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

Xiaoli Zhu; Chuan-Lan Wang; Jian-Feng Yu; Jianjun Weng; Bing Han; Yanqing Liu; Xiaowei Tang; Bo Pan

doi:10.3389/fncel.2023.1256184

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

Front Cell Neurosci. 2023 Sep 28:17:1256184. doi: 10.3389/fncel.2023.1256184. eCollection 2023.

Authors

Xiaoli Zhu^{1

2}, Chuan-Lan Wang³, Jian-Feng Yu³, Jianjun Weng^{1

2}, Bing Han^{1

2}, Yanqing Liu^{1

2}, Xiaowei Tang⁴, Bo Pan^{1

2}

Affiliations

¹ The Key Laboratory of Syndrome Differentiation and Treatment of Gastric Cancer of the State Administration of Traditional Chinese Medicine, Yangzhou University Medical College, Yangzhou, China.
² Institute of Translational Medicine, Yangzhou University Medical College, Yangzhou, China.
³ Tongzhou District Hospital of TCM, Nantong, China.
⁴ Department of Psychiatry, Affiliated WuTaiShan Hospital of Yangzhou University Medical College, Yangzhou, China.

Abstract

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.

Keywords: CIBERSORT; CLIC3; LASSO; WGCNA; peripheral immune-related biomarkers; random forest; schizophrenia; support vector machine.