[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128494-en":3,"doc-seo-128494-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},128494,13056712833777,"Logic","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Identification of the Role of Immune-Related Genes in the Diagnosis of Bipolar Disorder with Metabolic Syndrome - Through Machine Learning and Comprehensive Bioinformatics Analysis","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.","TYPE Original Research PUBLISHED 04 October 2023 DOI 10.3389/fpsyt.2023.1187360  \nOPEN ACCESS  \nEDITED BY  \nMassimo Tusconi,  \nUniversity of Cagliari, Italy  \nREVIEWED BY  \nMaria Carmela Padula, Ospedale San Carlo, Italy Jung Goo Lee,  \nInje University Haeundae Paik Hospital, Republic of Korea  \n*CORRESPONDENCE  \nHuifeng Yang  \n [290367464@qq.com](290367464@qq.com)  \nRECEIVED 16 March 2023  \nACCEPTED 20 September 2023  \nPUBLISHED 04 October 2023  \nCITATION  \nShen 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 .  \ndoi: 10.3389/fpsyt.2023.1187360  \nCOPYRIGHT  \n© 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.  \nIdentification of the role of immune-related genes in the diagnosis of bipolar disorder with metabolic syndrome through machine learning and comprehensive bioinformatics analysis  \nJing Shen 1, Yu Feng 2, 3, Minyan Lu 1, Jin He 1 and Huifeng Yang 1*  \n1The 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  \nBackground: 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.  \nMethods: 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.  \nResults: 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.  \nConclusion: 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.  \nKEYWORDS  \nbipolar disorder, metabolic syndrome, differentially expressed genes, machine learning, immune infiltration bipolar disorder, immune infiltration  \nFrontiers in Psychiatry 01 [frontiersin.org](frontiersin.org)  \n1. Introduction  \nBipolar 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","cbCaid4yK7Gzl5vT","https://ap.wps.com/l/cbCaid4yK7Gzl5vT","pdf",9998654,1,12,"English","en",105,"# Introduction\n## Background and rationale\n# Methods\n## Data source and differential expression\n## WGCNA and immune-centered gene selection\n## PPI network, LASSO, and Random Forest\n## ROC diagnosis evaluation\n## Immune cell infiltration analysis\n# Results\n## Differentially expressed genes and module genes overlap\n## Candidate gene selection and diagnostic performance\n# Conclusion","[{\"question\":\"What is the goal of this study?\",\"answer\":\"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.\"},{\"question\":\"Which datasets and analytical methods were used?\",\"answer\":\"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).\"},{\"question\":\"How were the key diagnostic genes selected and validated?\",\"answer\":\"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.\"}]","Identification of the Role of Immune-Related Genes in the Diagnosis of Bipolar Disorder with Metabolic Syndrome - Through Machine Learning and Comprehensive Bioinformatics Analysis | PDF",1786001375,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"identification-of-the-role-of-immune-related-genes-in-the-diagnosis-of-bipolar-disorder-with-metabolic-syndrome-through-machine-learning-and-comprehensive-bioinformatics-analysis","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/identification-of-the-role-of-immune-related-genes-in-the-diagnosis-of-bipolar-disorder-with-metabolic-syndrome-through-machine-learning-and-comprehensive-bioinformatics-analysis/128494/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the goal of this study?","Question",{"text":76,"@type":77},"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.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which datasets and analytical methods were used?",{"text":81,"@type":77},"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).",{"name":83,"@type":74,"acceptedAnswer":84},"How were the key diagnostic genes selected and validated?",{"text":85,"@type":77},"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.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":122},"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]