endometriosis.