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基于整合生物信息学和机器学习识别预测双相情感障碍合并动脉硬化风险的重要生物标志物

Identification of significant biomarkers for predicting the risk of bipolar disorder with arteriosclerosis based on integrative bioinformatics and machine learning.

作者信息

Zheng Xiabing, Zhang Xiaozhe, Zhang Yaqi, Chen Cai, Ji Erni

机构信息

Department of Bipolar Disorder, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, Guangdong, China.

Department of Cardiology, The Eighth Affiliated Hospital of Sun Yat-Sen University, Shenzhen, Guangzhou, China.

出版信息

Front Psychiatry. 2024 Sep 3;15:1392437. doi: 10.3389/fpsyt.2024.1392437. eCollection 2024.

DOI:10.3389/fpsyt.2024.1392437
PMID:39290304
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11405317/
Abstract

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 arteriosclerosis-related 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 DEGs and 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.

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.

摘要

引言

越来越多的证据表明双相情感障碍（BD）与动脉硬化（AS）之间存在联系，但其具体分子机制仍不清楚。本研究旨在探究BD合并AS的核心基因和分子通路。

方法

从基因表达综合数据库下载BD相关数据集GSE12649，通过Limma和加权基因共表达网络分析（WGCNA）鉴定差异表达基因（DEGs）和关键模块基因。AS相关基因来源于DisGeNET数据库，将DEGs与AS相关基因之间的重叠基因表征为差异表达动脉硬化相关基因（DE-ASRGs）。进行功能富集分析、蛋白质-蛋白质相互作用（PPI）网络和三种机器学习算法以探索核心基因，并使用两个外部验证集进行验证。此外，对BD进行免疫浸润分析。

结果

总体而言，发现67个DE-ASRGs在DEGs和AS相关基因之间重叠。功能富集分析突出了BD和AS之间的癌症通路。我们鉴定出七个候选核心基因（CTSD、IRF3、NPEPPS、ST6GAL1、HIF1A、SOX9和CX3CR1）。最终，通过机器学习算法鉴定出两个核心基因（CX3CR1和ST6GAL1）作为BD和AS的共同生物标志物。免疫浸润揭示了免疫细胞的紊乱。

讨论

本研究鉴定出BD和AS中的核心基因CX3CR1和ST6GAL1，为进一步研究BD合并AS的生物信息学机制提供了新见解，并有助于精神科临床实践中AS的诊断和预防。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/648e/11405317/24533a59e60f/fpsyt-15-1392437-g007.jpg
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