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

ABSTRACT:

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 o

SUBMITTER: Zhang H 

PROVIDER: S-EPMC10034389 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature