The role of senescence-related genes in major depressive disorder: insights from machine learning and single cell analysis.

ABSTRACT:

Background

Evidence indicates that patients with Major Depressive Disorder (MDD) exhibit a senescence phenotype or an increased susceptibility to premature senescence. However, the relationship between senescence-related genes (SRGs) and MDD remains underexplored.

Methods

We analyzed 144 MDD samples and 72 healthy controls from the GEO database to compare SRGs expression. Using Random Forest (RF) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), we identified five hub SRGs to construct a logistic regression model. Consensus cluster analysis, based on SRGs expression patterns, identified subclusters of MDD patients. Weighted Gene Co-expression Network Analysis (WGCNA) identified gene modules strongly linked to each cluster. Single-cell RNA sequencing was use

SUBMITTER: Lian K 

PROVIDER: S-EPMC11874787 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature