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<p>Patients with schizophrenia (SCZ) exhibit a significantly higher prevalence of metabolic syndrome (MetS), suggesting a potential biological link between the two conditions. However, the molecular mechanisms underlying this comorbidity remain unclear. This study aimed to identify shared molecular features between SCZ and MetS through integrated bioinformatics analyses. Peripheral blood transcriptomic datasets for SCZ (GSE38481) and MetS (GSE145412) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the limma package for SCZ and DESeq2 for MetS. Weighted gene co-expression network analysis (WGCNA) was performed to identify disease-related gene modules. Functional enrichment analysis of module genes was conducted using Metascape. Shared genes from disease-related modules were used to construct&#xa0;a protein–protein interaction (PPI) network via the STRING database, and hub genes were identified using the cytoHubba plugin in Cytoscape. Gene Set Enrichment Analysis (GSEA) was employed to explore the biological functions of the central hub gene in both disorders. Potential therapeutics were predicted using the Connectivity Map (CMap) and validated through molecular docking using CB-Dock2. Our analyses identified one SCZ-related and three MetS-related gene modules&#xa0;via WGCNA. A total of 48 intersecting genes were shared between the disease-related modules, which were primarily enriched in immune- and inflammation-related pathways. PPI network analysis revealed PGLYRP1 as a central hub gene, associated with immune dysregulation, metabolic abnormalities, and neurological dysfunction in both disorders. CMap analysis predicted several candidate compounds capable of reversing the PGLYRP1-centered comorbidity gene expression pattern, and subsequent molecular docking revealed that carbetocin demonstrated the highest binding affinity for PGLYRP1. In conclusion, immune and inflammatory processes are pivotal in the pathophysiology of SCZ–MetS comorbidity. PGLYRP1 may serve as a central molecular target for this comorbidity, and carbetocin shows promise as a candidate therapeutic, providing a theoretical basis for future experimental validation and potential clinical application.</p>

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Integrated Bioinformatics Analyses of Peripheral Blood Transcriptomes Reveals Shared Molecular Features Underlying the Comorbidity of Schizophrenia and Metabolic Syndrome

  • 期刊论文

  • 发表日期：

    2025年11月18日

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  • Yue Zhang,
  • Mengmeng Hao,
  • Yanwei Hu

摘要

Patients with schizophrenia (SCZ) exhibit a significantly higher prevalence of metabolic syndrome (MetS), suggesting a potential biological link between the two conditions. However, the molecular mechanisms underlying this comorbidity remain unclear. This study aimed to identify shared molecular features between SCZ and MetS through integrated bioinformatics analyses. Peripheral blood transcriptomic datasets for SCZ (GSE38481) and MetS (GSE145412) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the limma package for SCZ and DESeq2 for MetS. Weighted gene co-expression network analysis (WGCNA) was performed to identify disease-related gene modules. Functional enrichment analysis of module genes was conducted using Metascape. Shared genes from disease-related modules were used to construct a protein–protein interaction (PPI) network via the STRING database, and hub genes were identified using the cytoHubba plugin in Cytoscape. Gene Set Enrichment Analysis (GSEA) was employed to explore the biological functions of the central hub gene in both disorders. Potential therapeutics were predicted using the Connectivity Map (CMap) and validated through molecular docking using CB-Dock2. Our analyses identified one SCZ-related and three MetS-related gene modules via WGCNA. A total of 48 intersecting genes were shared between the disease-related modules, which were primarily enriched in immune- and inflammation-related pathways. PPI network analysis revealed PGLYRP1 as a central hub gene, associated with immune dysregulation, metabolic abnormalities, and neurological dysfunction in both disorders. CMap analysis predicted several candidate compounds capable of reversing the PGLYRP1-centered comorbidity gene expression pattern, and subsequent molecular docking revealed that carbetocin demonstrated the highest binding affinity for PGLYRP1. In conclusion, immune and inflammatory processes are pivotal in the pathophysiology of SCZ–MetS comorbidity. PGLYRP1 may serve as a central molecular target for this comorbidity, and carbetocin shows promise as a candidate therapeutic, providing a theoretical basis for future experimental validation and potential clinical application.