Study on potential differentially expressed genes in stroke by bioinformatics analysis

Yang, Xitong; Wang, Pengyu; Yan, Shanquan; Wang, Guangming

doi:10.1007/s10072-021-05470-1

Cited by 10 publications

(7 citation statements)

References 37 publications

(37 reference statements)

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“…CLEC4D is one of the hub genes that is differentially expressed between IS patients and controls 21 and may be involved in mir-27a-3p−CLEC4D regulation. 22 In addition, it was previously found that CLEC4D and CD163 are immune biomarkers for IS, and these genes were used to construct classification models based on a neural network algorithm. This also revealed that CLEC4D is strongly correlated with the proportion of neutrophils in sera of IS patients.…”

Section: ■ Discussionmentioning

confidence: 99%

Serum Olink Proteomics-Based Identification of Protein Biomarkers Associated with the Immune Response in Ischemic Stroke

Zhao,

Zeng,

Zhang

et al. 2024

J. Proteome Res.

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The immune response is considered essential for pathology of ischemic stroke (IS), but it remains unclear which immune response-related proteins exhibit altered expression in IS patients. Here, we used Olink proteomics to examine the expression levels of 92 immune response-related proteins in the sera of IS patients (n = 88) and controls (n = 88), and we found that 59 of these proteins were differentially expressed. Feature variables were screened from the differentially expressed proteins by the least absolute shrinkage and selection operator (LASSO) and the random forest and by determining whether their proteins had an area under the curve (AUC) greater than 0.8. Ultimately, we identified six potential protein biomarkers of IS, namely, MASP1, STC1, HCLS1, CLEC4D, PTH1R, and PIK3AP1, and established a logistic regression model that used these proteins to diagnose IS. The AUCs of the models in the internal validation and the test set were 0.962 (95% confidence interval (CI): 0.895−1.000) and 0.954 (95% CI: 0.884−1.000), respectively, and the same protein detection method was performed in an external independent validation set (AUC: 0.857 (95% CI: 0.801−0.913)). These proteins may play a role in immune regulation via the C-type lectin receptor signaling pathway, the PI3K−AKT signaling pathway, and the B-cell receptor signaling pathway.

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Section: ■ Discussionmentioning

confidence: 99%

Serum Olink Proteomics-Based Identification of Protein Biomarkers Associated with the Immune Response in Ischemic Stroke

Zhao,

Zeng,

Zhang

et al. 2024

J. Proteome Res.

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“…[39] hsa-miR-27a-3p and hsa-miR-335-5p were crucial in IS, whereas hsa-miR-34a-5p was crucial in MI. [40,41] The above information may reveal some unique mechanisms underlying the diseases, suggest new disease markers, and provide a possible basis for predicting IS and MI, as well as identify potential therapeutic targets. The prediction of drugs may have a role in disease prevention.…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics strategies to identify differences in molecular biomarkers for ischemic stroke and myocardial infarction

Wang,

Gao,

Chen

et al. 2023

Medicine

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Ischemic strokes (ISs) are commonly treated by intravenous thrombolysis using a recombinant tissue plasminogen activator; however, successful treatment can only occur within 3 hours after the stroke. Therefore, it is crucial to determine the causes and underlying molecular mechanisms, identify molecular biomarkers for early diagnosis, and develop precise preventive treatments for strokes. We aimed to clarify the differences in gene expression, molecular mechanisms, and drug prediction approaches between IS and myocardial infarction (MI) using comprehensive bioinformatics analysis. The pathogenesis of these diseases was explored to provide directions for future clinical research. The IS (GSE58294 and GSE16561) and MI (GSE60993 and GSE141512) datasets were downloaded from the Gene Expression Omnibus database. IS and MI transcriptome data were analyzed using bioinformatics methods, and the differentially expressed genes (DEGs) were screened. A protein–protein interaction network was constructed using the STRING database and visualized using Cytoscape, and the candidate genes with high confidence scores were identified using Degree, MCC, EPC, and DMNC in the cytoHubba plug-in. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were performed using the database annotation, visualization, and integrated discovery database. Network Analyst 3.0 was used to construct transcription factor (TF) – gene and microRNA (miRNA) – gene regulatory networks of the identified candidate genes. The DrugBank 5.0 database was used to identify gene–drug interactions. After bioinformatics analysis of IS and MI microarray data, 115 and 44 DEGS were obtained in IS and MI, respectively. Moreover, 8 hub genes, 2 miRNAs, and 3 TFs for IS and 8 hub genes, 13 miRNAs, and 2 TFs for MI were screened. The molecular pathology between IS and MI presented differences in terms of GO and KEGG enrichment pathways, TFs, miRNAs, and drugs. These findings provide possible directions for the diagnosis of IS and MI in the future.

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“…Integrin subunit alpha M (ITGAM) gene encodes Integrin αM (CD11b), which might promote the development and progression of abdominal aortic aneurysm via mediating the adhesion of endothelial cells and the transendothelial migration of circulating monocytes/macrophages (53). C-type lectin domain family 4 member D (CLEC4D) and mast cell expressed membrane protein 1 (MCEMP1) are identified to be potential prognostic and diagnostic biomarkers for ischemic stroke (54,55). Solute carrier family 2 member 3 (SLC2A3) is reported to correlate with platelet aggregation (56), syndromic congenital heart disease (57), and chronic thromboembolic pulmonary hypertension (58).…”

Section: Discussionmentioning

confidence: 99%

Integrated Gene Expression Profiling Analysis Reveals Potential Molecular Mechanisms and Candidate Biomarkers for Early Risk Stratification and Prediction of STEMI and Post-STEMI Heart Failure Patients

Yang

2021

Front. Cardiovasc. Med.

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Objective: To explore the molecular mechanism and search for the candidate differentially expressed genes (DEGs) with the predictive and prognostic potentiality that is detectable in the whole blood of patients with ST-segment elevation (STEMI) and those with post-STEMI HF.Methods: In this study, we downloaded GSE60993, GSE61144, GSE66360, and GSE59867 datasets from the NCBI-GEO database. DEGs of the datasets were investigated using R. Gene ontology (GO) and pathway enrichment were performed via ClueGO, CluePedia, and DAVID database. A protein interaction network was constructed via STRING. Enriched hub genes were analyzed by Cytoscape software. The least absolute shrinkage and selection operator (LASSO) logistic regression algorithm and receiver operating characteristics analyses were performed to build machine learning models for predicting STEMI. Hub genes for further validated in patients with post-STEMI HF from GSE59867.Results: We identified 90 upregulated DEGs and nine downregulated DEGs convergence in the three datasets (|log2FC| ≥ 0.8 and adjusted p < 0.05). They were mainly enriched in GO terms relating to cytokine secretion, pattern recognition receptors signaling pathway, and immune cells activation. A cluster of eight genes including ITGAM, CLEC4D, SLC2A3, BST1, MCEMP1, PLAUR, GPR97, and MMP25 was found to be significant. A machine learning model built by SLC2A3, CLEC4D, GPR97, PLAUR, and BST1 exerted great value for STEMI prediction. Besides, ITGAM and BST1 might be candidate prognostic DEGs for post-STEMI HF.Conclusions: We reanalyzed the integrated transcriptomic signature of patients with STEMI showing predictive potentiality and revealed new insights and specific prospective DEGs for STEMI risk stratification and HF development.

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Study on potential differentially expressed genes in stroke by bioinformatics analysis

Cited by 10 publications

References 37 publications

Serum Olink Proteomics-Based Identification of Protein Biomarkers Associated with the Immune Response in Ischemic Stroke

Serum Olink Proteomics-Based Identification of Protein Biomarkers Associated with the Immune Response in Ischemic Stroke

Bioinformatics strategies to identify differences in molecular biomarkers for ischemic stroke and myocardial infarction

Integrated Gene Expression Profiling Analysis Reveals Potential Molecular Mechanisms and Candidate Biomarkers for Early Risk Stratification and Prediction of STEMI and Post-STEMI Heart Failure Patients

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