Identification of the role of immune-related genes in the diagnosis of bipolar disorder with metabolic syndrome through machine learning and comprehensive bioinformatics analysis

Frontiers in Psychiatry

Psychiatry

Yu Feng¹  Huifeng Yang²  Jing Shen²  Minyan Lu²  Jin He² 

[1] Medicine and Health, The University of New South Wales, Kensington, NSW, Australia;Melbourne Medical School, The University of Melbourne, Parkville, VIC, Australia;The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Nanjing, China;

关键词: bipolar disorder;    metabolic syndrome;    differentially expressed genes;    machine learning;    immune infiltration bipolar disorder;    immune infiltration;   

DOI  :  10.3389/fpsyt.2023.1187360

 received in 2023-03-16, accepted in 2023-09-20,  发布年份 2023

来源: Frontiers

【 摘 要 】

BackgroundBipolar 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.MethodsA 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.ResultsThere 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.ConclusionPotential 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.

【 授权许可】

Unknown   
Copyright © 2023 Shen, Feng, Lu, He and Yang.