{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Lian K"],"funding":["Yunnan Clinical Research Center for Mental Disorders","Kunming University of Science and Technology"],"pagination":["188"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11874787"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["25(1)"],"pubmed_abstract":["<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 use"],"journal":["BMC psychiatry"],"pubmed_title":["The role of senescence-related genes in major depressive disorder: insights from machine learning and single cell analysis."],"pmcid":["PMC11874787"],"funding_grant_id":["KUST-YX2022002","202102AA100058"],"pubmed_authors":["Lian K","Yang W","Chen Y","Zhang L","Xu X","Ye J"],"additional_accession":[]},"is_claimable":false,"name":"The role of senescence-related genes in major depressive disorder: insights from machine learning and single cell analysis.","description":"<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 use","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Mar","modification":"2025-04-04T02:45:45.216Z","creation":"2025-04-04T02:45:45.216Z"},"accession":"S-EPMC11874787","cross_references":{"pubmed":["40033248"],"doi":["10.1186/s12888-025-06542-8"]}}