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ORIGINAL RESEARCH article
Front. Psychiatry
Sec. Schizophrenia
Volume 15 - 2024 |
doi: 10.3389/fpsyt.2024.1429437
Analyses of single-cell and bulk RNA sequencing combined with machine learning reveal the expression patterns of disrupted mitophagy in Schizophrenia
Provisionally accepted- 1 The Second Affiliated Hospital of Kunming Medical University, Kunming, China
- 2 The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan Province, China
Background: Mitochondrial dysfunction is an important factor in the pathogenesis of schizophrenia. However, the relationship between mitophagy and schizophrenia remains to be elucidated.Single-cell RNA sequencing datasets of peripheral blood and brain organoids from SCZ patients and healthy controls were retrieved. Mitophagy-related genes that were differentially expressed between the two groups were screened. The diagnostic model based on hub mitophagy genes was constructed using two machine learning methods, and the relationship between mitophagy and immune cells was analyzed. Single-cell RNA sequencing data of brain organoids was used to calculate the mitophagy score (Mitoscore).Results: We found 7 hub mitophagy genes to construct a diagnostic model. The mitophagy genes were related to the infiltration of neutrophils, activated dendritic cells, resting NK cells, regulatory T cells, resting memory T cells, and CD8 T cells. In addition, we identified 12 cell clusters based on the Mitoscore, and the most abundant neurons were further divided into three subgroups. Results at the single-cell level showed that Mitohigh_Neuron established a novel interaction with endothelial cells via SPP1 signaling pathway, suggesting their distinct roles in SCZ pathogenesis.We identified a mitophagy signature for schizophrenia that provides new insights into disease pathogenesis and new possibilities for its diagnosis and treatment.
Keywords: Schizophrenia, mitophagy, Bulk RNA analysis, Single-cell RNA analysis, machine learning
Received: 08 May 2024; Accepted: 29 Aug 2024.
Copyright: © 2024 Lian, Yang, Ye, Chng and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Xiufeng Xu, The First Affiliated Hospital of Kunming Medical University, Kunming, 650032, Yunnan Province, China
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