Identification of Significant Biomarkers for Predicting the Risk of Bipolar Disorder with Arteriosclerosis - based on integrative bioinformatics and machine learning

Aug 5, 2026

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Increasing evidence links bipolar disorder with arteriosclerosis, but the specific molecular mechanisms remain unclear. This study integrates gene-expression and disease-association resources to identify hub genes and molecular pathways shared by bipolar disorder and arteriosclerosis. Differentially expressed genes and co-expression modules are intersected with arteriosclerosis-related genes, followed by enrichment, protein-protein interaction analysis, and three machine-learning models. Two external datasets validate CX3CR1 and ST6GAL1 as co-biomarkers, while immune infiltration patterns are assessed to characterize immunocyte disturbances.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages16

Reading Time40 min

AccessFree to download

Introduction

Bipolar disorder and cardiovascular risk

Arteriosclerosis and clinical relevance

Links between arteriosclerosis and bipolar disorder

Methods

Data sources and preprocessing

Differential expression and co-expression analysis

Candidate gene intersection and hub identification

Functional enrichment, PPI network, and validation

Immune infiltration analysis

Results

Overlapping DE-ASRGs

Candidate hub genes

Machine-learning co-biomarkers

Immune infiltration findings

Discussion

TYPE Original Research PUBLISHED 03 September 2024 DOI 10.3389/fpsyt.2024.1392437 OPEN ACCESS EDITED BY Tao Wang, Northwestern Polytechnical University, China REVIEWED BY Junwei Han, Harbin Medical University, China Massimo Tusconi, University of Cagliari, Italy *CORRESPONDENCE Erni Ji [38346840@qq.com](38346840@qq.com)[ ](38346840@qq.com)Xiabing Zheng [824381788@qq.com](824381788@qq.com) †These authors have contributed equally to this work and share ﬁrst authorship RECEIVED 04 April 2024 ACCEPTED 19 August 2024 PUBLISHED 03 September 2024 CITATION Zheng X, Zhang X, Zhang Y, Chen C and Ji E (2024) Identiﬁcation of signiﬁcant biomarkers for predicting the risk of bipolar disorder with arteriosclerosis based on integrative bioinformatics and machine learning. Front. Psychiatry 15:1392437 . doi: 10.3389/fpsyt.2024.1392437 COPYRIGHT © 2024 Zheng, Zhang, Zhang, Chen and Ji. 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 signiﬁcant biomarkers for predicting the risk of bipolar disorder with arteriosclerosis based on integrative bioinformatics and machine learning Xiabing Zheng 1*†, Xiaozhe Zhang2†, Yaqi Zhang 3, Cai Chen 4 and Erni Ji 1* 1 Department of Bipolar Disorder, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, Guangdong, China, 2 Department of Cardiology, The Eighth Afﬁliated Hospital of Sun Yat-Sen University, Shenzhen, Guangzhou, China, 3 Department of Geriatrics, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, Guangdong, China, 4 Department of Drug Dependence, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, Guangdong, China Introduction: Increasing evidence has indicated a connection between bipolar disorder (BD) and arteriosclerosis (AS), yet the speciﬁc molecular mechanisms remain unclear. This study aims to investigate the hub genes and molecular pathways for BD with AS. Methods: BD-related dataset GSE12649 were downloaded from the Gene Expression Omnibus database and differentially expressed genes (DEGs) and key module genes derived from Limma and weighted gene co-expression network analyses (WGCNA) were identiﬁed. AS-related genes were sourced from the DisGeNET database, and the overlapping genes between DEGs and AS-related genes were characterized as differentially expressed arteriosclerosisrelated genes (DE-ASRGs) . The functional enrichment analysis, protein-protein interaction (PPI) network and three machine learning algorithms were performed to explore the hub genes, which were validated with two external validation sets. Additionally, immune inﬁltration was performed in BD. Results: Overall, 67 DE-ASRGs were found to be overlapping between the DEGsand AS-related genes. Functional enrichment analysis highlighted the cancer pathways between BD and AS. We identiﬁed seven candidate hub genes (CTSD, IRF3, NPEPPS, ST6GAL1, HIF1A, SOX9 and CX3CR1) . Eventually, two hub genes (CX3CR1 and ST6GAL1) were identiﬁed as BD and AS co-biomarkers by using machine learning algorithms. Immune inﬁltration had revealed the disorder of immunocytes. Frontiers in Psychiatry 01 [frontiersin.org](frontiersin.org) Discussion: This study identiﬁed the hub genes CX3CR1 and ST6GAL1 in BD and AS, providing new insights for further research on the bioinformatic mechanisms of BD with AS and contributing to the diagnosis and prevention of AS in psychiatric clinical practice. KEYWORDS bipolar disorder, arteriosclerosis, bioinformatics, hub genes, CX3CR1, ST6GAL1 1 Introduction Bipolar disorder (BD) is marked

FAQs

What is the main goal of the study on bipolar disorder and arteriosclerosis?

To identify hub genes and molecular pathways shared by bipolar disorder and arteriosclerosis, clarifying potential mechanisms and co-biomarkers for risk prediction.

How were candidate biomarkers selected in this research?

The study intersected bipolar disorder differentially expressed genes with arteriosclerosis-related genes (from DisGeNET), then used enrichment, PPI network analysis, and three machine-learning algorithms with external validations.

Which genes were finally identified as co-biomarkers and why are they important?

CX3CR1 and ST6GAL1 were identified as co-biomarkers for bipolar disorder and arteriosclerosis, supported by machine-learning results and validation across external datasets.

Aug 5, 2026

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