Potential biomarkers of acute myocardial infarction based on weighted gene co-expression network analysis

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Background

Acute myocardial infarction (AMI) is the common cause of mortality in developed countries. The feasibility of whole-genome gene expression analysis to identify outcome-related genes and dysregulated pathways remains unknown. Molecular marker such as BNP, CRP and other serum inflammatory markers have got the notice at this point. However, these biomarkers exhibit elevated levels in patients with thyroid disease, renal failure and congestive heart failure. In this study, three groups of microarray data sets (GES66360, GSE48060, GSE29532) were collected from GEO, a total of 99, 52 and 55 samples, respectively. Weighted gene co-expression network analysis (WGCNA) was performed to obtain a classifier which composed of related genes that best characterize the AMI.

Results

Here, this study obtained three groups of microarray data sets (GES66360, GSE48060, GSE29532) on AMI blood samples, a total of 99, 52 and 24 samples, respectively. In all, 4672 genes, 3185 genes, 3660 genes were identified in GSE66360, GSE48060, GSE60993 modules, respectively. We preformed WGCNA, GO and KEGG pathway enrichment analysis on these three data sets, finding function enrichment of the differential expression gene on inflammation and immune response. Transcriptome analysis were performed in AMI patients at four time points compared to CAD patients with no history of MI, to determine gene expression profiles and their possible changes during the recovery from myocardial infarction.

Conclusions

The results suggested that three overlapping genes ( FGFBP2, GFOD1 and MLC1) between two modules could be a potential use of gene biomarkers for the diagnose of AMI.

Related collections

Most cited references 25

Record: found
Abstract: found
Article: not found

A novel hierarchical selective ensemble classifier with bioinformatics application.

Jiasheng Guo, Shixiang Wan, Leyi Wei … (2017)

Selective ensemble learning is a technique that selects a subset of diverse and accurate basic models in order to generate stronger generalization ability. In this paper, we proposed a novel learning algorithm that is based on parallel optimization and hierarchical selection (PTHS). Our novel feature selection method is based on maximize the sum of relevance and distance (MSRD) for solving the problem of high dimensionality. Specifically, we have a PTHS algorithm that employs parallel optimization and candidate model pruning based on k-means and a hierarchical selection framework. We combine the prediction result of each basic model by majority voting, which employs the divide-and-conquer strategy to save computing time. In addition, the PT algorithm is capable to transform a multi-class problem into a binary classification problem, and thereby allowing our ensemble model to address multi-class problems. Empirical study shows that MSRD is efficient in solving the high dimensionality problem, and PTHS exhibits better performance than the other existing classification algorithms. Most importantly, our classifier achieved high-level performance on several bioinformatics problems (e.g. tRNA identification, and protein-protein interaction prediction, etc.), demonstrating efficiency and robustness.

0 comments Cited 86 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Absolute and attributable risks of cardiovascular disease incidence in relation to optimal and borderline risk factors: comparison of African American with white subjects--Atherosclerosis Risk in Communities Study.

Atsushi Hozawa, Aaron Folsom, Lloyd E. Chambless … (2007)

Among white Americans, a large proportion of cardiovascular disease (CVD) events is explained by borderline or any elevated CVD risk factor levels. The degree to which this is true among African American subjects is unclear. The Atherosclerosis Risk in Communities Study included 14 162 middle-aged adults who were free of recognized stroke or coronary heart disease and had baseline information on risk factors. Based on national guidelines, we categorized risk factors (blood pressure, cholesterol levels, diabetes, and smoking) into 3 categories, ie, optimal, borderline, and elevated. Incidence of CVD (composite of stroke and coronary heart disease) (n = 1492) and CVD mortality (n = 612) were identified for a 13-year period. The proportion of subjects with all optimal risk factor levels was lower in African American (3.8%) than in white (7.5%) subjects. Conversely, the proportion of subjects with at least 1 elevated risk factor was higher in African American (approximately 80%) than in white (approximately 60%) subjects. After adjustment for these risk factor differences and education level, African American and white subjects had virtually identical rates of CVD (relative hazard for African American subjects, 1.01; 95% confidence interval, 0.90-1.14). The proportion of CVD events explained by elevated risk factors was high in African American subjects (approximately 90%) compared with approximately 65% in white subjects. The higher CVD incidence rate in African American than in white subjects seems largely attributable to a high frequency of elevated CVD risk factors in African American subjects. Primary prevention of elevated CVD risk factors in African American subjects might greatly reduce CVD occurrence as much as it has for white subjects.

0 comments Cited 58 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

The underlying risk of death after myocardial infarction in the absence of treatment.

Nicholas Wald, Michael R Law, C Watt (2002)

The underlying risk of death in the absence of treatment after a myocardial infarction (MI) is poorly documented. Analysis of 23 published studies in which 14 211 patients were followed prospectively after MI; 6817 deaths were recorded. We restricted the analysis to studies in which follow-up was completed by 1980 to quantify the underlying risk in the absence of effective treatments. After a first MI, on average, 23% of patients died before reaching the hospital and another 13% died during hospital admission; these rates increased with age. After hospital discharge cardiovascular mortality was approximately 10% in the first year and 5% per year thereafter, rates that were unrelated to age or sex. The yearly death rate of 5% persisted indefinitely; after 15 years, cumulative cardiovascular mortality was 70%. After a subsequent MI, 33% of patients died before reaching the hospital, and 20% died in hospital. After discharge, cardiovascular mortality was approximately 20% in the first year and 10% per year thereafter, rates again unrelated to age and sex. Approximately a third of all heart disease deaths occurred minutes after the first MI, a sixth during the first hospitalization, and half after a subsequent MI, which could occur many years after the first. In persons with a history of MI, cardiovascular mortality in the absence of treatment is high-5% per year after a first MI and 10% per year after a subsequent MI, persisting for many years and probably for the rest of a person's life. The high mortality rate emphasizes the need to ensure that everyone who has had an MI, even years previously, receives effective preventive treatment.

0 comments Cited 53 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Zhihua Liu: zh.liu@siat.ac.cn

Journal

Journal ID (nlm-ta): Biomed Eng Online

Journal ID (iso-abbrev): Biomed Eng Online

Title: BioMedical Engineering OnLine

Publisher: BioMed Central (London )

ISSN (Electronic): 1475-925X

Publication date (Electronic): 25 January 2019

Publication date PMC-release: 25 January 2019

Publication date Collection: 2019

Volume: 18

Electronic Location Identifier: 9

Affiliations

[1 ]ISNI 0000000119573309, GRID grid.9227.e, Shenzhen Institutes of Advanced Technology, , Chinese Academy of Sciences, ; Shenzhen, 518055 China

[2 ]ISNI 0000 0001 0662 3178, GRID grid.12527.33, Tsinghua University, ; Beijing, 100084 China

[3 ]Beijing Yuqiu Medical Research Institute, Beijing, 100022 China

[4 ]Shenzhen Yuqiu Biological Big Data Research Institute, Shenzhen, 518033 China

[5 ]Nanjing Yuqiu Biotechnology Co., Ltd., Nanjing, 210009 China

[6 ]ISNI 0000 0000 9226 1013, GRID grid.412030.4, Hebei University of Technology, ; Tianjin, 300130 China

Article

Publisher ID: 625

DOI: 10.1186/s12938-019-0625-6

PMC ID: 6347746

PubMed ID: 30683112

SO-VID: 92781d43-7886-4515-a1d9-7519b7f0d3e8

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 26 January 2018

Date accepted : 1 March 2018

Funding

Funded by: SZ Basic Research Grant

Award ID: JCYJ20150401145529007

Award Recipient : Zhihua Liu

Funded by: SZ Technology Research Grant

Award ID: CXZZ20151015163619907

Award ID: JSGG20160229123927512

Award ID: KQJSCX20170331161941176

Award Recipient : Zhihua Liu

Custom metadata

ScienceOpen disciplines: Biomedical engineering

Keywords: acute myocardial infarction,weighted gene co-expression network analysis,gene ontology,functional enrichment analysis

Data availability:

ScienceOpen disciplines: Biomedical engineering

Keywords: acute myocardial infarction, weighted gene co-expression network analysis, gene ontology, functional enrichment analysis

Potential biomarkers of acute myocardial infarction based on weighted gene co-expression network analysis

Read this article at

Abstract

Background

Results

Conclusions

Related collections

Journal of Disability Research

Most cited references 25

A novel hierarchical selective ensemble classifier with bioinformatics application.

Absolute and attributable risks of cardiovascular disease incidence in relation to optimal and borderline risk factors: comparison of African American with white subjects--Atherosclerosis Risk in Communities Study.

The underlying risk of death after myocardial infarction in the absence of treatment.

Author and article information

Contributors

Journal

Affiliations

Article

History

Funding

Categories

Custom metadata

Comments

Comment on this article

Similar content 219

Cited by 14

Most referenced authors 594