Identification of the Role of Immune-Related Genes in the Diagnosis of Bipolar Disorder with Metabolic Syndrome - Through Machine Learning and Comprehensive Bioinformatics Analysis

Aug 6, 2026

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Bipolar disorder and metabolic syndrome show links with immune dysregulation, motivating a search for diagnostic candidate genes for bipolar affective disorder comorbid with metabolic syndrome. Gene Expression Omnibus datasets supported differential expression analysis and weighted gene co-expression network analysis to derive immune-relevant modules. Functional enrichment and protein–protein interaction modeling were used, followed by LASSO and Random Forest feature selection. Receiver operating characteristic evaluation assessed diagnostic utility, and immune infiltration analysis explored immune cell imbalance. Results identified 5 candidate genes with strong diagnostic value and diagnostic significance.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Introduction

Background and rationale

Methods

Data source and differential expression

WGCNA and immune-centered gene selection

PPI network, LASSO, and Random Forest

ROC diagnosis evaluation

Immune cell infiltration analysis

Results

Differentially expressed genes and module genes overlap

Candidate gene selection and diagnostic performance

Conclusion

TYPE Original Research PUBLISHED 04 October 2023 DOI 10.3389/fpsyt.2023.1187360 OPEN ACCESS EDITED BY Massimo Tusconi, University of Cagliari, Italy REVIEWED BY Maria Carmela Padula, Ospedale San Carlo, Italy Jung Goo Lee, Inje University Haeundae Paik Hospital, Republic of Korea *CORRESPONDENCE Huifeng Yang [290367464@qq.com](290367464@qq.com) RECEIVED 16 March 2023 ACCEPTED 20 September 2023 PUBLISHED 04 October 2023 CITATION Shen J, Feng Y, Lu M, He J and Yang H (2023) Identification of the role of immune-related genes in the diagnosis of bipolar disorder with metabolic syndrome through machine learning and comprehensive bioinformatics analysis. Front. Psychiatry 14:1187360 . doi: 10.3389/fpsyt.2023.1187360 COPYRIGHT © 2023 Shen, Feng, Lu, He and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Identification of the role of immune-related genes in the diagnosis of bipolar disorder with metabolic syndrome through machine learning and comprehensive bioinformatics analysis Jing Shen 1, Yu Feng 2, 3, Minyan Lu 1, Jin He 1 and Huifeng Yang 1* 1The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Nanjing, China, 2 Medicine and Health, The University of New South Wales, Kensington, NSW, Australia, 3 Melbourne Medical School, The University of Melbourne, Parkville, VIC, Australia Background: 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. Methods: 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. Results: 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. Conclusion: 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. KEYWORDS bipolar disorder, metabolic syndrome, differentially expressed genes, machine learning, immune infiltration bipolar disorder, immune infiltration Frontiers in Psychiatry 01 [frontiersin.org](frontiersin.org) 1. Introduction Bipolar disorder is a serious mental illness characterized by depression, mania, and mixed development ( 1) . Patients often have persistent residual symptoms, psychosocial function problems, cognitive impairment, and low quality of life, along with othe

FAQs

What is the goal of this study?

The study aims to identify effective diagnostic candidate immune-related genes for bipolar affective disorder with metabolic syndrome using machine learning and comprehensive bioinformatics analyses.

Which datasets and analytical methods were used?

Validation data came from the Gene Expression Omnibus (GEO) database. Differentially expressed genes were identified with Limma, followed by weighted gene co-expression network analysis (WGCNA).

How were the key diagnostic genes selected and validated?

Key immune-related genes were obtained through function enrichment and protein–protein interaction (PPI) network analyses, then refined using LASSO and Random Forest. Receiver operating characteristic (ROC) curves were used to evaluate diagnostic value.

Aug 6, 2026

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