Abstract
Heart failure (HF) is a heterogeneous clinical syndrome with a variety of causes, risk factors, and pathology. Clinically, only brain natriuretic peptide (BNP) or its precursor N-terminus proBNP (NTproBNP) has been validated for HF diagnosis, but they are also affected by other conditions, such as female gender, renal disease, and acute coronary syndromes, and false low levels in the setting of obesity or flash pulmonary edema. In addition, there is no one biomarker which could encompass all heart failure phenotypes. Advances in bioinformatics have provided us with large databases that characterize the complex genetic and epigenetic changes associated with human diseases. The use of data mining strategies on public access databases to identify previously unknown disease markers is an innovative approach to identify potential biomarkers or even new therapeutic targets in complex diseases such as heart failure (HF). In this study, we analyzed the genomic and transcription data of HF peripheral blood mononuclear cell (PBMC) samples obtained from the Gene Expression Omnibus data sets using Omicsbean online database (http://www.omicsbean.cn/) and found that the prostaglandin-endoperoxide synthase 2 (PTGS2), also named as cyclooxygenase-2 (COX-2), as well as its related micro RNAs including miR-1297 and miR-4649-3p might be used as potential biomarkers for non-ischemic heart failure. Our result showed that plasma COX-2 and miR-4649-3p were significantly up-regulated, whereas the plasma miR-1297 was significantly decreased, and miR-4649-3p displayed high predictive power for non-ischemic heart failure.
Highlights
Heart failure (HF) is a heterogeneous clinical syndrome with a variety of risk factors, and pathology and a leading cause of mortality in the world (Linsenmayer et al, 2019)
The diagnosis of heart failure is most frequently made at the time of presentation of symptoms, while advances in congestive heart failure (CHF) management depend on biomarkers for monitoring disease progression and therapeutic response (Inamdar and Inamdar, 2016)
We explored the deregulated genes of nonischemic heart failure based on Gene Expression Omnibus (GEO) data sets and predicted related micro RNAs using bioinformatics analyses
Summary
Heart failure (HF) is a heterogeneous clinical syndrome with a variety of risk factors, and pathology and a leading cause of mortality in the world (Linsenmayer et al, 2019). COX2 and miRNAs for HF Prediction validated for HF diagnosis, and they were affected by many other conditions such as advanced age, female gender, renal disease and acute coronary syndromes, and obesity or flash pulmonary edema (Goetze et al, 2005). The diagnosis of heart failure is most frequently made at the time of presentation of symptoms, while advances in congestive heart failure (CHF) management depend on biomarkers for monitoring disease progression and therapeutic response (Inamdar and Inamdar, 2016). There is no biomarker that could encompass all heart failure phenotypes (Senthong et al, 2017). It is urgent to discover novel and reliable biomarkers for HF
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