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Qin H, Wang W, Hu L, Yu Z, Chen Y, Zhao Y, Liao Y, Yang R. New Insights into the Role of HMGB2 in ST-Segment Elevation Myocardial Infarction. Int J Gen Med 2023; 16:4181-4191. [PMID: 37727529 PMCID: PMC10506601 DOI: 10.2147/ijgm.s429761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Accepted: 08/31/2023] [Indexed: 09/21/2023] Open
Abstract
Background Ischemic heart disease is one of the leading causes of death in the world, of which ST-segment elevation myocardial infarction (STEMI) is an important type. Inappropriate activation and accumulation of platelets typically induced thrombosis, which may result in acute vessel occlusion and STEMI. Multiple cytokines have been shown to regulate platelet activation, but the relationship between HMGB2 and platelet activation has not been elucidated. Methods We collected peripheral blood of STEMI patients and healthy adults, and mass spectrometry analysis of platelet proteins was conducted. The "edgeR" package was used to identify the differentially expressed proteins (DEPs). The Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene ontology (GO) and Gene Set Enrichment Analysis (GSEA) were used to identify the significantly changed pathways. Western blot and ELISA were used to detect the expression of a high mobility group box 2 (HMGB2). Flow cytometric analysis and platelet aggregation rate were performed to evaluate the activation of platelets. Results We identified ALOX5, HIST1H1B, S100A11, HMGB2, and RPS15A were the top five up-regulated proteins by differential expression analysis. Western blot verified that the relative protein expression of HMGB2 in platelet was significantly higher in STEMI patients compared with control adults, and the results of ELISA indicated that the serum HMGB2 level increased and significantly correlated with neutrophil count in STEMI patients. Further investigation showed that the platelet aggregation induced by ADP, the activation of integrin αIIbβ3 and CD62P expression on platelet surface were all enhanced by the recombinant HMGB2 (rHMGB2). Conclusion In conclusion, HMGB2 may be the key molecule to regulate platelet activation in patients with STEMI, which may serve as a potential therapeutic target for STEMI.
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Affiliation(s)
- Hao Qin
- Department of Cardiovascular Medicine, the Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, 330006, People’s Republic of China
| | - Wenjun Wang
- Department of Respiratory Diseases, the Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, 330006, People’s Republic of China
| | - Longlong Hu
- Department of Cardiovascular Medicine, the Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, 330006, People’s Republic of China
| | - Zuozhong Yu
- Department of Cardiovascular Medicine, the Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, 330006, People’s Republic of China
| | - Yang Chen
- Department of Cardiovascular Medicine, the Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, 330006, People’s Republic of China
| | - Yuanbin Zhao
- Department of Cardiovascular Medicine, the Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, 330006, People’s Republic of China
| | - Yanhui Liao
- Department of Cardiovascular Medicine, the Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, 330006, People’s Republic of China
| | - Renqiang Yang
- Department of Cardiovascular Medicine, the Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, 330006, People’s Republic of China
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Xie J, Luo C, Mo B, Lin Y, Liu G, Wang X, Li L. Inflammation and Oxidative Stress Role of S100A12 as a Potential Diagnostic and Therapeutic Biomarker in Acute Myocardial Infarction. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2022; 2022:2633123. [PMID: 36062187 PMCID: PMC9436632 DOI: 10.1155/2022/2633123] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 07/20/2022] [Accepted: 08/09/2022] [Indexed: 12/12/2022]
Abstract
Acute myocardial infarction (AMI) is one of the most serious cardiovascular diseases with high morbidity and mortality. Numerous studies have indicated that S100A12 may has an essential role in the occurrence and development of AMI, and in-depth studies are currently lacking. The purpose of this study is to investigate the effect of S100A12 on inflammation and oxidative stress and to determine its clinical applicability in AMI. Here, AMI datasets used to explore the expression pattern of S100A12 in AMI were derived from the Gene Expression Omnibus (GEO) database. The pooled standard average deviation (SMD) was calculated to further determine S100A12 expression. The overlapping differentially expressed genes (DEGs) contained in all included datasets were recognized by the GEO2R tool. Then, functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, were carried out to determine the molecular function of overlapping DEGs. Gene set enrichment analysis (GSEA) was conducted to determine unrevealed mechanisms of S100A12. Summary receiver operating characteristic (SROC) curve analysis and receiver operating characteristic (ROC) curve analysis were carried out to identify the diagnostic capabilities of S100A12. Moreover, we screened miRNAs targeting S100A12 using three online databases (miRWalk, TargetScan, and miRDB). In addition, by comprehensively using enzyme-linked immunosorbent assay (ELISA), real-time quantitative PCR (RT-qPCR), Western blotting (WB) methods, etc., we used the AC16 cells to validate the expression and underlying mechanism of S100A12. In our study, five datasets related to AMI, GSE24519, GSE60993, GSE66360, GSE97320, and GSE48060 were included; 412 overlapping DEGs were identified. Protein-protein interaction (PPI) network and functional analyses showed that S100A12 was a pivotal gene related to inflammation and oxidative stress. Then, S100A12 overexpression was identified based on the included datasets. The pooled standard average deviation (SMD) also showed that S100A12 was upregulated in AMI (SMD = 1.36, 95% CI: 0.70-2.03, p = 0.024). The SROC curve analysis result suggested that S100A12 had remarkable diagnostic ability in AMI (AUC = 0.90, 95% CI: 0.87-0.92). And nine miRNAs targeting S100A12 were also identified. Additionally, the overexpression of S100A12 was further confirmed that it maybe promote inflammation and oxidative stress in AMI through comprehensive in vitro experiments. In summary, our study suggests that overexpressed S100A12 may be a latent diagnostic biomarker and therapeutic target of AMI that induces excessive inflammation and oxidative stress. Nine miRNAs targeting S100A12 may play a crucial role in AMI, but further studies are still needed. Our work provides a positive inspiration for the in-depth study of S100A12 in AMI.
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Affiliation(s)
- Jian Xie
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Guangxi Cardiovascular Institute, Nanning, 530021 Guangxi, China
| | - Changjun Luo
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Guangxi Cardiovascular Institute, Nanning, 530021 Guangxi, China
| | - Binhai Mo
- Department of Cardiology, The First People Hospital of Nanning & The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, 530016 Guangxi, China
| | - Yunhua Lin
- The First Clinical Medical College, Guangxi Medical University, Nanning 530021, China
| | - Guoqing Liu
- The First Clinical Medical College, Guangxi Medical University, Nanning 530021, China
| | - Xiantao Wang
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Guangxi Cardiovascular Institute, Nanning, 530021 Guangxi, China
| | - Lang Li
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Guangxi Cardiovascular Institute, Nanning, 530021 Guangxi, China
- Guangxi Key Laboratory of Precision Medicine in Cardio-Cerebrovascular Diseases Control and Prevention, Nanning, 530021 Guangxi, China
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Feng S, Li R, Zhou Q, Qu F, Hu W, Liu X. Bioinformatics analysis to identify potential biomarkers and therapeutic targets for ST-segment–elevation myocardial infarction-related ischemic stroke. Front Neurol 2022; 13:894289. [PMID: 36034287 PMCID: PMC9403764 DOI: 10.3389/fneur.2022.894289] [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: 03/11/2022] [Accepted: 07/18/2022] [Indexed: 11/28/2022] Open
Abstract
Background Acute myocardial infarction (AMI) is one of the major causes of mortality and disability worldwide, and ischemic stroke (IS) is a serious complication after AMI. In particular, patients with ST-segment–elevation myocardial infarction (STEMI) are more susceptible to IS. However, the interrelationship between the two disease mechanisms is not clear. Using bioinformatics tools, we investigated genes commonly expressed in patients with STEMI and IS to explore the relationship between these diseases, with the aim of uncovering the underlying biomarkers and therapeutic targets for STEMI-associated IS. Methods Differentially expressed genes (DEGs) related to STEMI and IS were identified through bioinformatics analysis of the Gene Expression Omnibus (GEO) datasets GSE60993 and GSE16561, respectively. Thereafter, we assessed protein-protein interaction networks, gene ontology term annotations, and pathway enrichment for DEGs using various prediction and network analysis methods. The predicted miRNAs targeting the co-expressed STEMI- and IS-related DEGs were also evaluated. Results We identified 210 and 29 DEGs in GSE60993 and GSE16561, respectively. CD8A, TLR2, TLR4, S100A12, and TREM1 were associated with STEMI, while the hubgenes, IL7R, CCR7, FCGR3B, CD79A, and ITK were implicated in IS. In addition, binding of the transcripts of the co-expressed DEGs MMP9, ARG1, CA4, CRISPLD2, S100A12, and GZMK to their corresponding predicted miRNAs, especially miR-654-5p, may be associated with STEMI-related IS. Conclusions STEMI and IS are related and MMP9, ARG1, CA4, CRISPLD2, S100A12, and GZMK genes may be underlying biomarkers involved in STEMI-related IS.
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Affiliation(s)
| | | | | | | | - Wei Hu
- *Correspondence: Xinfeng Liu
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Zhai H, Huang L, Gong Y, Liu Y, Wang Y, Liu B, Li X, Peng C, Li T. Human Plasma Transcriptome Implicates Dysregulated S100A12 Expression: A Strong, Early-Stage Prognostic Factor in ST-Segment Elevated Myocardial Infarction: Bioinformatics Analysis and Experimental Verification. Front Cardiovasc Med 2022; 9:874436. [PMID: 35722095 PMCID: PMC9200219 DOI: 10.3389/fcvm.2022.874436] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 05/02/2022] [Indexed: 11/13/2022] Open
Abstract
The ability of blood transcriptome analysis to identify dysregulated pathways and outcome-related genes following myocardial infarction remains unknown. Two gene expression datasets (GSE60993 and GSE61144) were downloaded from Gene Expression Omnibus (GEO) Datasets to identify altered plasma transcriptomes in patients with ST-segment elevated myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention. GEO2R, Gene Ontology/Kyoto Encyclopedia of Genes and Genomes annotations, protein–protein interaction analysis, etc., were adopted to determine functional roles and regulatory networks of differentially expressed genes (DEGs). Dysregulated expressomes were verified at transcriptional and translational levels by analyzing the GSE49925 dataset and our own samples, respectively. A total of 91 DEGs were identified in the discovery phase, consisting of 15 downregulated genes and 76 upregulated genes. Two hub modules consisting of 12 hub genes were identified. In the verification phase, six of the 12 hub genes exhibited the same variation patterns at the transcriptional level in the GSE49925 dataset. Among them, S100A12 was shown to have the best discriminative performance for predicting in-hospital mortality and to be the only independent predictor of death during follow-up. Validation of 223 samples from our center showed that S100A12 protein level in plasma was significantly lower among patients who survived to discharge, but it was not an independent predictor of survival to discharge or recurrent major adverse cardiovascular events after discharge. In conclusion, the dysregulated expression of plasma S100A12 at the transcriptional level is a robust early prognostic factor in patients with STEMI, while the discrimination power of the protein level in plasma needs to be further verified by large-scale, prospective, international, multicenter studies.
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Affiliation(s)
- Hu Zhai
- Department of Heart Center, The Tianjin Third Central Hospital, Tianjin, China
- Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Tianjin, China
- Artificial Cell Engineering Technology Research Center, Tianjin, China
- Tianjin Institute of Hepatobiliary Disease, Tianjin, China
- *Correspondence: Hu Zhai,
| | - Lei Huang
- Department of Heart Center, The Tianjin Third Central Hospital, Tianjin, China
- Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Tianjin, China
- Artificial Cell Engineering Technology Research Center, Tianjin, China
- Tianjin Institute of Hepatobiliary Disease, Tianjin, China
| | - Yijie Gong
- The Third Central Clinical College, Tianjin Medical University, Tianjin, China
| | - Yingwu Liu
- Department of Heart Center, The Tianjin Third Central Hospital, Tianjin, China
- Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Tianjin, China
| | - Yu Wang
- Department of Heart Center, The Tianjin Third Central Hospital, Tianjin, China
- Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Tianjin, China
| | - Bojiang Liu
- Department of Heart Center, The Tianjin Third Central Hospital, Tianjin, China
- Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Tianjin, China
| | - Xiandong Li
- Department of Laboratory Medicine, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Chunyan Peng
- Department of Laboratory Medicine, Taihe Hospital, Hubei University of Medicine, Shiyan, China
- Hubei Key Laboratory of Wudang Local Chinese Medicine Research, Hubei University of Medicine, Shiyan, China
- Hubei Key Laboratory of Embryonic Stem Cell Research, Hubei University of Medicine, Shiyan, China
- Chunyan Peng,
| | - Tong Li
- Department of Heart Center, The Tianjin Third Central Hospital, Tianjin, China
- Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Tianjin, China
- Artificial Cell Engineering Technology Research Center, Tianjin, China
- Tianjin Institute of Hepatobiliary Disease, Tianjin, China
- Tong Li,
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Wu Y, Chen H, Li L, Zhang L, Dai K, Wen T, Peng J, Peng X, Zheng Z, Jiang T, Xiong W. Construction of Novel Gene Signature-Based Predictive Model for the Diagnosis of Acute Myocardial Infarction by Combining Random Forest With Artificial Neural Network. Front Cardiovasc Med 2022; 9:876543. [PMID: 35694667 PMCID: PMC9174464 DOI: 10.3389/fcvm.2022.876543] [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: 02/15/2022] [Accepted: 04/26/2022] [Indexed: 11/19/2022] Open
Abstract
Background Acute myocardial infarction (AMI) is one of the most common causes of mortality around the world. Early diagnosis of AMI contributes to improving prognosis. In our study, we aimed to construct a novel predictive model for the diagnosis of AMI using an artificial neural network (ANN), and we verified its diagnostic value via constructing the receiver operating characteristic (ROC). Methods We downloaded three publicly available datasets (training sets GSE48060, GSE60993, and GSE66360) from Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified between 87 AMI and 78 control samples. We applied the random forest (RF) and ANN algorithms to further identify novel gene signatures and construct a model to predict the possibility of AMI. Besides, the diagnostic value of our model was further validated in the validation sets GSE61144 (7 AMI patients and 10 controls), GSE34198 (49 AMI patients and 48 controls), and GSE97320 (3 AMI patients and 3 controls). Results A total of 71 DEGs were identified, of which 68 were upregulated and 3 were downregulated. Firstly, 11 key genes in 71 DEGs were screened with RF classifier for the classification of AMI and control samples. Then, we calculated the weight of each key gene using ANN. Furthermore, the diagnostic model was constructed and named neuralAMI, with significant predictive power (area under the curve [AUC] = 0.980). Finally, our model was validated with the independent datasets GSE61144 (AUC = 0.900), GSE34198 (AUC = 0.882), and GSE97320 (AUC = 1.00). Conclusion Machine learning was used to develop a reliable predictive model for the diagnosis of AMI. The results of our study provide potential gene biomarkers for early disease screening.
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Affiliation(s)
- Yanze Wu
- Department of Cardiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
- Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Hui Chen
- Department of Cardiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Lei Li
- Department of Cardiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Liuping Zhang
- Department of Cardiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Kai Dai
- Department of Cardiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Tong Wen
- Department of Cardiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Jingtian Peng
- Department of Cardiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xiaoping Peng
- Department of Cardiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zeqi Zheng
- Department of Cardiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Ting Jiang
- Department of Hospital Infection Control, The First Affiliated Hospital of Nanchang University, Nanchang, China
- *Correspondence: Ting Jiang,
| | - Wenjun Xiong
- Department of Cardiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
- Wenjun Xiong,
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