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. 2020 Jan 31;40(1):BSR20191731.
doi: 10.1042/BSR20191731.

A Susceptibility Biomarker Identification Strategy Based on Significantly Differentially Expressed ceRNA Triplets for Ischemic Cardiomyopathy

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Free PMC article

A Susceptibility Biomarker Identification Strategy Based on Significantly Differentially Expressed ceRNA Triplets for Ischemic Cardiomyopathy

Yuqing Zou et al. Biosci Rep. .
Free PMC article

Abstract

Ischemic cardiomyopathy (ICM) is a common human heart disease that causes death. No effective biomarkers for ICM could be found in existing databases, which is detrimental to the in-depth study of this disease. In the present study, ICM susceptibility biomarkers were identified using a proposed strategy based on RNA-Seq and miRNA-Seq data of ICM and normal samples. Significantly differentially expressed competing endogenous RNA (ceRNA) triplets were constructed using permutation tests and differentially expressed mRNAs, miRNAs and lncRNAs. Candidate ICM susceptible genes were screened out as differentially expressed genes in significantly differentially expressed ceRNA triplets enriched in ICM-related functional classes. Finally, eight ICM susceptibility genes and their significantly correlated lncRNAs with high classification accuracy were identified as ICM susceptibility biomarkers. These biomarkers would contribute to the diagnosis and treatment of ICM. The proposed strategy could be extended to other complex diseases without disease biomarkers in public databases.

Keywords: ICM; ceRNA; lncRNA; mRNA; miRNA.

Conflict of interest statement

The authors declare that there are no competing interests associated with the manuscript.

Figures

Figure 1
Figure 1. A schematic representation of the ICM biomarker identification strategy adopted in the present study
Figure 2
Figure 2. Functional enrichment analysis of differentially expressed genes in significantly differentially expressed ceRNA triplets
ICM-related functional classes (the outer ring) and GO terms (the inner ring) significantly enriched by differentially expressed genes in significantly differentially expressed ceRNA triplets including at least one differentially expressed miRNA or lncRNA. The axis indicates FDR-adjusted P-value of the enrichment analyses.
Figure 3
Figure 3. Classification accuracy of 37 candidate ICM susceptibility genes
ROC curves of the SVM classifier with 37 candidate ICM susceptibility genes as classification features using four kernel functions: (A) the linear kernel, (B) the polynomial kernel, (C) the sigmoid kernel, and (D) the radial basis function kernel.
Figure 4
Figure 4. Classification accuracy comparison of ICM susceptibility biomarkers
Classification accuracy of ICM susceptibility biomarkers (mRNAs and lncRNAs) and randomly selected mRNAs and lncRNAs from those differentially expressed or in significantly differential correlated pairs.
Figure 5
Figure 5. Classification performance of five significantly differentially expressed ceRNA triplets from the ICM samples
ROC curves of significantly differentially expressed ceRNA triplets composed of (A) FLT4+mir-4635+LINC00900, (B) FLT4+mir-6869+AC099778.1, (C) PKD1+mir-674+TGFB2-AS1, (D) PSMB1+mir-3619+AL021368.2, and (E) PSMB1+mir-6801+AL021368.2.
Figure 6
Figure 6. Classification performance of two significantly differentially expressed ceRNA triplets from the normal samples
ROC curves of significantly differentially expressed ceRNA triplets composed of (A) FLT4+mir-6505+LINC00884 and (B) FLT4+mir-4315-2+DBH-AS1.

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