A novel strategy of identifying circRNA biomarkers in cardiovascular disease by meta-analysis
Journal of Cellular Physiology
School of Medical and Health Sciences
Circular RNAs (circRNAs) are stable and abundantly expressed in vivo but are abnormally expressed in several diseases. This study aimed to identify circRNAs acting as potential biomarkers for cardiovascular disease (CVD). Research were retrieved from the articles published by September 2018 in eight databases to compare circRNA expression profiles between CVD and non-CVD in human and animal models. Meta-analysis under a random effects model was conducted. Subgroup analysis of tissue, species, and disease-specific circRNAs was examined. Sensitivity analysis was performed to explain the uncertainty among all studies. Diagnostic accuracy of circRNAs in CVD was analyzed to testify the discriminative ability. Bioinformatics analysis including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was conducted. Among 6,284 differentially expressed circRNAs from 32 original studies, only 322 circRNAs were reported in three or more studies. The meta-analysis identified 63 significantly dysregulated circRNAs, 44 upregulated and 19 downregulated. Among the tissue-specific or disease-specific circRNAs identified in the subgroup analysis, two circRNAs (circCDKN2BAS and circMACF1) showed the potential to be circulating biomarkers for CVD. Sensitivity analysis demonstrated 69% of circRNAs were in conformity with the overall analysis. The pooled diagnostic odds ratio was 2.94 (95% confidence interval [CI], 2.35-3.58), and the overall area under the curve value was 0.86 (95% CI, 0.83-0.89). GO and KEGG enrichment analyses indicated that the target genes of circRNAs participate in cardiogenesis-related processes and pathways. This study demonstrates circRNAs have a high diagnostic value as potential biomarkers for CVD, and two candidate circRNAs, circCDKN2BAS and circMACF1, are potential circulating biomarkers for CVD diagnosis and treatment.
Multidisciplinary biological approaches to personalised disease diagnosis, prognosis and management