Data_Sheet_6_Identification of immune-related biomarkers in peripheral blood of schizophrenia using bioinformatic methods and machine learning algorithms.XLSX

Schizophrenia is a group of severe neurodevelopmental disorders. Identification of peripheral diagnostic biomarkers is an effective approach to improving diagnosis of schizophrenia. In this study, four datasets of schizophrenia patients’ blood or serum samples were downloaded from the GEO database and merged and de-batched for the analyses of differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WCGNA). The WGCNA analysis showed that the cyan module, among 9 modules, was significantly related to schizophrenia, which subsequently yielded 317 schizophrenia-related key genes by comparing with the DEGs. The enrichment analyses on these key genes indicated a strong correlation with immune-related processes. The CIBERSORT algorithm was adopted to analyze immune cell infiltration, which revealed differences in eosinophils, M0 macrophages, resting mast cells, and gamma delta T cells. Furthermore, by comparing with the immune genes obtained from online databases, 95 immune-related key genes for schizophrenia were screened out. Moreover, machine learning algorithms including Random Forest, LASSO, and SVM-RFE were used to further screen immune-related hub genes of schizophrenia. Finally, CLIC3 was found as an immune-related hub gene of schizophrenia by the three machine learning algorithms. A schizophrenia rat model was established to validate CLIC3 expression and found that CLIC3 levels were reduced in the model rat plasma and brains in a brain-regional dependent manner, but can be reversed by an antipsychotic drug risperidone. In conclusion, using various bioinformatic and biological methods, this study found an immune-related hub gene of schizophrenia – CLIC3 that might be a potential diagnostic biomarker and therapeutic target for schizophrenia.

精神分裂症（Schizophrenia）是一组严重的神经发育性障碍。鉴定外周血诊断生物标志物是改善精神分裂症诊断的有效途径。本研究从GEO数据库（Gene Expression Omnibus）下载了4组精神分裂症患者血液或血清样本数据集，并进行合并与批次效应校正，以开展差异表达基因（differentially expressed genes, DEGs）分析及加权基因共表达网络分析（weighted gene co-expression network analysis, WGCNA）。WGCNA分析显示，在9个基因模块中，青色模块与精神分裂症显著相关；随后通过与DEGs比对，共获得317个精神分裂症相关关键基因。对上述关键基因的富集分析结果表明，其与免疫相关过程存在显著关联。本研究采用CIBERSORT算法分析免疫细胞浸润情况，结果显示嗜酸性粒细胞、M0巨噬细胞、静息肥大细胞及γδ T细胞的浸润水平存在显著差异。此外，通过与在线数据库获取的免疫基因进行比对，本研究筛选得到95个精神分裂症相关免疫关键基因。进一步采用随机森林（Random Forest）、LASSO及SVM-RFE（支持向量机递归特征消除）等机器学习算法，筛选精神分裂症免疫相关核心基因。最终，通过上述三种机器学习算法，确定CLIC3（氯细胞内通道3）为精神分裂症免疫相关核心基因。本研究构建精神分裂症大鼠模型以验证CLIC3的表达水平，结果显示模型大鼠血浆及脑组织中CLIC3的表达呈脑区域依赖性降低，而抗精神病药物利培酮（risperidone）可逆转这一表达变化。综上，本研究通过多种生物信息学及生物学实验方法，发现了一个精神分裂症免疫相关核心基因——CLIC3，其有望成为精神分裂症潜在的诊断生物标志物及治疗靶点。