Potential Biomarkers of Acute Myocardial Infarction Based on Co-Expression Network Analysis

2020 ◽  
Author(s):  
Zhiwen Ding ◽  
Zhaohui Hu ◽  
Xiangjun Ding ◽  
Yuyao Ji ◽  
Guiyuan li ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Network Analysis ◽  
Pearson Correlation ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Control Group ◽  
Hub Genes ◽  
And Control ◽  
Potential Biomarkers

Abstract Background: Acute myocardial infarction (AMI) is a common cause of death in many countries. Analyzing the potential biomarkers of AMI is crucial to understanding the molecular mechanism of disease. However, specific diagnostic biomarkers have not been fully elucidated, and candidate regulatory targets for AMI have not been determined.Methods: In this study, AMI gene chip data GSE48060, blood samples from normal cardiac function controls (n = 21) and AMI patients (n = 26) were downloaded from Gene Expression Omnibus. The differentially expressed genes (DEGs) of AMI and control group were identified with Online tool GEO2R. the genes co-expressed with were found. The co-expression network of DEGs was analyzed by calculating the Pearson correlation coefficient of all gene pairs, MR screening and cutoff threshold screening. Then, GO database was used to analyze the function and pathway enrichment of genes in the most important modules. KEGG DISEASE and BioCyc were used to analyze the hub gene in the module to determine important sub-pathways. In addition, the expression of hub genes were certified by RT-qPCR in AMI and control specimens.Results: This study identified 52 DEGs, including 26 up-regulated genes and 26 down-regulated genes. Co-expression network analysis of 52 DEGs revealed that there are mainly three up-regulated genes (AKR1C3, RPS24 and P2RY12) and three down-regulated genes (ACSL1, B3GNT5 and MGAM) as key hub genes in the co-expression network. Furthermore, GO enrichment analysis was performed on all AMI co-expression network genes and found to be functionally enriched mainly in RAGE receptor binding and negative regulation of T cell cytokine production. In addition, through KEGG DISEASE and BioCyc analysis, the functions of genes RPS24 and P2RY12 were enriched in cardiovascular diseases, AKR1C3 was enriched in cardenolide biosynthesis, MGAM was enriched in glycogenolysis, B3GNT5 was enriched in glycosphingolipids biosynthesis, and ACSL1 enriched in icosapentaenoate biosynthesis II. Conclusion: The hub genes AKR1C3, RPS24, P2RY12, ACSL1, B3GNT5 and MGAM are potential targets of AMI and have potential application value in the diagnosis of AMI.

scholarly journals Potential biomarkers of acute myocardial infarction based on co‑expression network analysis

2021 ◽  
Vol 23 (2) ◽  
Author(s):  
Zhaohui Hu ◽  
Ruhui Liu ◽  
Hairong Hu ◽  
Xiangjun Ding ◽  
Yuyao Ji ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Network Analysis ◽  
Potential Biomarkers

Depression in Acute Coronary Syndrome

2003 ◽  
Vol 93 (3_suppl) ◽  
pp. 1105-1108 ◽  
Author(s):  
Damir Kozmar ◽  
Katija Čatipović-Veselica ◽  
Andrea Galić ◽  
Jasna Habek
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Acute Coronary Syndrome ◽  
Ventricular Fibrillation ◽  
Good Prognosis ◽  
Left Ventricular ◽  
Control Group ◽  
Coronary Syndrome ◽  
Mean Differences ◽  
And Control

This study examined the prevalence of depression based on scores of 200 patients with acute coronary syndrome on the Emotion Profile Index of Plutchik and its relationship with the type of acute coronary syndrome and the severity of ischemic heart disease. Patients with acute coronary syndrome scored higher on depression than the control group. There was no difference in scores on Depression by type of acute coronary syndrome and no significant mean differences on Depression for patients with and without left ventricular failure. Patients with acute myocardial infarction and ventricular fibrillation scored lower on Depression than other patients with acute myocardial infarction and control group. This study supports the view that patients with acute myocardial infarction and ventricular fibrillation and lower scores on Depression have good prognosis during hospitalization and maybe for the long term.

scholarly journals Ten genes are considered as potential biomarkers for the diagnosis of dermatomyositis

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260511
Author(s):  
Lu Xiao ◽  
Wei Xiao ◽  
Shudian Lin
Keyword(s):  
Characteristic Curve ◽  
Area Under The Curve ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Type I ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Healthy Control ◽  
Potential Biomarkers

Objective This study aimed to identify the biomarkers and mechanisms for dermatomyositis (DM) progression at the transcriptome level through a combination of microarray and bioinformatic analyses. Method Microarray datasets for skeletal muscle of DM and healthy control (HC) were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified by using GEO2R. Enrichment analyses were performed to understand the functions and enriched pathways of DEGs. A protein–protein interaction network was constructed to identify hub genes. The top 10 hub genes were validated by other GEO datasets. The diagnostic accuracy of the top 10 hub genes for DM was evaluated using the area under the curve of the receiver operating characteristic curve. Result A total of 63 DEGs were identified between 10 DM samples and 9 HC samples. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis indicated that DEGs are mostly enriched in response to virus, defense response to virus, and type I interferon signaling pathway. 10 hub genes and 3 gene cluster modules were identified by Cytoscape. The identified hub genes were verified by GSE1551 and GSE11971 datasets and proven to be potential biomarkers for the diagnosis of DM. Conclusion Our work identified 10 valuable genes as potential biomarkers for the diagnosis of DM and explored the potential underlying molecular mechanism of the disease.

scholarly journals Identification of Infertility-Associated Topologically Important Genes Using Weighted Co-expression Network Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Jingni Wu ◽  
Xiaomeng Xia ◽  
Ye Hu ◽  
Xiaoling Fang ◽  
Sandra Orsulic
Keyword(s):  
Network Analysis ◽  
Topological Analysis ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Messenger Rnas ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Highly Correlated

Endometriosis has been associated with a high risk of infertility. However, the underlying molecular mechanism of infertility in endometriosis remains poorly understood. In our study, we aimed to discover topologically important genes related to infertility in endometriosis, based on the structure network mining. We used microarray data from the Gene Expression Omnibus (GEO) database to construct a weighted gene co-expression network for fertile and infertile women with endometriosis and to identify gene modules highly correlated with clinical features of infertility in endometriosis. Additionally, the protein–protein interaction network analysis was used to identify the potential 20 hub messenger RNAs (mRNAs) while the network topological analysis was used to identify nine candidate long non-coding RNAs (lncRNAs). Functional annotations of clinically significant modules and lncRNAs revealed that hub genes might be involved in infertility in endometriosis by regulating G protein-coupled receptor signaling (GPCR) activity. Gene Set Enrichment Analysis showed that the phospholipase C-activating GPCR signaling pathway is correlated with infertility in patients with endometriosis. Taken together, our analysis has identified 29 hub genes which might lead to infertility in endometriosis through the regulation of the GPCR network.

scholarly journals PIK3R1, SPNB2, and CRYAB as Potential Biomarkers for Patients with Diabetes and Developing Acute Myocardial Infarction

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yue Zheng ◽  
Yuheng Lang ◽  
Zhenchang Qi ◽  
Wenqing Gao ◽  
Xiaomin Hu ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Young Patients ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Venn Diagram ◽  
Hub Genes ◽  
Ventricular Ejection Fraction ◽  
Pathway Analyses ◽  
Potential Biomarkers

Background. Young patients with type 2 diabetes mellitus (DM) and acute myocardial infarction (AMI) have high long-term all-cause and cardiovascular mortality rates. We aimed to investigate the differentially expressed genes (DEGs) that might be potential targets for DM patients with AMI. Methods. Gene datasets GSE775, GSE19322, and GSE97494 were meta-analyzed to obtain DEGs of the left ventricle myocardium in infarcted mice. Gene datasets including GSE3313, GSE10617, and GSE136948 were meta-analyzed to identify DEGs in diabetes mice. A Venn diagram was used to obtain the overlapping DEGs. KEGG and GO pathway analyses were performed, and hub genes were obtained. Pivotal miRNAs were predicted and validated using the miRNA dataset in GSE114695. To investigate the cardiac function of the screened genes, a MI mouse model was constructed; echocardiogram, qPCR, and ELISA of hub genes were performed; ELISA of hub genes in human blood samples was also utilized. Results. A total of 67 DEGs were identified, which may be potential biomarkers for patients with DM and AMI. GO and KEGG pathway analyses were performed, which were mainly enriched in response to organic cyclic compound and PI3K-Akt signaling pathway. The expression of PIK3R1 and SPNB2 increased in the MI group and was negatively correlated to left ventricular ejection fraction (LVEF), whereas that of CRYAB decreased and was positively correlated to LVEF. Patients with high CRYAB expression demonstrated a short hospital stay and the area under the curves of the three protein levels before and after treatment were 0.964, 0.982, and 0.918, suggesting that PIK3R1, SPNB2, and CRYAB may be diagnostic and prognostic biomarkers for the diabetes patients with AMI. Conclusion. The screened hub genes, PIK3R1, SPNB2, and CRYAB, were validated as credible molecular biomarkers and may provide a novel therapy for diabetic cardiac diseases with increased proteotoxic stress.

scholarly journals BPI and KIR6.1 as significant hub genes for vein graft restenosis

2020 ◽  
Vol 48 (11) ◽  
pp. 030006052096933
Author(s):  
Yun-peng Bai ◽  
Bo-chen Yao ◽  
Mei Wang ◽  
Xian-kun Liu ◽  
Xiao-long Zhu ◽  
...  
Keyword(s):  
Vein Graft ◽  
Area Under The Curve ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Control Group ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Protein Protein Interaction

Background Vein graft restenosis (VGR), which appears to be caused by dyslipidemia following vascular transplantation, seriously affects the prognosis and long-term quality of life of patients. Methods This study analyzed the genetic data of restenosis (VGR group) and non-stenosis (control group) vessels from patients with coronary heart disease post-vascular transplantation and identified hub genes that might be responsible for its occurrence. GSE110398 was downloaded from the Gene Expression Omnibus database. A repeatability test for the GSE110398 dataset was performed using R language. This included the identification of differentially expressed genes (DEGs), enrichment analysis via Metascape software, pathway enrichment analysis, and construction of a protein–protein interaction network and a hub gene network. Results Twenty-four DEGs were identified between VGR and control groups. The four most important hub genes ( KIR6.1, PCLP1, EDNRB, and BPI) were identified, and Pearson’s correlation coefficient showed that KIR6.1 and BPI were significantly correlated with VGR. KIR6.1 could also sensitively predict VGR (0.9 < area under the curve ≤1). Conclusion BPI and KIR6.1 were differentially expressed in vessels with and without stenosis after vascular transplantation, suggesting that these genes or their encoded proteins may be involved in the occurrence of VGR.

scholarly journals Based on bioinformatics analysis, NF1, BTRC and MAPK14 are the potential biomarkers in the progression of osteoarthritis synovitis

2021 ◽  
Author(s):  
Mingyi Yang ◽  
Yani Su ◽  
Yao Ma ◽  
Yirixiati Aihaiti ◽  
Peng Xu
Keyword(s):  
Differentially Expressed Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Hub Gene ◽  
Potential Biomarkers

Abstract Objective: To study the potential biomarkers and related pathways in osteoarthritis (OA) synovial lesions, and to provide theoretical basis and research directions for the pathogenesis and treatment of OA. Methods: Download the microarray data sets GSE12021 and GSE82107 from Gene Expression Omnibus. GEO2R recognizes differentially expressed genes. Perform functional enrichment analysis of differentially expressed genes and construct protein-protein interaction network. Cytoscape performs module analysis and enrichment analysis of top-level modules. Further identify the Hub gene and perform functional enrichment analysis. TargetScan, miRDB and miRWalk three databases predict the target miRNAs of Hub gene and identify key miRNAs. Results: Finally, 10 Hub genes and 17 key miRNAs related to the progression of OA synovitis were identified. NF1, BTRC and MAPK14 may play a vital role in OA synovial disease. Conclusion: The Hub genes and key miRNAs discovered in this study may be potential biomarkers in the development of OA synovitis, and provide research methods and target basis for the pathogenesis and treatment of OA.

scholarly journals Identification of 10 Hub genes related to the progression of colorectal cancer by co-expression analysis

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9633
Author(s):  
Jie Meng ◽  
Rui Su ◽  
Yun Liao ◽  
Yanyan Li ◽  
Ling Li
Keyword(s):  
Colorectal Cancer ◽  
Network Analysis ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Data Sets ◽  
Ppi Network ◽  
Hub Genes ◽  
Normal Tissues ◽  
Go Enrichment ◽  
Go Enrichment Analysis

Background Colorectal cancer (CRC) is the third most common cancer in the world. The present study is aimed at identifying hub genes associated with the progression of CRC. Method The data of the patients with CRC were obtained from the Gene Expression Omnibus (GEO) database and assessed by weighted gene co-expression network analysis (WGCNA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses performed in R by WGCNA, several hub genes that regulate the mechanism of tumorigenesis in CRC were identified. Differentially expressed genes in the data sets GSE28000 and GSE42284 were used to construct a co-expression network for WGCNA. The yellow, black and blue modules associated with CRC level were filtered. Combining the co-expression network and the PPI network, 15 candidate hub genes were screened. Results After validation using the TCGA-COAD dataset, a total of 10 hub genes (MT1X, MT1G, MT2A, CXCL8, IL1B, CXCL5, CXCL11, IL10RA, GZMB, KIT) closely related to the progression of CRC were identified. The expressions of MT1G, CXCL8, IL1B, CXCL5, CXCL11 and GZMB in CRC tissues were higher than normal tissues (p-value < 0.05). The expressions of MT1X, MT2A, IL10RA and KIT in CRC tissues were lower than normal tissues (p-value < 0.05). Conclusions By combinating with a series of methods including GO enrichment analysis, KEGG pathway analysis, PPI network analysis and gene co-expression network analysis, we identified 10 hub genes that were associated with the progression of CRC.

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