biostudies-literatureLian KYunnan Clinical Research Center for Mental DisordersKunming University of Science and Technology188https://www.ebi.ac.uk/biostudies/studies/S-EPMC11874787biostudies-literatureUnknown25(1)<h4>Background</h4>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.<h4>Methods</h4>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 useBMC psychiatryThe role of senescence-related genes in major depressive disorder: insights from machine learning and single cell analysis.PMC11874787KUST-YX2022002202102AA100058Lian KYang WChen YZhang LXu XYe JfalseThe role of senescence-related genes in major depressive disorder: insights from machine learning and single cell analysis.<h4>Background</h4>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.<h4>Methods</h4>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 use2025-01-01T00:00:00Z2025 Mar2025-04-04T02:45:45.216Z2025-04-04T02:45:45.216ZS-EPMC118747874003324810.1186/s12888-025-06542-8