昆明医科大学学报2025，Vol.46Issue(1)：23-35,13.DOI:10.12259/j.issn.2095-610X.S20250104
综合生物信息学方法识别精神分裂症状中关键线粒体自噬基因
Identify Key Mitochondrial Autophagy Genes in Schizophrenia through Integrated Bioinformatics Approaches
摘要
Abstract
Objective To utilize single-cell and peripheral blood transcriptomic data from 3D brain organoids,combined with machine learning,to analyze the role of mitochondrial autophagy genes in schizophrenia(SCZ).Methods By integrating two machine learning algorithms,we identified differentially expressed mitochondrial autophagy-related genes between schizophrenia patients and healthy controls using peripheral blood RNA sequencing data.The relationship between mitophagy gene,immune cells and inflammatory factors was further explored.Comprehensive single-cell analysis was used to explore the signaling pathways and specific transcription factors based on mitophagy genes.Results Using machine learning,seven key mitophagy genes expressed in schizophrenia patients were identified.Based on Mitoscore analysis,at the single-cell level,neurons with high mitochondrial autophagy activity(Mitohigh_Neuron)formed new interactions with endothelial cells via the SPP1 signaling pathway.Conclusion This study identified two subtypes of mitophagy and seven key mitophagy genes in schizophrenia,providing new insights into the pathogenesis of the disease.关键词
精神分裂症/线粒体自噬/神经元/单细胞测序/机器学习Key words
Schizophrenia/Mitochondrial autophagy/Neurons/Single-cell sequencing/Machine learning分类
医药卫生引用本文复制引用
廉坤,李咏梅,施诚龙,陈怡兰,张磊,杨薇,许秀峰..综合生物信息学方法识别精神分裂症状中关键线粒体自噬基因[J].昆明医科大学学报,2025,46(1):23-35,13.基金项目
云南省精神心理疾病临床医学研究中心(202102AA100058) （202102AA100058）
昆明理工大学医学联合专项(KUST-YX2022002) （KUST-YX2022002）
