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Multiple Feature Selection Strategies Identified Novel Cardiac Gene Expression Signature for Heart Failure

Overview
Journal Front Physiol
Date 2020 Dec 11
PMID 33304275
Citations 11
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Abstract

Heart failure (HF) is a serious condition in which the support of blood pumped by the heart is insufficient to meet the demands of body at a normal cardiac filling pressure. Approximately 26 million patients worldwide are suffering from heart failure and about 17-45% of patients with heart failure die within 1-year, and the majority die within 5-years admitted to a hospital. The molecular mechanisms underlying the progression of heart failure have been poorly studied. We compared the gene expression profiles between patients with heart failure ( = 177) and without heart failure ( = 136) using multiple feature selection strategies and identified 38 HF signature genes. The support vector machine (SVM) classifier based on these 38 genes evaluated with leave-one-out cross validation (LOOCV) achieved great performance with sensitivity of 0.983 and specificity of 0.963. The network analysis suggested that the hub gene may play important roles in HF. Other genes, such as , , and , also showed great promises. Our results can facilitate the early detection of heart failure and can reveal its molecular mechanisms.

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References
1.
Chen L, Pan X, Guo W, Gan Z, Zhang Y, Niu Z . Investigating the gene expression profiles of cells in seven embryonic stages with machine learning algorithms. Genomics. 2020; 112(3):2524-2534. DOI: 10.1016/j.ygeno.2020.02.004. View

2.
Tao X, Wu X, Huang T, Mu D . Identification and Analysis of Dysfunctional Genes and Pathways in CD8 T Cells of Non-Small Cell Lung Cancer Based on RNA Sequencing. Front Genet. 2020; 11:352. PMC: 7227791. DOI: 10.3389/fgene.2020.00352. View

3.
Kittleson M, Ye S, Irizarry R, Minhas K, Edness G, Conte J . Identification of a gene expression profile that differentiates between ischemic and nonischemic cardiomyopathy. Circulation. 2004; 110(22):3444-51. DOI: 10.1161/01.CIR.0000148178.19465.11. View

4.
Prohaszka Z, Munthe-Fog L, Ueland T, Gombos T, Yndestad A, Forhecz Z . Association of ficolin-3 with severity and outcome of chronic heart failure. PLoS One. 2013; 8(4):e60976. PMC: 3626638. DOI: 10.1371/journal.pone.0060976. View

5.
Jiang Z, Guo N, Hong K . A three-tiered integrative analysis of transcriptional data reveals the shared pathways related to heart failure from different aetiologies. J Cell Mol Med. 2020; 24(16):9085-9096. PMC: 7417717. DOI: 10.1111/jcmm.15544. View