Identification of key genes and pathways in schizophrenia: a bioinformatics analysis based on GWAS and GEO

Alireza Shokrgozar; Maryam Rahimi; Shahrzad Shoraka

doi:10.3389/fpsyt.2025.1464676

Identification of key genes and pathways in schizophrenia: a bioinformatics analysis based on GWAS and GEO

Front Psychiatry. 2025 Apr 7:16:1464676. doi: 10.3389/fpsyt.2025.1464676. eCollection 2025.

Authors

Alireza Shokrgozar¹, Maryam Rahimi², Shahrzad Shoraka³

Affiliations

¹ Department of Psychology, Islamic Azad University, Karaj, Iran.
² Clinical Care and Health Promotion Research Center, Islamic Azad University, Karaj, Iran.
³ Department of Microbiology and Microbial Biotechnology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran.

Abstract

Introduction: Schizophrenia is a mental illness that is associated with many disorders, such as incoherence of mental activities, and impairment of perception, thinking, emotions, and behavior. Although the exact cause of schizophrenia is unknown, many studies have highlighted the role of genetic background and environmental factors in this disease. Therefore, the identification of key genes involved in schizophrenia provides a promising opportunity to develop novel diagnosis and/or treatment methods. This study aims to investigate schizophrenia-related hub genes by bioinformatics analysis based on genome-wide association (GWAS) and gene expression omnibus (GEO) datasets.

Material and methods: In this study, the GWAS catalog and GEO dataset were used to identify key candidate genes and pathways that are important in the diagnosis and treatment of schizophrenia, and then the results were analyzed using Enrichr and Cytoscape tools.

Result: According to our result NRXN, CACNA1C, and GRIN2A genes had the highest scores in the GWAS analyses and BRCA1, ATM, and STAT1 genes had the highest scores in the GEO dataset. Also, glucuronidation, ascorbate, and aldarate metabolism pathways in the GWAS, PI3K/AKT and Rap1 signaling in the GEO had the highest associations with schizophrenia.

Conclusion: This study highlights the need for further validation of the genes and molecular pathways in schizophrenia. Also, the identified genes could be promising candidates for future diagnostic and/or treatment strategies for schizophrenia.

Keywords: GEO; GWAS; bioinformatics analysis; hub gene analysis; key pathway and genes; schizophrenia.