Bioinformatics Analysis of Gene Expression Profiles of Sex Differences in Ischemic Stroke

Zhu, Wenhao; Nan, Yinan; Wang, Shaoqing; Liu, Wei

doi:10.1155/2019/2478453

Cited by 16 publications

(9 citation statements)

References 25 publications

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“…Recent years, microarray analyses have been attempted in sex differences of human IS. Some previous studies have screened sexually dimorphic differences of gene expression by comparing male IS patients with females directly [ 15 ]. However, some DEGs were located on sex chromosomes that could represent transcript expression differences based on sex or differences based on disease being studied.…”

Section: Resultsmentioning

confidence: 99%

Sex Difference of Ribosome in Stroke-Induced Peripheral Immunosuppression by Integrated Bioinformatics Analysis

Xie

Yapeng

Sun

et al. 2020

BioMed Research International

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Ischemic stroke (IS) greatly threatens human health resulting in high mortality and substantial loss of function. Recent studies have shown that the outcome of IS has sex specific, but its mechanism is still unclear. This study is aimed at identifying the sexually dimorphic to peripheral immune response in IS progression, predicting potential prognostic biomarkers that can lead to sex-specific outcome, and revealing potential treatment targets. Gene expression dataset GSE37587, including 68 peripheral whole blood samples which were collected within 24 hours from known onset of symptom and again at 24-48 hours after onset (20 women and 14 men), was downloaded from the Gene Expression Omnibus (GEO) datasets. First, using Bioconductor R package, two kinds of differentially expressed genes (DEGs) (nonsex-specific- and sex-specific-DEGs) were screened by follow-up (24-48 hours) vs. baseline (24 hours). 30 nonsex-specific DEGs (1 upregulated and 29 downregulated), 79 female-specific DEGs (25 upregulated and 54 downregulated), and none of male-specific DEGs were obtained finally. Second, bioinformatics analysis of female-specific DEGs was performed. Gene Ontology (GO) functional annotation analysis shows that DEGs were mainly enriched in translational initiation, cytosolic ribosome, and structural constituent of ribosome. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis shows that the top 6 enrichment pathways are ribosome, nuclear factor-kappa B (NF-kappa B) signaling pathway, apoptosis, mineral absorption, nonalcoholic fatty liver disease, and pertussis. Three functional modules were clustered in the protein–protein interaction (PPI) network of DEGs. The top 10 key genes of the PPI network constructed were selected, including RPS14, RPS15A, RPS24, FAU, RPL27, RPL31, RPL34, RPL35A, RSL24D1, and EEF1B2. Sex difference of ribosome in stroke-induced peripheral immunosuppression may be the potential mechanism of sex disparities in outcome after IS, and women are more likely to have stroke-induced immunosuppression. RPS14, RPS15A, RPS24, FAU, RPL27, RPL31, RPL34, RPL35A, RSL24D1, and EEF1B2 may be novel prognostic biomarkers and potential therapeutic targets for IS.

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Section: Resultsmentioning

confidence: 99%

Sex Difference of Ribosome in Stroke-Induced Peripheral Immunosuppression by Integrated Bioinformatics Analysis

Xie

Yapeng

Sun

et al. 2020

BioMed Research International

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“…Zou et al (2019) identified ZNF566, PDZK1IP1, ZFHX3, and PITX2 genes as significant biomarkers for AF-related stroke. Zhu WH et al (2019) found that IL1α, IL1β, IL6, IL8, CXCL1, CXCL2, CXCL20, CCL4, ICAM1, and PTGS2 had a protective effect against stroke in females. However, no gene expression profile studies have yet focused on HF-related stroke, and the underlying genes and biological processes of HF-related stroke can be clearly understood by revealing its gene expression variations and patterns.…”

Section: Introductionmentioning

confidence: 92%

Bioinformatic analysis for potential biological processes and key targets of heart failure-related stroke

Liu

Chen

Zhang

et al. 2021

J. Zhejiang Univ. Sci. B

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This study aimed to uncover underlying mechanisms and promising intervention targets of heart failure (HF)-related stroke. HF-related dataset GSE42955 and stroke-related dataset GSE58294 were obtained from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was conducted to identify key modules and hub genes. Gene Ontology (GO) and pathway enrichment analyses were performed on genes in the key modules. Genes in HF-and stroke-related key modules were intersected to obtain common genes for HF-related stroke, which were further intersected with hub genes of stroke-related key modules to obtain key genes in HF-related stroke. Key genes were functionally annotated through GO in the Reactome and Cytoscape databases. Finally, key genes were validated in these two datasets and other datasets. HF-and stroke-related datasets each identified two key modules. Functional enrichment analysis indicated that protein ubiquitination, Wnt signaling, and exosomes were involved in both HF-and stroke-related key modules. Additionally, ten hub genes were identified in stroke-related key modules and 155 genes were identified as common genes in HF-related stroke. OTU deubiquitinase with linear linkage specificity (OTULIN) and nuclear factor interleukin 3-regulated (NFIL3) were determined to be the key genes in HF-related stroke. Through functional annotation, OTULIN was involved in protein ubiquitination and Wnt signaling, and NFIL3 was involved in DNA binding and transcription. Importantly, OTULIN and NFIL3 were also validated to be differentially expressed in all HF and stroke groups. Protein ubiquitination, Wnt signaling, and exosomes were involved in HF-related stroke. OTULIN and NFIL3 may play a key role in HF-related stroke through regulating these processes, and thus serve as promising intervention targets.

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“…Several studies have shown that the CCL4 signaling pathway is important for tumorigenesis and progression (21)(22)(23)(24). Zhu et al revealed that CCL4 may play a critical role in ischemic stroke, and may be significantly correlated with sex (25). CCL4 has also been shown to regulate human vascular smooth muscle cells (26).…”

Section: Discussionmentioning

confidence: 99%

Identification of vasospasm biomarkers for cerebral hemorrhage via bio-informatics analysis

Wu¹,

Liang²,

Li³

et al. 2022

Ann Palliat Med

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Background: The incidence of cerebral hemorrhage has rapidly increased over time, and vascular dysfunction has a significant influence on the pathogenesis and outcome of these patients. This is also the case for vasospasm in cerebral hemorrhage, but there is no method to assess this. We conducted this study to find molecular biomarkers of vasospasm in cerebral hemorrhage patients.Methods: Raw data of GSE37924 was downloaded from the Gene Expression Omnibus (GEO) database, including 66 samples with cerebral vasospasm and 62 samples without cerebral vasospasm. Differentially expressed genes (DEGs) between samples with cerebral vasospasm and those without cerebral vasospasm were analyzed using the limma package in R software. To determine the functions of DEGs, we conducted functional enrichment analysis of DEGs through the clusterProfiler package in R. The protein-protein interaction (PPI) network of DEGs was constructed through STRING (https://string-db.org/) and generated via Cytoscape software. To understand the correlation between DEGs and immune-related genes, immune-related cerebral vasospasm genes were obtained via intersecting immune-related genes and cerebral vasospasm DEGs. We also compared the infiltration of 28 immune cells between cases with cerebral vasospasm and those without cerebral vasospasm. Finally, we constructed a model to perform the validation experiments.Results: Of the DEGs, there were 24 upregulated and 21 downregulated genes in the vasospasm samples compared to the no-vasospasm samples. Functional enrichment analysis showed that these genes play key roles in several biological processes and signaling pathways such as the bone morphogenetic protein (BMP) signaling pathway, cellular response to BMP stimulus, natural killer cell chemotaxis, negative regulation of transmembrane receptor protein serine/threonine kinase signaling pathway, MHC protein complex binding, and receptor ligand activity, among others. CCL4, HLA-DQA1, IGF2, NTS, and so on were the significant immune-related genes. Furthermore, the immune cell infiltration results showed that there were differences between patients with vasospasm and those without vasospasm. Finally, we found that CCL4 had significantly higher expression in patients with vasospasm than those without vasospasm.Conclusions: CCL4 is an important regulator of vascular dysfunction in cerebral hemorrhage.

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Bioinformatics Analysis of Gene Expression Profiles of Sex Differences in Ischemic Stroke

Cited by 16 publications

References 25 publications

Sex Difference of Ribosome in Stroke-Induced Peripheral Immunosuppression by Integrated Bioinformatics Analysis

Sex Difference of Ribosome in Stroke-Induced Peripheral Immunosuppression by Integrated Bioinformatics Analysis

Bioinformatic analysis for potential biological processes and key targets of heart failure-related stroke

Identification of vasospasm biomarkers for cerebral hemorrhage via bio-informatics analysis

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