Establishment of a novel glycolysis-immune-related diagnosis gene signature for endometriosis by machine learning.

ABSTRACT:

Purpose

The objective of this study was to investigate the key glycolysis-related genes linked to immune cell infiltration in endometriosis and to develop a new endometriosis (EMS) predictive model.

Methods

A training set and a test set were created from the Gene Expression Omnibus (GEO) public database. We identified five glycolysis-related genes using least absolute shrinkage and selection operator (LASSO) regression and the random forest method. Then, we developed and tested a prediction model for EMS diagnosis. The CIBERSORT method was used to compare the infiltration of 22 different immune cells. We examined the relationship between key glycolysis-related genes and immune factors in the eutopic endometrium of women with endometriosis. In addition, Gene Ontology (GO)-bas

SUBMITTER: Chen Q 

PROVIDER: S-EPMC10239430 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature