ArticleJournal of molecular neuroscience : MN2023
Identification of Immune-Linked Hub Genes and Diagnostic Model Construction in Schizophrenia.
Article in Journal of molecular neuroscience : MN, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 9 citations in OpenAlex.
- Machine learning-based integration identifies a 10-gene predictive signature and its classification patterns in schizophrenia.European archives of psychiatry and clinical neuroscience · 2026Article
- Machine learning-based predictive models and subtypes patterns in peripheral blood of schizophrenia based on a machine learning computational framework.Schizophrenia (Heidelberg, Germany) · 2026Article
- Article
- Aberrant Expression of TH2LCRR and GATA3 in Peripheral Blood Mononuclear Cells of Patients with Acute-Phase Schizophrenia: Integrative Bioinformatics Analysis and Experimental Study.Molecular neurobiology · 2025Article
- Identification of Immune-Related Biomarkers of Schizophrenia in the Central Nervous System Using Bioinformatic Methods and Machine Learning Algorithms.Molecular neurobiology · 2025Article
- Comprehensive bioinformatics analysis identifies hub genes associated with immune cell infiltration in early-onset schizophrenia.BMC psychiatry · 2025Article
- Genetic association between gene expression profiles in oligodendrocyte precursor cells and psychiatric disorders.Frontiers in psychiatry · 2025Article
- Shared Genetic Architecture Among Gastrointestinal Diseases, Schizophrenia, and Brain Subcortical Volumes.Schizophrenia bulletin · 2024Article
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
Abstract
Schizophrenia (SCZ) is a prevalent, severe, and persistent mental disorder with an unknown etiology. Growing evidence indicates that immunological dysfunction is vital in the development of SCZ. Our study aims to uncover potential immune-linked hub genes and immune infiltration characteristics of SCZ, as well as to develop a diagnostic model based on immune-linked central genes. GSE38484 and GSE54913 chip expression data for patients with SCZ and healthy controls were retrieved. Weighted gene co-expression network analysis (WGCNA) was performed to identify major module genes and critical immune-linked genes. Functional enrichment analysis was conducted to elucidate the involvement of key genes in the immunological response to SCZ, along with the examination of their protein interactions. Moreover, 202 peripheral blood samples were examined using the single-sample gene set enrichment analysis (ssGSEA) method to detect distinct immune cell types. Hub immune-linked genes in SCZ were identified using the minimal absolute contraction and selection operator analysis. Receptor profiles of central immune-linked genes were analyzed to distinguish the two groups. Finally, the association between immune-linked hub genes and various types of immune cells was assessed. Our findings revealed ten immune cell types and nine key genes involved in SCZ, including effector memory CD4+ T cells, activated CD8+ T cells, mast cells, naive CD8+ T cells, PBMC, type 17 helper cells (Th17), central memory CD8+ T cells, CD56 bright NK cells, memory B cells, and regulatory T cells. Diagnostic models constructed using LASSO regression exhibited an average area under the curve (AUC) of 0.866. Our results indicate immunological dysfunction as a factor in the development of SCZ. ASGR2, ADRM1, AHANK, S100A8, FUCA1, AKNA, GATA3, AHCYL2, and PTRH2 are the key regulatory genes of immune cells, highlighting their potential as novel therapeutic targets for SCZ.
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Registered trials
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