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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.