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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

BACKGROUND: 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 diagnos...

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Autores principales: Shen, Jing, Feng, Yu, Lu, Minyan, He, Jin, Yang, Huifeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10250745/
https://www.ncbi.nlm.nih.gov/pubmed/37305753
http://dx.doi.org/10.3389/fneur.2023.1189746
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author Shen, Jing
Feng, Yu
Lu, Minyan
He, Jin
Yang, Huifeng
author_facet Shen, Jing
Feng, Yu
Lu, Minyan
He, Jin
Yang, Huifeng
author_sort Shen, Jing
collection PubMed
description BACKGROUND: 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. METHODS: 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–protein 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. RESULTS: Through comprehensive analysis, a diagnostic prediction model with good effect was obtained. Both the training group (AUC 0.82, CI 0.93–0.71) and the verification group (AUC 0.81, CI 0.90–0.72) 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 1–0.64). 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. CONCLUSION: Through comprehensive analysis, a diagnostic prediction model with good effect was obtained. Both the training group (AUC 0.82, CI 0.93–0.71) and the verification group (AUC 0.81, CI 0.90–0.72) 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 1–0.64). 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.
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spelling pubmed-102507452023-06-10 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 Shen, Jing Feng, Yu Lu, Minyan He, Jin Yang, Huifeng Front Neurol Neurology BACKGROUND: 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. METHODS: 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–protein 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. RESULTS: Through comprehensive analysis, a diagnostic prediction model with good effect was obtained. Both the training group (AUC 0.82, CI 0.93–0.71) and the verification group (AUC 0.81, CI 0.90–0.72) 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 1–0.64). 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. CONCLUSION: Through comprehensive analysis, a diagnostic prediction model with good effect was obtained. Both the training group (AUC 0.82, CI 0.93–0.71) and the verification group (AUC 0.81, CI 0.90–0.72) 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 1–0.64). 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. Frontiers Media S.A. 2023-05-26 /pmc/articles/PMC10250745/ /pubmed/37305753 http://dx.doi.org/10.3389/fneur.2023.1189746 Text en Copyright © 2023 Shen, Feng, Lu, He and Yang. https://creativecommons.org/licenses/by/4.0/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.
spellingShingle Neurology
Shen, Jing
Feng, Yu
Lu, Minyan
He, Jin
Yang, Huifeng
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
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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_short 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
title_sort 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
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10250745/
https://www.ncbi.nlm.nih.gov/pubmed/37305753
http://dx.doi.org/10.3389/fneur.2023.1189746
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