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BACKGROUND: Schizophrenia (SCZ) is a neuropsychiatric disorder that is not yet fully understood, characterized by behavioral, emotional, and cognitive dysfunctions. In this study, we analyzed publicly available gene-expression data to identify SCZ-associated genes and pathways, offering deeper insights into its biological basis. METHODS: The data collection aimed to retrieve gene expression databases featuring samples from the brain's prefrontal cortex and blood. After performing exclusion criteria and quality checks, 17 datasets were retrieved from six different sources: Brain biopsy (n = 6), whole blood (n = 2), peripheral blood mononuclear cells (n = 2), leukocytes (n = 1), lymphocytes (n = 1) and isolated neurons (n = 5). We used four brain datasets as the discovery set for the initial analysis. Differentially expressed genes (DEGs) were identified by comparing SCZ patients with controls and were subsequently used in enrichment analysis. Finally, we applied feature selection to pinpoint the most informative DEGs for SCZ and evaluated their accuracy using data from other tissues. RESULTS: This analytical approach identified 532 DEGs. Feature selection revealed three genes-HUWE1, PTGDS, and RPL31-that effectively discriminated SCZ from control samples. The 3-gene model's performance was validated in other datasets, presenting accuracy (> 72%) in brain tissue, whole blood, PMBCs and leukocytes. Furthermore, the enrichment analysis revealed a potential linkage with neurodegenerative biological pathways. CONCLUSION: These insights open new avenues for exploring key genes for SCZ, which can lead to new therapeutic targets or tools for diagnosis, potentially transforming the management of SCZ and enhancing patient care.
Rebouças et al. (Sat,) studied this question.