[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126978-en":3,"doc-seo-126978-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126978,687207024478,"Liam","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Identification of Significant Biomarkers for Predicting the Risk of Bipolar Disorder with Arteriosclerosis - based on integrative bioinformatics and machine learning","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.","TYPE Original Research PUBLISHED 03 September 2024 DOI 10.3389/fpsyt.2024.1392437  \nOPEN ACCESS  \nEDITED BY Tao Wang,  \nNorthwestern Polytechnical University, China  \nREVIEWED BY Junwei Han,  \nHarbin Medical University, China Massimo Tusconi,  \nUniversity of Cagliari, Italy  \n*CORRESPONDENCE Erni Ji  \n [38346840@qq.com](38346840@qq.com)[ ](38346840@qq.com)Xiabing Zheng  \n [824381788@qq.com](824381788@qq.com)  \n†These authors have contributed equally to this work and share first authorship  \nRECEIVED 04 April 2024  \nACCEPTED 19 August 2024  \nPUBLISHED 03 September 2024  \nCITATION  \nZheng 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.  \nFront. Psychiatry 15:1392437 .  \ndoi: 10.3389/fpsyt.2024.1392437  \nCOPYRIGHT  \n© 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.  \nIdentification of significant biomarkers for predicting the risk of bipolar disorder with arteriosclerosis based on integrative bioinformatics and machine learning  \nXiabing Zheng 1*†, Xiaozhe Zhang2†, Yaqi Zhang 3, Cai Chen 4 and Erni Ji 1*  \n1 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  \nIntroduction: 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.  \nMethods: 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.  \nResults: 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.  \nFrontiers in Psychiatry 01 [frontiersin.org](frontiersin.org)  \nDiscussion: 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.  \nKEYWORDS  \nbipolar disorder, arteriosclerosis, bioinformatics, hub genes, CX3CR1, ST6GAL1  \n1 Introduction  \nBipolar disorder (BD) is marked ","cbCaibN5GKbGowDt","https://ap.wps.com/l/cbCaibN5GKbGowDt","pdf",5622363,1,16,"English","en",105,"# Introduction\n## Bipolar disorder and cardiovascular risk\n## Arteriosclerosis and clinical relevance\n## Links between arteriosclerosis and bipolar disorder\n# Methods\n## Data sources and preprocessing\n## Differential expression and co-expression analysis\n## Candidate gene intersection and hub identification\n## Functional enrichment, PPI network, and validation\n## Immune infiltration analysis\n# Results\n## Overlapping DE-ASRGs\n## Candidate hub genes\n## Machine-learning co-biomarkers\n## Immune infiltration findings\n# Discussion","[{\"question\":\"What is the main goal of the study on bipolar disorder and arteriosclerosis?\",\"answer\":\"To identify hub genes and molecular pathways shared by bipolar disorder and arteriosclerosis, clarifying potential mechanisms and co-biomarkers for risk prediction.\"},{\"question\":\"How were candidate biomarkers selected in this research?\",\"answer\":\"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.\"},{\"question\":\"Which genes were finally identified as co-biomarkers and why are they important?\",\"answer\":\"CX3CR1 and ST6GAL1 were identified as co-biomarkers for bipolar disorder and arteriosclerosis, supported by machine-learning results and validation across external datasets.\"}]","Identification of Significant Biomarkers for Predicting the Risk of Bipolar Disorder with Arteriosclerosis - 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