Dataset Information

NCF2, MYO1F, S1PR4, and FCN1 as potential noninvasive diagnostic biomarkers in patients with obstructive coronary artery: A weighted gene co-expression network analysis.

ABSTRACT: This study aims to explore the predictive noninvasive biomarker for obstructive coronary artery disease (CAD). By using the data set GSE90074, weighted gene co-expression network analysis (WGCNA), and protein-protein interactive network, construction of differentially expressed genes in peripheral blood mononuclear cells was conducted to identify the most significant gene clusters associated with obstructive CAD. Univariate and multivariate stepwise logistic regression analyses and receiver operating characteristic analysis were used to predicate the diagnostic accuracy of biomarker candidates in the detection of obstructive CAD. Furthermore, functional prediction of candidate gene biomarkers was further confirmed in ST-segment elevation myocardial infarction (STEMI) patients or stable CAD patients by using the datasets of GSE62646 and GSE59867. We found that the blue module discriminated by WGCNA contained 13 hub-genes that could be independent risk factors for obstructive CAD (P??.05). This four-gene signature could also act as a prognostic biomarker to discriminate STEMI patients from stable CAD patients. These findings suggest a four-gene signature (NCF2, MYO1F, S1PR4, and FCN1) alone or combined with two risk factors (male sex and hyperlipidemia) as a promising prognostic biomarker in the diagnosis of STEMI. Well-designed cohort studies should be implemented to warrant the diagnostic value of these genes in clinical purpose.

SUBMITTER: Mo XG

PROVIDER: S-EPMC6771964 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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NCF2, MYO1F, S1PR4, and FCN1 as potential noninvasive diagnostic biomarkers in patients with obstructive coronary artery: A weighted gene co-expression network analysis.

Mo Xian-Gang XG Liu Wei W Yang Yao Y Imani Saber S Lu Shan S Dan Guorong G Nie Xuqiang X Yan Jun J Zhan Rixing R Li Xiaohui X Deng Youcai Y Chen Bingbo B Cai Yue Y

Journal of cellular biochemistry 20190627 10

This study aims to explore the predictive noninvasive biomarker for obstructive coronary artery disease (CAD). By using the data set GSE90074, weighted gene co-expression network analysis (WGCNA), and protein-protein interactive network, construction of differentially expressed genes in peripheral blood mononuclear cells was conducted to identify the most significant gene clusters associated with obstructive CAD. Univariate and multivariate stepwise logistic regression analyses and receiver oper ...[more]

PMID: 31245869

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Project description:ObjectiveTo externally validate and extend a recently proposed prediction model to diagnose obstructive coronary artery disease (CAD), with the ultimate aim to better select patients for coronary angiography.DesignAnalysis of individual baseline data of a prospective cardiology cohort.SettingSingle-centre secondary and tertiary cardiology clinic.Participants4888 patients with suspected CAD, without known previous CAD or other heart diseases, who underwent an elective coronary angiography between 2004 and 2008 as part of the prospective Coronary Artery disease Risk Determination In Innsbruck by diaGnostic ANgiography (CARDIIGAN) cohort. Relevant data were recorded as in routine clinical practice.Main outcome measuresThe probability of obstructive CAD, defined as a stenosis of minimally 50% diameter in at least one of the main coronary arteries, estimated with the predictors age, sex, type of chest pain, diabetes status, hypertension, dyslipidaemia, smoking status and laboratory data. Missing predictor data were multiply imputed. Performance of the suggested models was evaluated according to discrimination (area under the receiver operating characteristic curve, depicted by the c statistic) and calibration. Logistic regression modelling was applied for model updating.ResultsAmong the 4888 participants (38% women and 62% men), 2127 (44%) had an obstructive CAD. The previously proposed model had a c statistic of 0.69 (95% CI 0.67 to 0.70), which was lower than the expected c statistic while correcting for case mix (c=0.80). Regarding calibration, there was overprediction of risk for high-risk patients. All logistic regression coefficients were smaller than expected, especially for the predictor 'chest pain'. Extension of the model with high-density lipoprotein and low-density lipoprotein cholesterol, fibrinogen, and C reactive protein led to better discrimination (c=0.72, 95% CI 0.71 to 0.74, p<0.001 for improvement).ConclusionsThe proposed prediction model has a moderate performance to diagnose obstructive CAD in an unselected patient group with suspected CAD referred for elective CA. A small, but significant improvement was attained by including easily available and measurable cardiovascular risk factors.

Dataset Information

NCF2, MYO1F, S1PR4, and FCN1 as potential noninvasive diagnostic biomarkers in patients with obstructive coronary artery: A weighted gene co-expression network analysis.

Publications

NCF2, MYO1F, S1PR4, and FCN1 as potential noninvasive diagnostic biomarkers in patients with obstructive coronary artery: A weighted gene co-expression network analysis.

Similar Datasets

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