{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T01:25:40Z","timestamp":1766625940942,"version":"3.41.2"},"reference-count":47,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,5,26]],"date-time":"2023-05-26T00:00:00Z","timestamp":1685059200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Patients with comorbid schizophrenia, depression, drug use, and multiple psychiatric diagnoses have a greater risk of carotid revascularization following stroke. The gut microbiome (GM) plays a crucial role in the attack of mental illness and IS, which may become an index for the diagnosis of IS. A genomic study of the genetic commonalities between SC and IS, as well as its mediated pathways and immune infiltration, will be conducted to determine how schizophrenia contributes to the high prevalence of IS. According to our study, this could be an indicator of ischemic stroke development.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We selected two datasets of IS from the Gene Expression Omnibus (GEO), one for training and the other for the verification group. Five genes related to mental disorders and GM were extracted from Gene cards and other databases. Linear models for microarray data (Limma) analysis was utilized to identify differentially expressed genes (DEGs) and perform functional enrichment analysis. It was also used to conduct machine learning exercises such as random forest and regression to identify the best candidate for immune-related central genes. Protein\u2013protein interaction (PPI) network and artificial neural network (ANN) were established for verification. The receiver operating characteristic (ROC) curve was drawn for the diagnosis of IS, and the diagnostic model was verified by qRT-PCR. Further immune cell infiltration analysis was performed to study the IS immune cell imbalance. We also performed consensus clustering (CC) to analyze the expression of candidate models under different subtypes. Finally, miRNA, transcription factors (TFs), and drugs related to candidate genes were collected through the Network analyst online platform.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Through comprehensive analysis, a diagnostic prediction model with good effect was obtained. Both the training group (AUC 0.82, CI 0.93\u20130.71) and the verification group (AUC 0.81, CI 0.90\u20130.72) had a good phenotype in the qRT-PCR test. And in verification group 2 we validated between the two groups with and without carotid-related ischemic cerebrovascular events (AUC 0.87, CI 1\u20130.64). Furthermore, we investigated cytokines in both GSEA and immune infiltration and verified cytokine-related responses by flow cytometry, particularly IL-6, which played an important role in IS occurrence and progression. Therefore, we speculate that mental illness may affect the development of IS in B cells and IL-6 in T cells. MiRNA (hsa-mir-129-2-3p, has-mir-335-5p, and has-mir-16-5p) and TFs (CREB1, FOXL1), which may be related to IS, were obtained.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Through comprehensive analysis, a diagnostic prediction model with good effect was obtained. Both the training group (AUC 0.82, CI 0.93\u20130.71) and the verification group (AUC 0.81, CI 0.90\u20130.72) had a good phenotype in the qRT-PCR test. And in verification group 2 we validated between the two groups with and without carotid-related ischemic cerebrovascular events (AUC 0.87, CI 1\u20130.64). MiRNA (hsa-mir-129-2-3p, has-mir-335-5p, and has-mir-16-5p) and TFs (CREB1, FOXL1), which may be related to IS, were obtained.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fneur.2023.1189746","type":"journal-article","created":{"date-parts":[[2023,5,26]],"date-time":"2023-05-26T04:57:51Z","timestamp":1685077071000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Predictive model, miRNA-TF network, related subgroup identification and drug prediction of ischemic stroke complicated with mental disorders based on genes related to gut microbiome"],"prefix":"10.3389","volume":"14","author":[{"given":"Jing","family":"Shen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minyan","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huifeng","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2023,5,26]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"S6","DOI":"10.1212\/wnl.0000000000012781","article-title":"Global epidemiology of stroke and access to acute ischemic stroke interventions","volume":"97","author":"Saini","year":"2021","journal-title":"Neurology"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"113382","DOI":"10.1016\/j.expneurol.2020.113382","article-title":"Emerging role of Micrornas in ischemic stroke with comorbidities","volume":"331","author":"Qian","year":"2020","journal-title":"Exp Neurol"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1016\/s1474-4422(08)70193-5","article-title":"Sex differences in stroke: epidemiology, clinical presentation, medical care, and outcomes","volume":"7","author":"Reeves","year":"2008","journal-title":"Lancet Neurol"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"1252","DOI":"10.1161\/circresaha.122.319951","article-title":"Post-stroke cognitive impairment and dementia","volume":"130","author":"Rost","year":"2022","journal-title":"Circ Res"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1016\/j.bbadis.2016.01.015","article-title":"Stroke injury, cognitive impairment and vascular dementia","volume":"1862","author":"Kalaria","year":"2016","journal-title":"Biochim Biophys Acta"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.neubiorev.2018.04.005","article-title":"Post stroke depression: the Sequelae of cerebral stroke","volume":"90","author":"Das","year":"2018","journal-title":"Neurosci Biobehav Rev"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1111\/j.1747-4949.2012.00906.x","article-title":"Frequency of anxiety after stroke: a systematic review and Meta-analysis of observational studies","volume":"8","author":"Campbell Burton","year":"2013","journal-title":"Int J Stroke"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1192\/bjp.166.3.328","article-title":"Anxiety disorders after stroke: results from the Perth community stroke study","volume":"166","author":"Burvill","year":"1995","journal-title":"Br J Psychiatry"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1161\/strokeaha.117.020078","article-title":"Anxiety after stroke: the importance of subtyping","volume":"49","author":"Chun","year":"2018","journal-title":"Stroke"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"2601","DOI":"10.1161\/strokeaha.120.032995","article-title":"Increased risk of stroke in patients with obsessive-compulsive disorder: a Nationwide longitudinal study","volume":"52","author":"Chen","year":"2021","journal-title":"Stroke"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"e2514","DOI":"10.1212\/wnl.0000000000007565","article-title":"Patients with stroke and psychiatric comorbidities have lower carotid revascularization rates","volume":"92","author":"Bongiorno","year":"2019","journal-title":"Neurology"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1186\/s12866-019-1552-1","article-title":"Change of intestinal microbiota in cerebral ischemic stroke patients","volume":"19","author":"Li","year":"2019","journal-title":"BMC Microbiol"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"516","DOI":"10.1038\/nm.4068","article-title":"Commensal microbiota affects ischemic stroke outcome by regulating intestinal \u0393\u03b4 T cells","volume":"22","author":"Benakis","year":"2016","journal-title":"Nat Med"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.bbi.2016.04.003","article-title":"Brain injury induces specific changes in the Caecal microbiota of mice via altered autonomic activity and Mucoprotein production","volume":"57","author":"Houlden","year":"2016","journal-title":"Brain Behav Immun"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"915","DOI":"10.3390\/ijms22020915","article-title":"The role of gut microbiota in an ischemic stroke","volume":"22","author":"Pluta","year":"2021","journal-title":"Int J Mol Sci"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"101943","DOI":"10.1016\/j.cpr.2020.101943","article-title":"The gut microbiota in anxiety and depression \u2013 a systematic review","volume":"83","author":"Simpson","year":"2021","journal-title":"Clin Psychol Rev"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1186\/s12974-020-01961-8","article-title":"Associations between gut microbiota and Alzheimer's disease, major depressive disorder, and schizophrenia","volume":"17","author":"Zhuang","year":"2020","journal-title":"J Neuroinflammation"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"D991","DOI":"10.1093\/nar\/gks1193","article-title":"Ncbi geo: archive for functional genomics data sets\u2014update","volume":"41","author":"Barrett","year":"2012","journal-title":"Nucleic Acids Res"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1186\/1471-2105-13-335","article-title":"Unlocking the potential of publicly available microarray data using Insilicodb and Insilicomerging R\/bioconductor packages","volume":"13","author":"Taminau","year":"2012","journal-title":"BMC Bioinformatics"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1093\/biostatistics\/kxj037","article-title":"Adjusting batch effects in microarray expression data using empirical Bayes methods","volume":"8","author":"Johnson","year":"2007","journal-title":"Biostatistics"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1186\/1471-2105-5-108","article-title":"Linear fuzzy gene network models obtained from microarray data by exhaustive search","volume":"5","author":"Sokhansanj","year":"2004","journal-title":"BMC Bioinformatics"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"e36","DOI":"10.1002\/imt2.36","article-title":"Sangerbox: a comprehensive, interaction-friendly clinical bioinformatics analysis platform","volume":"1","author":"Shen","year":"2022","journal-title":"iMeta"},{"year":"2022","key":"ref23"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1089\/omi.2011.0118","article-title":"Clusterprofiler: an R package for comparing biological themes among gene clusters","volume":"16","author":"Yu","year":"2012","journal-title":"OMICS"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"15545","DOI":"10.1073\/pnas.0506580102","article-title":"Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles","volume":"102","author":"Subramanian","year":"2005","journal-title":"Proc Natl Acad Sci U S A"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"1739","DOI":"10.1093\/bioinformatics\/btr260","article-title":"Molecular signatures database (Msigdb) 3.0","volume":"27","author":"Liberzon","year":"2011","journal-title":"Bioinformatics"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"1314","DOI":"10.3389\/fonc.2019.01314","article-title":"An immune-related signature predicts survival in patients with lung adenocarcinoma","volume":"9","author":"Zhang","year":"2019","journal-title":"Front Oncol"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"3687","DOI":"10.1016\/j.sjbs.2022.02.047","article-title":"Prediction of antimicrobial minimal inhibitory concentrations for Neisseria Gonorrhoeae using machine learning models","volume":"29","author":"Yasir","year":"2022","journal-title":"Saudi J Biol Sci"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"W60","DOI":"10.1093\/nar\/gky311","article-title":"Genemania Update 2018","volume":"46","author":"Franz","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1186\/1471-2105-12-77","article-title":"Proc: an open-source package for R and S+ to analyze and compare roc curves","volume":"12","author":"Robin","year":"2011","journal-title":"BMC Bioinformatics"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v085.i11","article-title":"Neuralnettools: visualization and analysis tools for neural networks","volume":"85","author":"Beck","year":"2018","journal-title":"J Stat Softw"},{"key":"ref32","doi-asserted-by":"publisher","first-page":"520351","DOI":"10.1101\/2023.02.14.520351","article-title":"Translatome analysis reveals microglia and astrocytes to be distinct regulators of inflammation in the Hyperacute and acute phases after stroke","volume":"2","author":"Hernandez","year":"2023","journal-title":"bio Rxiv"},{"key":"ref33","doi-asserted-by":"publisher","first-page":"726747","DOI":"10.3389\/fimmu.2021.726747","article-title":"Identifying immune cell infiltration and effective diagnostic biomarkers in rheumatoid arthritis by bioinformatics analysis","volume":"12","author":"Zhou","year":"2021","journal-title":"Front Immunol"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.4045\/tidsskr.18.0042","article-title":"Pearson's or Spearman's correlation coefficients","volume":"138","author":"Pripp","year":"2018","journal-title":"Tidsskr Nor Laegeforen"},{"key":"ref35","doi-asserted-by":"publisher","first-page":"8307975","DOI":"10.1155\/2022\/8307975","article-title":"Identification of atrial fibrillation-related Lncrna based on Bioinformatic analysis","volume":"2022","author":"Xie","year":"2022","journal-title":"Dis Markers"},{"key":"ref36","doi-asserted-by":"publisher","first-page":"W234","DOI":"10.1093\/nar\/gkz240","article-title":"Networkanalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and Meta-analysis","volume":"47","author":"Zhou","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"ref37","doi-asserted-by":"publisher","first-page":"1572","DOI":"10.1093\/bioinformatics\/btq170","article-title":"Consensusclusterplus: a class discovery tool with confidence assessments and item tracking","volume":"26","author":"Wilkerson","year":"2010","journal-title":"Bioinformatics"},{"key":"ref38","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1186\/1471-2105-14-7","article-title":"Gsva: gene set variation analysis for microarray and Rna-Seq data","volume":"14","author":"H\u00e4nzelmann","year":"2013","journal-title":"BMC Bioinformatics"},{"key":"ref39","doi-asserted-by":"publisher","first-page":"8060477","DOI":"10.1155\/2021\/8060477","article-title":"Role of calcium signaling pathway-related gene regulatory networks in ischemic stroke based on multiple Wgcna and single-cell analysis","volume":"2021","author":"Lin","year":"2021","journal-title":"Oxidative Med Cell Longev"},{"key":"ref40","doi-asserted-by":"publisher","first-page":"D1074","DOI":"10.1093\/nar\/gkx1037","article-title":"Drugbank 5.0: a major update to the Drugbank database for 2018","volume":"46","author":"Wishart","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"ref41","doi-asserted-by":"publisher","first-page":"D1257","DOI":"10.1093\/nar\/gkac833","article-title":"Comparative Toxicogenomics database (Ctd): update 2023","volume":"51","author":"Davis","year":"2022","journal-title":"Nucleic Acids Res"},{"key":"ref42","doi-asserted-by":"publisher","first-page":"114026","DOI":"10.1016\/j.biopha.2022.114026","article-title":"The mechanism of intestinal microbiota regulating immunity and inflammation in ischemic stroke and the role of natural botanical active ingredients in regulating intestinal microbiota: a review","volume":"157","author":"Zeng","year":"2023","journal-title":"Biomed Pharmacother"},{"key":"ref43","doi-asserted-by":"publisher","first-page":"7341","DOI":"10.1002\/jcp.27493","article-title":"Cxcl 8 gene silencing promotes Neuroglial cells activation while inhibiting Neuroinflammation through the Pi3k\/Akt\/Nf-\u039ab-signaling pathway in mice with ischemic stroke","volume":"234","author":"Lv","year":"2019","journal-title":"J Cell Physiol"},{"key":"ref44","doi-asserted-by":"publisher","first-page":"806200","DOI":"10.3389\/fnagi.2022.806200","article-title":"The construction and analysis of immune infiltration and competing endogenous Rna network in acute ischemic stroke","volume":"14","author":"Ma","year":"2022","journal-title":"Front Aging Neurosci"},{"key":"ref45","doi-asserted-by":"publisher","first-page":"e28218","DOI":"10.7759\/cureus.28218","article-title":"Relationship of Methylenetetrahydrofolate Reductase (Mthfr) C677t variation with susceptibility of patients with ischemic stroke: a Meta-analysis","volume":"14","author":"Kumar","year":"2022","journal-title":"Cureus"},{"key":"ref46","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.brainresbull.2006.07.014","article-title":"Methylenetetrahydrofolate Reductase gene polymorphisms are associated with ischemic and hemorrhagic stroke: dual effect of Mthfr polymorphisms C677t and A1298c","volume":"71","author":"Sazci","year":"2006","journal-title":"Brain Res Bull"},{"key":"ref47","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.envint.2018.12.037","article-title":"Associations of multiple plasma metals with the risk of ischemic stroke: a case-control study","volume":"125","author":"Wen","year":"2019","journal-title":"Environ Int"}],"container-title":["Frontiers in Neurology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fneur.2023.1189746\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,26]],"date-time":"2023-05-26T04:57:54Z","timestamp":1685077074000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fneur.2023.1189746\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,26]]},"references-count":47,"alternative-id":["10.3389\/fneur.2023.1189746"],"URL":"https:\/\/doi.org\/10.3389\/fneur.2023.1189746","relation":{},"ISSN":["1664-2295"],"issn-type":[{"type":"electronic","value":"1664-2295"}],"subject":[],"published":{"date-parts":[[2023,5,26]]},"article-number":"1189746"}}