Abstract

<i>Background: </i>Schizophrenia (SZ) is a common and severe mental disease. However, its etiology and pathogenesis have not been fully established. In this study, bioinformatics was used to identify SZ-related genes and reveal the potential mechanisms of them. <i>Methods: </i>Gene expression profiles were obtained from the GSE46509 dataset. Differentially expressed genes (DEGs) were analyzed by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment databases. A protein-protein interaction (PPI) network was established. TargetScan and miRGen, which are based on bioinformatics algorithms, were used to predict potential candidate target miRNAs and transcription factors. <i>Results: </i>Compared to healthy people controls, a total of 1422 DEGs were identified in SZ patient samples. Functional enrichment analysis revealed that these DEGs were significantly enriched in RNA processing, mRNA binding, and cell adhesion molecules. In addition, in the PPI network, SOCS3, FBXO9, ASB17, FBXO10, and ASB4 were identified as hub genes. In the predicted TF-miRNA-mRNA targeting regulatory network, hsa-miR-4514 was up-regulated by the highly expressed transcription factor (TF) NRF1, which down-regulated multiple hubs genes such as SOCS3, FBXO9, and FBXO10. <i>Conclusions: </i>Several potential biomarkers involved in SZ development were identified by bioinformatics analyses. Furthermore, our findings revealed the underpinning mechanisms of these potential biomarkers in the pathogenesis of SZ. And these results suggest a potential application value in clinical practice.

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