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Identification of Hub Genes Associated with Immune Infiltration in Cardioembolic Stroke by Whole Blood Transcriptome Analysis.

ABSTRACT: Cardioembolic stroke (CS) is the most common type of ischemic stroke in the clinic, leading to high morbidity and mortality worldwide. Although many studies have been conducted, the molecular mechanism underlying CS has not been fully grasped. This study was aimed at exploring the molecular mechanism of CS using comprehensive bioinformatics analysis and providing new insights into the pathophysiology of CS. We downloaded the public datasets GSE58294 and GSE16561. Differentially expressed genes (DEGs) were screened via the limma package using R software. CIBERSORT was used to estimate the proportions of 22 immune cells based on the gene expression profiling of CS patients. Using weighted gene correlation network analysis (WGCNA) to cluster the genes into different modules and detect relationships between modules and immune cell types, hub genes were identified based on the intersection of the protein-protein interaction (PPI) network analysis and WGCNA, and their clinical significance was then verified using another independent dataset GSE16561. Totally, 319 genes were identified as DEGs and 5413 genes were clustered into nine modules using WGCNA. The blue module, with the highest correlation coefficient, was identified as the key module associated with stroke, neutrophils, and B cells naïve. Based on the PPI analysis and WGCNA, five genes (MCEMP1, CLEC4D, GPR97, TSPAN14, and FPR2) were identified as hub genes. Correlation analysis indicated that hub genes had general association with infiltration-related immune cells. ROC analysis also showed they had potential clinical significance. The results were verified using another dataset, which were consistent with our analysis. Five crucial genes determined using integrative bioinformatics analysis might play significant roles in the pathophysiological mechanism in CS and be potential targets for pharmaceutic therapies.

SUBMITTER: Li Q

PROVIDER: S-EPMC8783710 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:IntroductionPeriodontitis is an inflammatory disease and its molecular mechanisms is not clear. A recently discovered cell death pathway called cuproptosis, may related to the disease.MethodsThe datasets GSE10334 of human periodontitis and control were retrieved from the Gene Expression Omnibus database (GEO) for analysis.Following the use of two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature removal (SVM-RFE) were used to find CRG-based signature. Then the Receiver operating characteristic (ROC) curves was used to evaluate the gene signature's discriminatory ability. The CIBERSORT deconvolution algorithm was used to study the link between hub genes and distinct types of immune cells. Next, the association of the CRGs with immune cells in periodontitis and relevant clusters of cuproptosis were found. The link between various clusters was ascertained by the GSVA and CIBERSORT deconvolution algorithm. Finally, An external dataset (GSE16134) was used to confirm the diagnosis capacity of the identified biomarkers. In addition, clinical samples were examined using qRT-PCR and immunohistochemistry to verifiy the expression of genes related to cuprotosis in periodontitis and the signature may better predict the periodontitis.Results15 periodontitis-related DE-CRGs were found,then 11-CRG-based signature was found by using of LASSO and SVM-RFE. ROC curves also supported the value of signature. CIBERSORT results of immune cell signature in periodontitis showed that signature genes is a crucial component of the immune response.The relevant clusters of cuproptosis found that the NFE2L2, SLC31A1, FDX1,LIAS, DLD, DLAT, and DBT showed a highest expression levels in Cluster2 ,while the NLRP3, MTF1, and DLST displayed the lowest level in Cluster 2 but the highest level in Cluster1. The GSVA results also showed that the 11 cuproptosis diagnostic gene may regulate the periodontitis by affecting immune cells. The external dataset (GSE16134) confirm the diagnosis capacity of the identified biomarkers, and clinical samples examined by qRT-PCR and immunohistochemistry also verified that these cuprotosis related signiture genes in periodontitis may better predict the periodontitis.ConclusionThese findings have important implications for the cuproptosis and periodontitis, and highlight further research is needed to better understand the mechanisms underlying this relationship between the cuproptosis and periodontitis.

Project description:BackgroundAlzheimer's disease (AD) is the most common type of neurodegenerative disease. Tau pathology is one of the pathological features of AD, and its progression is closely related to the progress of AD. Immune system dysfunction is an important mediator of Tau pathological progression, but the specific molecular mechanism is still unclear. The purpose of this study is to determine the immune hub genes and peripheral immune cell infiltration associated with the Braak stages, and the molecular mechanisms between them.MethodsIn this study, 60 samples with different Braak stages in the GSE106241 dataset were used to screen Braak stages-related immune hub genes by using the WGCNA package in R and cytoHubba plugin. The temporal lobe expression data in the Alzdata database were used to verify the results. The correlation between the expression level of immune core genes and the pathological features of AD was analyzed to evaluate the abundance of peripheral immune cell infiltration and screened Braak stages-related cells. Finally, we used correlation analysis of immune hub genes and immune cells and Gene Set Enrichment Analysis (GSEA) of them.ResultsSeven genes (GRB2, HSP90AA1, HSPA4, IGF1, KRAS, PIK3R1, and PTPN11) were identified as immune core genes after the screening of the test datasets and validation of independent data. Among them, Kirsten rat sarcoma viral oncogene homolog (KRAS) and Phosphoinositide-3-Kinase Regulatory Subunit 1 (PIK3R1) were the most closely related to Tau and Aβ pathology in AD. In addition, the ImmuneScore increased gradually with the increase of Braak stages. Five types of immune cells (plasma cells, T follicular helper cells, M2 macrophage, activated NK cells, and eosinophils) were correlated with Braak stages. KRAS and PIK3R1 were the immune core genes most related to the abnormal infiltration of peripheral immune cells. They participated in the regulation of the pathological process of AD through axon guidance, long-term potentiation, cytokine-cytokine receptor interaction, RNA polymerase, etc.ConclusionThe KRAS and PIK3R1 genes were identified as the immune hub genes most associated with Tau pathological progress in AD. The abnormal infiltration of peripheral immune cells mediated by these cells was involved in the Tau pathological process. This provides new insights for AD.

Project description:ObjectiveCardioembolic stroke (CE stroke, also known as cardiogenic cerebral embolism, CCE) has the highest recurrence rate and fatality rate among all subtypes of ischemic stroke, the pathogenesis of which was unclear. Autophagy plays an essential role in the development of CE stroke. We aim to identify the potential autophagy-related molecular markers of CE stroke and uncover the potential therapeutic targets through bioinformatics analysis.MethodsThe mRNA expression profile dataset GSE58294 was obtained from the GEO database. The potential autophagy-related differentially expressed (DE) genes of CE stroke were screened by R software. Protein-protein interactions (PPIs), correlation analysis, and gene ontology (GO) enrichment analysis were applied to the autophagy-related DE genes. GSE66724, GSE41177, and GSE22255 were introduced for the verification of the autophagy-related DE genes in CE stroke, and the differences in values were re-calculated by Student's t-test.ResultsA total of 41 autophagy-related DE genes (37 upregulated genes and four downregulated genes) were identified between 23 cardioembolic stroke patients (≤3 h, prior to treatment) and 23 healthy controls. The KEGG and GO enrichment analysis of autophagy-related DE genes indicated several enriched terms related to autophagy, apoptosis, and ER stress. The PPI results demonstrated the interactions between these autophagy-related genes. Moreover, several hub genes, especially for CE stroke, were identified and re-calculated by Student's t-test.ConclusionWe identified 41 potential autophagy-related genes associated with CE stroke through bioinformatics analysis. SERPINA1, WDFY3, ERN1, RHEB, and BCL2L1 were identified as the most significant DE genes that may affect the development of CE stroke by regulating autophagy. CXCR4 was identified as a hub gene of all types of strokes. ARNT, MAPK1, ATG12, ATG16L2, ATG2B, and BECN1 were identified as particular hub genes for CE stroke. These results may provide insight into the role of autophagy in CE stroke and contribute to the discovery of potential therapeutic targets for CE stroke treatment.

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Identification of Hub Genes Associated with Immune Infiltration in Cardioembolic Stroke by Whole Blood Transcriptome Analysis.

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