Feng Yu, Shen Jing
The University of New South Wales, Kensington, NSW, Australia.
The University of Melbourne, Parkville, VIC, Australia.
Front Mol Neurosci. 2023 Mar 13;16:1123708. doi: 10.3389/fnmol.2023.1123708. eCollection 2023.
Schizophrenia (SC) is one of the most common mental illnesses. However, the underlying genes that cause it and its effective treatments are unknown. Programmed cell death (PCD) is associated with many immune diseases and plays an important role in schizophrenia, which may be a diagnostic indicator of the disease.
Two groups as training and validation groups were chosen for schizophrenia datasets from the Gene Expression Omnibus Database (GEO). Furthermore, the PCD-related genes of the 12 patterns were extracted from databases such as KEGG. Limma analysis was performed for differentially expressed genes (DEG) identification and functional enrichment analysis. Machine learning was employed to identify minimum absolute contractions and select operator (LASSO) regression to determine candidate immune-related center genes, construct protein-protein interaction networks (PPI), establish artificial neural networks (ANN), and validate with consensus clustering (CC) analysis, then Receiver operating characteristic curve (ROC curve) was drawn for diagnosis of schizophrenia. Immune cell infiltration was developed to investigate immune cell dysregulation in schizophrenia, and finally, related drugs with candidate genes were collected the Network analyst online platform.
In schizophrenia, 263 genes were crossed between DEG and PCD-related genes, and machine learning was used to select 42 candidate genes. Ten genes with the most significant differences were selected to establish a diagnostic prediction model by differential expression profiling. It was validated using artificial neural networks (ANN) and consensus clustering (CC), while ROC curves were plotted to assess diagnostic value. According to the findings, the predictive model had a high diagnostic value. Immune infiltration analysis revealed significant differences in Cytotoxic and NK cells in schizophrenia patients. Six candidate gene-related drugs were collected from the Network analyst online platform.
Our study systematically discovered 10 candidate hub genes (, , , , , , , , , and ). A good diagnostic prediction model was obtained through comprehensive analysis in the training (AUC 0.91, CI 0.95-0.86) and validation group (AUC 0.94, CI 1.00-0.85). Furthermore, drugs that may be useful in the treatment of schizophrenia have been obtained (Valproic Acid, Epigallocatechin gallate).
精神分裂症（SC）是最常见的精神疾病之一。然而，导致该病的潜在基因及其有效治疗方法尚不清楚。程序性细胞死亡（PCD）与许多免疫疾病相关，在精神分裂症中起重要作用，可能是该疾病的诊断指标。
从基因表达综合数据库（GEO）中选择两组作为训练组和验证组的精神分裂症数据集。此外，从KEGG等数据库中提取12种模式的PCD相关基因。进行Limma分析以鉴定差异表达基因（DEG）并进行功能富集分析。采用机器学习来识别最小绝对收缩并选择算子（LASSO）回归以确定候选免疫相关中心基因，构建蛋白质 - 蛋白质相互作用网络（PPI），建立人工神经网络（ANN），并通过一致性聚类（CC）分析进行验证，然后绘制受试者工作特征曲线（ROC曲线）用于精神分裂症的诊断。开展免疫细胞浸润研究以调查精神分裂症中的免疫细胞失调情况，最后在网络分析在线平台上收集与候选基因相关的药物。
在精神分裂症中，DEG与PCD相关基因之间交叉有263个基因，使用机器学习选择了42个候选基因。通过差异表达谱选择差异最显著的10个基因来建立诊断预测模型。使用人工神经网络（ANN）和一致性聚类（CC）进行验证，同时绘制ROC曲线以评估诊断价值。根据研究结果，该预测模型具有较高的诊断价值。免疫浸润分析显示精神分裂症患者的细胞毒性细胞和自然杀伤细胞存在显著差异。从网络分析在线平台收集了6种候选基因相关药物。
我们的研究系统地发现了10个候选枢纽基因（，，，，，，，，，和）。通过在训练组（AUC 0.91，CI 0.95 - 0.86）和验证组（AUC 0.94，CI 1.00 - 0.85）中的综合分析获得了良好的诊断预测模型。此外，还获得了可能对精神分裂症治疗有用的药物（丙戊酸、表没食子儿茶素没食子酸酯）。
