Zhang Chi, Dong Naifu, Xu Shihan, Ma Haichun, Cheng Min
Department of Anesthesiology, The First Hospital of Jilin University, Changchun, China.
College of Basic Medical Sciences, Jilin University, Changchun, China.
Front Aging Neurosci. 2022 Oct 14;14:1032917. doi: 10.3389/fnagi.2022.1032917. eCollection 2022.
Schizophrenia (SCZ), which is characterized by debilitating neuropsychiatric disorders with significant cognitive impairment, remains an etiological and therapeutic challenge. Using transcriptomic profile analysis, disease-related biomarkers linked with SCZ have been identified, and clinical outcomes can also be predicted. This study aimed to discover diagnostic hub genes and investigate their possible involvement in SCZ immunopathology. The Gene Expression Omnibus (GEO) database was utilized to get SCZ Gene expression data. Differentially expressed genes (DEGs) were identified and enriched by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and disease ontology (DO) analysis. The related gene modules were then examined using integrated weighted gene co-expression network analysis. Single-sample gene set enrichment (GSEA) was exploited to detect immune infiltration. SVM-REF, random forest, and least absolute shrinkage and selection operator (LASSO) algorithms were used to identify hub genes. A diagnostic model of nomogram was constructed for SCZ prediction based on the hub genes. The clinical utility of nomogram prediction was evaluated, and the diagnostic utility of hub genes was validated. mRNA levels of the candidate genes in SCZ rat model were determined. Finally, 24 DEGs were discovered, the majority of which were enriched in biological pathways and activities. Four hub genes (NEUROD6, NMU, PVALB, and NECAB1) were identified. A difference in immune infiltration was identified between SCZ and normal groups, and immune cells were shown to potentially interact with hub genes. The hub gene model for the two datasets was verified, showing good discrimination of the nomogram. Calibration curves demonstrated valid concordance between predicted and practical probabilities, and the nomogram was verified to be clinically useful. According to our research, NEUROD6, NMU, PVALB, and NECAB1 are prospective biomarkers in SCZ and that a reliable nomogram based on hub genes could be helpful for SCZ risk prediction.
精神分裂症（SCZ）以伴有严重认知障碍的使人衰弱的神经精神疾病为特征，仍然是病因学和治疗方面的挑战。通过转录组谱分析，已鉴定出与SCZ相关的疾病生物标志物，并且还可以预测临床结果。本研究旨在发现诊断性核心基因并研究它们可能参与SCZ免疫病理学的情况。利用基因表达综合数据库（GEO）获取SCZ基因表达数据。通过基因本体论（GO）、京都基因与基因组百科全书（KEGG）和疾病本体论（DO）分析来鉴定和富集差异表达基因（DEG）。然后使用综合加权基因共表达网络分析来检查相关基因模块。利用单样本基因集富集分析（GSEA）来检测免疫浸润。使用支持向量机-递归特征消除法（SVM-REF）、随机森林和最小绝对收缩和选择算子（LASSO）算法来鉴定核心基因。基于核心基因构建列线图诊断模型用于SCZ预测。评估列线图预测的临床实用性，并验证核心基因的诊断效用。测定SCZ大鼠模型中候选基因的mRNA水平。最后，发现了24个DEG，其中大多数富集于生物学途径和活性中。鉴定出四个核心基因（NEUROD6、NMU、PVALB和NECAB1）。在SCZ组和正常组之间鉴定出免疫浸润的差异，并且显示免疫细胞可能与核心基因相互作用。验证了两个数据集的核心基因模型，显示列线图具有良好的区分度。校准曲线表明预测概率与实际概率之间具有有效的一致性，并且验证了列线图在临床上是有用的。根据我们的研究，NEUROD6、NMU、PVALB和NECAB1是SCZ的潜在生物标志物，并且基于核心基因的可靠列线图可能有助于SCZ风险预测。
