Shen Jing, Feng Yu, Lu Minyan, He Jin, Yang Huifeng
The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Nanjing, China.
Medicine and Health, The University of New South Wales, Kensington, NSW, Australia.
Front Psychiatry. 2023 Oct 4;14:1187360. doi: 10.3389/fpsyt.2023.1187360. eCollection 2023.
Bipolar disorder and metabolic syndrome are both associated with the expression of immune disorders. The current study aims to find the effective diagnostic candidate genes for bipolar affective disorder with metabolic syndrome.
A validation data set of bipolar disorder and metabolic syndrome was provided by the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were found utilizing the Limma package, followed by weighted gene co-expression network analysis (WGCNA). Further analyses were performed to identify the key immune-related center genes through function enrichment analysis, followed by machine learning-based techniques for the construction of protein-protein interaction (PPI) network and identification of the Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF). The receiver operating characteristic (ROC) curve was plotted to diagnose bipolar affective disorder with metabolic syndrome. To investigate the immune cell imbalance in bipolar disorder, the infiltration of the immune cells was developed.
There were 2,289 DEGs in bipolar disorder, and 691 module genes in metabolic syndrome were identified. The DEGs of bipolar disorder and metabolic syndrome module genes crossed into 129 genes, so a total of 5 candidate genes were finally selected through machine learning. The ROC curve results-based assessment of the diagnostic value was done. These results suggest that these candidate genes have high diagnostic value.
Potential candidate genes for bipolar disorder with metabolic syndrome were found in 5 candidate genes (AP1G2, C1orf54, DMAC2L, RABEPK and ZFAND5), all of which have diagnostic significance.
双相情感障碍和代谢综合征均与免疫紊乱的表达有关。本研究旨在寻找双相情感障碍合并代谢综合征的有效诊断候选基因。
基因表达综合数据库（GEO）提供了双相情感障碍和代谢综合征的验证数据集。利用Limma软件包找出差异表达基因（DEGs），随后进行加权基因共表达网络分析（WGCNA）。通过功能富集分析进一步分析以鉴定关键的免疫相关中心基因，接着运用基于机器学习的技术构建蛋白质-蛋白质相互作用（PPI）网络并鉴定最小绝对收缩和选择算子（LASSO）及随机森林（RF）。绘制受试者工作特征（ROC）曲线以诊断双相情感障碍合并代谢综合征。为研究双相情感障碍中的免疫细胞失衡情况，开展了免疫细胞浸润分析。
双相情感障碍中有2289个DEGs，代谢综合征中鉴定出691个模块基因。双相情感障碍的DEGs与代谢综合征模块基因交叉得到129个基因，最终通过机器学习共筛选出5个候选基因。基于ROC曲线结果对诊断价值进行了评估。这些结果表明这些候选基因具有较高的诊断价值。
在5个候选基因（AP1G2、C1orf54、DMAC2L、RABEPK和ZFAND5）中发现了双相情感障碍合并代谢综合征的潜在候选基因，所有这些基因均具有诊断意义。
