ArticleBMC psychiatry2025
The role of senescence-related genes in major depressive disorder: insights from machine learning and single cell analysis.
Article in BMC psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Article
- Cellular heterogeneity and multicellular mechanisms in the pathogenesis of late-life depression: insights from single-cell and spatial multi-omics.Frontiers in immunology · 2026Review
- Canonical and non-canonical roles of oligodendrocyte precursor cells in mental disorders.Npj mental health research · 2025Review
- A network-based approach to discover diagnostic metabolite markers associated with depressive features for major depressive disorder.Frontiers in psychiatry · 2025Article
- AI-based prediction of drug-gene interactions modulating tight junction integrity: A deep learning framework highlighting multiple therapeutic targets.Journal of oral biology and craniofacial researchArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundEvidence 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.
methodsWe 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 used to analyze MDD SRGs functions.
resultsThe five hub SRGs: ALOX15B, TNFSF13, MARCH 15, UBTD1, and MAPK14 showed differential expression between MDD patients and controls. Diagnostics models based on these hub genes demonstrated high accuracy. The hub SRGs correlated positively with neutrophils and negatively with T lymphocytes. SRGs expression pattern revealed two distinct MDD subclusters. WGCNA identified significant gene modules within these subclusters. Additionally, individual endothelial cells with high senescence scores were found to interact with astrocytes via the Notch signaling pathway, suggesting a specific role in MDD pathogenesis.
conclusionThis comprehensive study elucidates the significant role of SRGs in MDD, highlighting the importance of the Notch signaling pathway in mediating senescence effects.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.