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Zhao T, Zeng J, Zhang R, Fan W, Guan Q, Wang H, Pu L, Jiang Y, Yang H, Wang X, Han L. Serum Olink Proteomics-Based Identification of Protein Biomarkers Associated with the Immune Response in Ischemic Stroke. J Proteome Res 2024; 23:1118-1128. [PMID: 38319990 DOI: 10.1021/acs.jproteome.3c00885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
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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Affiliation(s)
- Tian Zhao
- Department of Clinical Epidemiology, Ningbo No. 2 Hospital, Ningbo 315000, China
- Center for Cardiovascular and Cerebrovascular Epidemiology and Translational Medicine, Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo 315000, China
| | - Jingjing Zeng
- Department of Clinical Epidemiology, Ningbo No. 2 Hospital, Ningbo 315000, China
- Center for Cardiovascular and Cerebrovascular Epidemiology and Translational Medicine, Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo 315000, China
| | - Ruijie Zhang
- Department of Clinical Epidemiology, Ningbo No. 2 Hospital, Ningbo 315000, China
- Center for Cardiovascular and Cerebrovascular Epidemiology and Translational Medicine, Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo 315000, China
| | - Weinv Fan
- Department of Neurology, Ningbo No.2 Hospital, Ningbo 315000, China
| | - Qiongfeng Guan
- Department of Neurology, Ningbo No.2 Hospital, Ningbo 315000, China
| | - Han Wang
- Department of Clinical Epidemiology, Ningbo No. 2 Hospital, Ningbo 315000, China
- Center for Cardiovascular and Cerebrovascular Epidemiology and Translational Medicine, Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo 315000, China
| | - Liyuan Pu
- Department of Clinical Epidemiology, Ningbo No. 2 Hospital, Ningbo 315000, China
- Center for Cardiovascular and Cerebrovascular Epidemiology and Translational Medicine, Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo 315000, China
| | - Yannan Jiang
- Department of Clinical Epidemiology, Ningbo No. 2 Hospital, Ningbo 315000, China
- Center for Cardiovascular and Cerebrovascular Epidemiology and Translational Medicine, Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo 315000, China
| | - Huiqun Yang
- Department of Clinical Epidemiology, Ningbo No. 2 Hospital, Ningbo 315000, China
- Center for Cardiovascular and Cerebrovascular Epidemiology and Translational Medicine, Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo 315000, China
| | - Xiaokun Wang
- Department of Neurology, the Second Affiliated Hospital, Harbin Medical University, Harbin 150081, China
| | - Liyuan Han
- Department of Clinical Epidemiology, Ningbo No. 2 Hospital, Ningbo 315000, China
- Center for Cardiovascular and Cerebrovascular Epidemiology and Translational Medicine, Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo 315000, China
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Zhang H, Sun J, Zou P, Huang Y, Yang Q, Zhang Z, Luo P, Jiang X. Identification of hypoxia- and immune-related biomarkers in patients with ischemic stroke. Heliyon 2024; 10:e25866. [PMID: 38384585 PMCID: PMC10878920 DOI: 10.1016/j.heliyon.2024.e25866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 01/26/2024] [Accepted: 02/05/2024] [Indexed: 02/23/2024] Open
Abstract
Background The immune microenvironment and hypoxia play crucial roles in the pathophysiology of ischemic stroke (IS). Hence, in this study, we aimed to identify hypoxia- and immune-related biomarkers in IS. Methods The IS microarray dataset GSE16561 was examined to determine differentially expressed genes (DEGs) utilizing bioinformatics-based analysis. The intersection of hypoxia-related genes and DEGs was conducted to identify differentially expressed hypoxia-related genes (DEHRGs). Then, using weighted correlation network analysis (WGCNA), all of the genes in GSE16561 dataset were examined to create a co-expression network, and module-clinical trait correlations were examined for the purpose of examining the genes linked to immune cells. The immune-related DEHRGs were submitted to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. A protein-protein interaction (PPI) network was constructed by Cytoscape plugin MCODE, in order to extract hub genes. The miRNet was used to predict hub gene-related transcription factors (TFs) and miRNAs. Finally, a diagnostic model was developed by least absolute shrinkage and selection operator (LASSO) logistic regression. Results Between the control and IS samples, 4171 DEGs were found. Thereafter, the intersection of hypoxia-related genes and DEGs was conducted to obtain 45 DEHRGs. Ten significantly differentially infiltrated immune cells were found-namely, CD56dim natural killer cells, activated CD8 T cells, activated dendritic cells, activated B cells, central memory CD8 T cells, effector memory CD8 T cells, natural killer cells, gamma delta T cells, plasmacytoid dendritic cells, and neutrophils-between IS and control samples. Subsequently, we identified 27 immune-related DEHRGs through the intersection of DEHRGs and genes in important modules of WGCNA. The immune-related DEHRGs were primarily enriched in response to hypoxia, cellular polysaccharide metabolic process, response to decreased oxygen levels, polysaccharide metabolic process, lipid and atherosclerosis, and HIF-1 signaling pathway H. Using MCODE, FOS, DDIT3, DUSP1, and NFIL3 were found to be hub genes. In the validation cohort and training set, the AUC values of the diagnostic model were 0.9188034 and 0.9395085, respectively. Conclusion In brief, we identified and validated four hub genes-FOS, DDIT3, DUSP1, and NFIL3-which might be involved in the pathological development of IS, potentially providing novel perspectives for the diagnosis and treatment of IS.
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Affiliation(s)
- Haofuzi Zhang
- Department of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Jidong Sun
- Department of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Peng Zou
- Department of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Yutao Huang
- Department of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Qiuzi Yang
- Department of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Zhuoyuan Zhang
- Department of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China
- Biochemistry and Molecular Biology, College of Life Science, Northwest University, Xi'an, China
| | - Peng Luo
- Department of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Xiaofan Jiang
- Department of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China
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Wang M, Gao Y, Chen H, Shen Y, Cheng J, Wang G. Bioinformatics strategies to identify differences in molecular biomarkers for ischemic stroke and myocardial infarction. Medicine (Baltimore) 2023; 102:e35919. [PMID: 37986378 PMCID: PMC10659606 DOI: 10.1097/md.0000000000035919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 10/11/2023] [Accepted: 10/12/2023] [Indexed: 11/22/2023] Open
Abstract
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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Affiliation(s)
- Min Wang
- School of Clinical Medicine, Dali University, Dali, Yunnan, P.R. China
| | - Yuan Gao
- School of Clinical Medicine, Zhengzhou University, Zhengzhou, Henan, P.R. China
| | - Huaqiu Chen
- Xichang People’s Hospital, Xichang, Sichuan, P.R. China
| | - Ying Shen
- The First Hospital of Liangshan, Xichang, Sichuan, P.R. China
| | - Jianjie Cheng
- The First Affiliated Hospital of Dali University, Yunnan, P.R. China
| | - Guangming Wang
- School of Clinical Medicine, Dali University, Dali, Yunnan, P.R. China
- Center of Genetic Testing, The First Affiliated Hospital of Dali University, Dali, Yunnan, P.R. China
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Braadt L, Naumann M, Freuer D, Schmitz T, Linseisen J, Ertl M. Novel inflammatory biomarkers associated with stroke severity: results from a cross-sectional stroke cohort study. Neurol Res Pract 2023; 5:31. [PMID: 37468969 DOI: 10.1186/s42466-023-00259-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 06/20/2023] [Indexed: 07/21/2023] Open
Abstract
BACKGROUND Stroke is a leading cause of mortality and disability worldwide and its occurrence is expected to increase in the future. Blood biomarkers have proven their usefulness in identification and monitoring of the disease. Stroke severity is a major factor for estimation of prognosis and risk of recurrent events, but knowledge on respective blood biomarkers is still scarce. Stroke pathophysiology comprises a multitude of ischemia-induced inflammatory and immune mediated responses. Therefore, the assessment of an immune-related panel in correlation with stroke severity seems promising. METHODS In the present cross-sectional evaluation, a set of 92 blood biomarkers of a standardized immune panel were gathered (median 4.6 days after admission) and related to stroke severity measures, assessed at hospital admission of acute stroke patients. Multivariable logistic regression models were used to determine associations between biomarkers and modified Rankin Scale (mRS), linear regression models were used for associations with National Institute of Health Stroke Scale. RESULTS 415 patients (mean age 69 years; 41% female) were included for biomarker analysis. C-type lectin domain family 4 member G (CLEC4G; OR = 2.89, 95% CI [1.49; 5.59], padj = 0.026, Cytoskeleton-associated protein 4 (CKAP4; OR = 2.38, 95% CI [1.43; 3.98], padj = 0.019), and Interleukin-6 (IL-6) (IL6; OR = 1.97, 95% CI [1.49; 2.62], padj < 0.001) were positively associated with stroke severity measured by mRS, while Lymphocyte antigen 75 (LY75; OR = 0.37, 95% CI [0.19; 0.73], padj = 0.049) and Integrin alpha-11 (ITGA11 OR = 0.24, 95% CI [0.14, 0.40] padj < 0.001) were inversely associated. When investigating the relationships with the NIHSS, IL-6 (β = 0.23, 95% CI [0.12, 0.33] padj = 0.001) and ITGA11 (β = - 0.60, 95% CI [- 0.83, - 0.37] padj < 0.001) were significantly associated. CONCLUSIONS Higher relative concentrations of plasma CLEC4G, CKAP4, and IL-6 were associated with higher stroke severity, whereas LY75 and ITGA11 showed an inverse association. Future research might show a possible use as therapeutic targets and application in individual risk assessments.
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Affiliation(s)
- Lino Braadt
- Department of Neurology and Clinical Neurophysiology, University Hospital Augsburg, Augsburg, Germany.
| | - Markus Naumann
- Department of Neurology and Clinical Neurophysiology, University Hospital Augsburg, Augsburg, Germany
| | - Dennis Freuer
- Epidemiology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Timo Schmitz
- Epidemiology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Jakob Linseisen
- Epidemiology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Michael Ertl
- Department of Neurology and Clinical Neurophysiology, University Hospital Augsburg, Augsburg, Germany
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Chen JM, Li XL, Yang Ye, Xu SM, Chen QF, Xu JW. Competing endogenous RNA network analysis of the molecular mechanisms of ischemic stroke. BMC Genomics 2023; 24:67. [PMID: 36755220 PMCID: PMC9906963 DOI: 10.1186/s12864-023-09163-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 02/01/2023] [Indexed: 02/10/2023] Open
Abstract
BACKGROUND Ischemic stroke (IS) is a serious neurological disease that largely results in long-term disability and death. Extensive evidence has indicated that the activation of inflammation and ferroptosis significantly contribute to the development of IS pathology. However, the underlying molecular mechanism remains unclear. In this study, we aimed to identify potential biomarkers associated with IS through the construction of a competing endogenous RNA (ceRNA) network and to investigate the possible inflammatory and ferroptosis-related molecular mechanisms. RESULTS We identified 178 differentially expressed target messenger RNAs (DETmRNAs) associated with IS. As revealed through enrichment analysis, the DEmRNAs were mainly enriched in the inflammatory signaling pathways and also related to ferroptosis mechanism. The CIBERSORT algorithm showed immune infiltration landscapes in which the naïve B cells, naïve T cells, and monocytes had statistically different numbers in the cerebral infarction group compared with the control group. A ceRNA network was constructed in this study involving 44 long non-coding RNAs (lncRNAs), 15 microRNAs (miRNAs), and 160 messenger RNAs (mRNAs). We used the receiver operating characteristic (ROC) analysis to identify three miRNAs (miR-103a-3p, miR-140-3p, and miR-17-5p), one mRNA (TLR4), and one lncRNA (NEAT1) as the potential key biomarkers of the ceRNA network. The key mRNA and lncRNA were shown to be highly related to the ferroptosis mechanism of IS. The expression of these key biomarkers was also further validated by a method of quantitative real-time polymerase chain reaction in SH-SY5Y cells, and the validated results were consistent with the findings predicted by bioinformatics. CONCLUSION Our results suggest that the ceRNA network may exert an important role in the inflammatory and ferroptosis molecular mechanisms of IS, providing new insight into therapeutic IS targets.
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Affiliation(s)
- Jian-Min Chen
- grid.412683.a0000 0004 1758 0400Department of Rehabilitation Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian China ,grid.412594.f0000 0004 1757 2961Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi China
| | - Xiao-Lu Li
- grid.412594.f0000 0004 1757 2961Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi China
| | - Yang Ye
- grid.412594.f0000 0004 1757 2961Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi China
| | - Sen-Ming Xu
- grid.412594.f0000 0004 1757 2961Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi China
| | - Qing-Fa Chen
- Department of Rehabilitation, Fujian Medical University Union Hospital, Fuzhou, Fujian, China.
| | - Jian-Wen Xu
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
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Zhang X, Wang Y, Dong B, Jiang Y, Liu D, Xie K, Yu Y. Expression pattern and clinical value of Key RNA methylation modification regulators in ischemic stroke. Front Genet 2022; 13:1009145. [PMID: 36263422 PMCID: PMC9574037 DOI: 10.3389/fgene.2022.1009145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 09/16/2022] [Indexed: 11/13/2022] Open
Abstract
Ischemic stroke (IS) is one of the major causes of death and disability worldwide, and effective diagnosis and treatment methods are lacking. RNA methylation, a common epigenetic modification, plays an important role in disease progression. However, little is known about the role of RNA methylation modification in the regulation of IS. The aim of this study was to investigate RNA methylation modification patterns and immune infiltration characteristics in IS through bioinformatics analysis. We downloaded gene expression profiles of control and IS model rat brain tissues from the Gene Expression Omnibus database. IS profiles were divided into two subtypes based on RNA methylation regulators, and functional enrichment analyses were conducted to determine the differentially expressed genes (DEGs) between the subtypes. Weighted gene co-expression network analysis was used to explore co-expression modules and genes based on DEGs. The IS clinical diagnosis model was successfully constructed and four IS characteristic genes (GFAP, GPNMB, FKBP9, and CHMP5) were identified, which were significantly upregulated in IS samples. Characteristic genes were verified by receiver operating characteristic curve and real-time quantitative PCR analyses. The correlation between characteristic genes and infiltrating immune cells was determined by correlation analysis. Furthermore, GPNMB was screened using the protein-protein interaction network, and its regulatory network and the potential therapeutic drug chloroquine were predicted. Our finding describes the expression pattern and clinical value of key RNA methylation modification regulators in IS and novel diagnostic and therapeutic targets of IS from a new perspective.
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Affiliation(s)
- Xinyue Zhang
- Department of Anesthesiology, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Institute of Anesthesiology, Tianjin, China
| | - Yuanlin Wang
- Department of Anesthesiology, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Institute of Anesthesiology, Tianjin, China
| | - Beibei Dong
- Department of Anesthesiology, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Institute of Anesthesiology, Tianjin, China
| | - Yi Jiang
- Department of Anesthesiology, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Institute of Anesthesiology, Tianjin, China
| | - Dan Liu
- School of Medicine, Nankai University, Tianjin, China
| | - Keliang Xie
- Department of Anesthesiology, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Institute of Anesthesiology, Tianjin, China
- Department of Critical Care Medicine, Tianjin Medical University General Hospital, Tianjin, China
| | - Yonghao Yu
- Department of Anesthesiology, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Institute of Anesthesiology, Tianjin, China
- *Correspondence: Yonghao Yu,
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Xu J, Yang Y. 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. Front Cardiovasc Med 2021; 8:736497. [PMID: 34957234 PMCID: PMC8702808 DOI: 10.3389/fcvm.2021.736497] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 11/16/2021] [Indexed: 12/27/2022] Open
Abstract
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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Affiliation(s)
- Jing Xu
- State Key Laboratory of Cardiovascular Diseases, Fuwai Hospital and National Center for Cardiovascular Diseases, Beijing, China.,Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Yuejin Yang
- State Key Laboratory of Cardiovascular Diseases, Fuwai Hospital and National Center for Cardiovascular Diseases, Beijing, China.,Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
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