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Updated: Aug 1, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Schizophrenia and cell senescence candidate genes screening, machine learning, diagnostic models, and drug prediction
Yu Feng1,2,3, Jing Shen1, Jin He1
1The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Suzhou, China.
Background:
Schizophrenia (SC) is one of the most common psychiatric diseases. Its potential pathogenic genes and effective treatment methods are still unclear. Cell senescence has been confirmed in mental diseases. A link exists between cellular senescence and immunity, and immune-related problems affect suicide rates in individuals suffering from schizophrenia. Therefore, the aims of this study were to identify candidate genes based on cell senescence that can affect the diagnosis and treatment of schizophrenia.
Methods:
Two data sets of schizophrenia were provided by the Gene Expression Omnibus (GEO) database, one was taken as training and the other as a validation group. The genes related to cell senescence were obtained from the CellAge database. DEGs were identified using the Limma package and weighted gene co-expression network analysis (WGCNA). The function enrichment analysis was conducted, followed by machine learning-based identification for least absolute shrinking and selection operators (LASSO) regression. Random Forest were used to identify candidate immune-related central genes and establish artificial neural networks for verification of the candidate genes. The receiver operating characteristic curve (ROC curve) was used for the diagnosis of schizophrenia. Immune cell infiltrates were constructed to study immune cell dysregulation in schizophrenia, and relevant drugs with candidate genes were collected from the DrugBank database.
Results:
Thirteen co-expression modules were screened for schizophrenia, of which 124 were the most relevant genes.There were 23 intersected genes of schizophrenia (including DEGs and the cellular senescence-related genes), and through machine learning six candidate genes were finally screened out. The diagnostic value was evaluated using the ROC curve data. Based on these results it was confirmed that these candidate genes have high diagnostic value.Two drugs related to candidate genes, Fostamatinib and Ritodine, were collected from the DrugBanks database.
Conclusion:
Six potential candidate genes (SFN, KDM5B, MYLK, IRF3, IRF7, and ID1) had been identified, all of which had diagnostic significance. Fostamatinib might be a drug choice for patients with schizophrenia to develop immune thrombocytopenic purpura (ITP) after treatment, providing effective evidence for the pathogenesis and drug treatment of schizophrenia.
Insights
This study identified six candidate genes linked to cell senescence that show diagnostic significance for schizophrenia. These findings offer potential new avenues for diagnosing and treating this complex psychiatric disorder.
Area of Science:
- Genetics
- Immunology
- Psychiatry
Background:
- Schizophrenia (SC) is a common psychiatric disorder with unclear genetic underpinnings and treatment options.
- Cellular senescence is implicated in mental diseases, with links to immunity and suicide risk in schizophrenia patients.
Purpose of the Study:
- To identify candidate genes associated with cellular senescence relevant to schizophrenia diagnosis and treatment.
- To explore the role of immune-related genes in schizophrenia pathogenesis.
Main Methods:
- Utilized Gene Expression Omnibus (GEO) datasets for schizophrenia (training and validation).
- Integrated cell senescence genes from CellAge database with differentially expressed genes (DEGs) from schizophrenia datasets.
- Employed weighted gene co-expression network analysis (WGCNA), LASSO regression, and Random Forest for gene identification and validation.
- Analyzed immune cell infiltrates and utilized Receiver Operating Characteristic (ROC) curves for diagnostic assessment.
Main Results:
- Identified six candidate genes (SFN, KDM5B, MYLK, IRF3, IRF7, ID1) with significant diagnostic value for schizophrenia.
- Confirmed high diagnostic accuracy using ROC curve analysis.
- Identified Fostamatinib and Ritodine as potential drugs targeting the candidate genes.
Conclusions:
- Six candidate genes demonstrate diagnostic significance for schizophrenia, potentially aiding in early detection.
- The findings provide evidence for the role of cell senescence and immune dysregulation in schizophrenia pathogenesis.
- Fostamatinib is highlighted as a potential therapeutic agent for schizophrenia, particularly in cases involving immune thrombocytopenic purpura.
