SynthesisTranslational psychiatry2026
Identification of suicide brain transcriptomic signatures using meta-analysis of multiple cohorts.
Synthesis in Translational psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
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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
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Gene expression meta-analysis in the prefrontal cortex: unraveling biological underpinnings of suicidal risk.BMC psychiatry · 2026Pooled it
- In the Search for Suicide Signatures, Phenotype Matters.Translational psychiatry · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Suicide remains a critical global public health issue, accounting for nearly one million deaths annually and imposing profound societal and economic burdens. Despite its urgency, the lack of diagnostic and predictive biomarkers continues to hinder the development of effective prevention and treatment strategies. This study presents a comprehensive meta-analysis that integrates publicly available postmortem brain transcriptomic datasets and a domestic cohort, encompassing 16 cohorts. The transcriptomic data, sourced from the Gene Expression Omnibus repository, were generated using various techniques, including traditional RNA sequencing, microarray methods, and single-cell RNA sequencing. Differential expression analyses were performed across multiple brain regions, with meta-analyses stratified by cortical regions, the dorsolateral prefrontal cortex (DLPFC), and combined. We further analyzed whether covariates may affect the identified genes. Three meta-analytic approaches were employed, complemented by pathway and cell-set enrichment analyses. The unadjusted meta-analysis consistently identified several genes with altered expression, including upregulated P2RY12, CX3CR1, and GPR34, and downregulated SOX9 and PMP2, all at nominal significance. Additionally, multiple genes encoding long non-coding RNAs (lncRNAs) exhibited nominally altered expression in suicide, including RP5-837J1.4, AC159540.14, DNM1P47, AC004158.2, EEF1A1P30, and RP11-339B21.8. Several alternative strategies to run meta-analysis were performed and moderators were investigated. Cell-type-specific expression deconvolution and meta-analysis identified several genes overlapping with bulk expression meta-analysis, and genes were attributed to neuronal lineages. These findings highlight plausible molecular targets for future validation studies, suggesting the involvement of microglia (P2RY12 and CX3CR1), astrocytes (SOX9), immune responses (GPR34), myelin regulation (PMP2), and epigenetic modulation via lncRNAs. This research advances the understanding of the molecular architecture of suicide and provides a foundation for future studies focused on targeted prevention and therapeutic interventions.
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