Blood-based biomarker discovery through integrative transcriptomic and miRNA network analyses in schizophrenia, major depressive disorder, and bipolar disorder.

Schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BD) are highly prevalent psychiatric disorders characterized by substantial clinical overlap and biological heterogeneity, complicating diagnosis and therapeutic stratification. The identification of robust, minimally invasive biomarkers remains a critical unmet need. In this study, we applied an integrative systems biology framework to identify convergent and disorder specific blood and tissue based molecular signatures across SCZ, MDD, and BD. Disorder associated genes were systematically curated from multiple biomedical repositories and integrated with transcriptomic profiles from independent GEO datasets encompassing brain tissue, peripheral blood, and disease relevant cellular models. Comparative analyses revealed 4607 genes shared across all three disorders, of which 4348 were detectable in blood. These shared genes were significantly enriched for immune regulation, stress response, metabolic control, and neurotransmitter related pathways. Protein-protein interaction network analysis enabled the prioritization of key genes, with PFKFB3, ATF3, LGALS3, and TIMP1 emerging as central, biologically coherent nodes that were consistently validated across independent differential expression datasets and confirmed for blood level detectability using the Human Protein Atlas. Regulatory network reconstruction further identified disease relevant miRNA-gene interactions, with enrichment of blood expressed miRNAs highlighting the miR-17∼92 cluster and its paralogs as dominant post transcriptional regulators. Notably, the prioritized gene and miRNA signatures demonstrated enrichment across pathways associated with metabolic, cardiovascular, neurological, and oncological disorders, highlighting potential molecular overlap between psychiatric and systemic disorders. Collectively, these findings identify clinically accessible candidate gene and miRNA biomarkers with mechanistic relevance, supporting their prioritization for future experimental and clinical investigation.