Identification of glucocorticoid-related genes in systemic lupus erythematosus using bioinformatics analysis and machine learning.

ABSTRACT:

Background

Systemic lupus erythematosus (SLE) is a complex autoimmune disease that has significant impacts on patients' quality of life and poses a substantial economic burden on society.

Objective

This study aimed to elucidate the molecular mechanisms underlying SLE by analyzing glucocorticoid-related genes (GRGs) expression profiles.

Methods

We examined the expression profiles of GRGs in SLE and performed consensus clustering analysis to identify stable patient clusters. We also identified differentially expressed genes (DEGs) within the clusters and between SLE patients and healthy controls. We conducted Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) to investigate biological functional differences, and we also conducted CIBERSORTx to es

SUBMITTER: Ren Y 

PROVIDER: S-EPMC11936220 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature