Schizophrenia and cell senescence candidate genes screening, machine learning, diagnostic models, and drug prediction

Yu Feng; Jing Shen; Jin He; Minyan Lu

doi:10.3389/fpsyt.2023.1105987

Schizophrenia and cell senescence candidate genes screening, machine learning, diagnostic models, and drug prediction

Front Psychiatry. 2023 Apr 11:14:1105987. doi: 10.3389/fpsyt.2023.1105987. eCollection 2023.

Authors

Yu Feng^{1

2

3}, Jing Shen¹, Jin He¹, Minyan Lu¹

Affiliations

¹ The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Suzhou, China.
² The University of New South Wales, Kensington, NSW, Australia.
³ The University of Melbourne, Parkville, VIC, Australia.

Abstract

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.

Keywords: cell senescence; diagnostic model; drug prediction; immune infiltration; machine learning; schizophrenia.