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Identification of long non-coding RNAs biomarkers for early diagnosis of myocardial infarction from the dysregulated coding-non-coding co-expression network.

ABSTRACT: Long non-coding RNAs (lncRNAs) have recently been shown as novel promising diagnostic or prognostic biomarkers for various cancers. However, lncRNA expression patterns and their predictive value in early diagnosis of myocardial infarction (MI) have not been systematically investigated. In our study, we performed a comprehensive analysis of lncRNA expression profiles in MI and found altered lncRNA expression pattern in MI compared to healthy samples. We then constructed a lncRNA-mRNA dysregulation network (DLMCEN) by integrating aberrant lncRNAs, mRNAs and their co-dysregulation relationships, and found that some of mRNAs were previously reported to be involved in cardiovascular disease, suggesting the functional roles of dysregulated lncRNAs in the pathogenesis of MI. Therefore, using support vector machine (SVM) and leave one out cross-validation (LOOCV), we developed a 9-lncRNA signature (termed 9LncSigAMI) from the discovery cohort which could distinguish MI patients from healthy samples with accuracy of 95.96%, sensitivity of 93.88% and specificity of 98%, and validated its predictive power in early diagnosis of MI in another completely independent cohort. Functional analysis demonstrated that these nine lncRNA biomarkers in the 9LncSigAMI may be involved in myocardial innate immune and inflammatory response, and their deregulation may lead to the dysfunction of the inflammatory and immune system contributing to MI recurrence. With prospective validation, the 9LncSigAMI identified by our work will provide additional diagnostic information beyond other known clinical parameters, and increase the understanding of the molecular mechanism underlying the pathogenesis of MI.

SUBMITTER: Sun C

PROVIDER: S-EPMC5341997 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Identification of long non-coding RNAs biomarkers for early diagnosis of myocardial infarction from the dysregulated coding-non-coding co-expression network.

Sun Chaoyu C Jiang Hao H Sun Zhiguo Z Gui Yifang Y Xia Hongyuan H

Oncotarget 20161101 45

Long non-coding RNAs (lncRNAs) have recently been shown as novel promising diagnostic or prognostic biomarkers for various cancers. However, lncRNA expression patterns and their predictive value in early diagnosis of myocardial infarction (MI) have not been systematically investigated. In our study, we performed a comprehensive analysis of lncRNA expression profiles in MI and found altered lncRNA expression pattern in MI compared to healthy samples. We then constructed a lncRNA-mRNA dysregulatio ...[more]

PMID: 27634901

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Project description:Acute myocardial infarction (AMI) is a common cause of death in numerous countries. Understanding the molecular mechanisms of the disease and analyzing potential biomarkers of AMI is crucial. However, specific diagnostic biomarkers have thus far not been fully established and candidate regulatory targets for AMI remain to be determined. In the present study, the AMI gene chip dataset GSE48060 comprising blood samples from control subjects with normal cardiac function (n=21) and patients with AMI (n=26) was downloaded from Gene Expression Omnibus. The differentially expressed genes (DEGs) between the AMI and control groups were identified with the online tool GEO2R. The co-expression network of DEGs was analyzed by calculating the Pearson correlation coefficient of all gene pairs, mutual rank screening and cutoff threshold screening. Subsequently, the Gene Ontology (GO) database was used to analyze the genes' functions and pathway enrichment of genes in the most important modules was performed. Kyoto Encyclopedia of Genes and Genomes (KEGG) Disease and BioCyc were used to analyze the hub genes in the module to determine important sub-pathways. In addition, the expression of hub genes was confirmed by reverse transcription-quantitative PCR in AMI and control specimens. In the present study, 52 DEGs, including 26 upregulated and 26 downregulated genes, were identified. As key hub genes, three upregulated genes (AKR1C3, RPS24 and P2RY12) and three downregulated genes (ACSL1, B3GNT5 and MGAM) were identified from the co-expression network. Furthermore, GO enrichment analysis of all AMI co-expression network genes revealed functional enrichment mainly in 'RAGE receptor binding' and 'negative regulation of T cell cytokine production'. In addition, KEGG Disease and BioCyc analysis indicated functional enrichment of the genes RPS24 and P2RY12 in 'cardiovascular diseases', of AKR1C3 in 'cardenolide biosynthesis', of MGAM in 'glycogenolysis', of B3GNT5 in 'glycosphingolipid biosynthesis' and of ACSL1 in 'icosapentaenoate biosynthesis II'. In conclusion, the hub genes AKR1C3, RPS24, P2RY12, ACSL1, B3GNT5 and MGAM are potential markers of AMI, and have potential application value in the diagnosis of AMI.

Project description:Long noncoding RNAs (lncRNAs) are non-protein coding transcripts regulating various critical physiological and pathological processes, yet limited information is available about lncRNAs expression in acute myocardial infarction (AMI). We aimed to identified differentially expressed lncRNAs in blood samples of patients with AMI to assess their diagnostic value. Differential expression of lncRNAs in peripheral blood mononuclear cells (PBMC) of patients with non-ST-elevation myocardial infarction (NSTEMI) and ST-elevation myocardial infarction (STEMI) was compared by RNA sequencing method and validated by real-time polymerase chain reaction (PCR). The area under the receiver operating characteristic curve (ROC) was used to evaluate diagnostic accuracy. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of lncRNA-coexpressed mRNAs were conducted to determine the related biological modules and pathological pathways. RNA sequencing data showed that 58 lncRNAs were differentially expressed between NSTEMI patients and STEMI patients, including 42 upregulated lncRNAs and 16 down-regulated lncRNAs. The ROC curves showed that ENST00000508020.2, LNC_001265, LNC_001526, and LNC_002674 could distinguish AMI patients with preferable sensitivity and specificity. GO enrichment analysis of lncRNA-coexpressed mRNAs indicated that the biological modules were correlated with cell adhesion, calcium ion homeostasis, complement receptor mediated signaling pathway, and immune system process. KEGG pathway analysis indicated that the lncRNAs-co-expressed mRNAs were involved in the regulation of peroxisome proliferators-activated receptors (PPAR) signaling pathway, mTOR signaling pathway, Insulin signaling pathway, HIF-1 signaling, and chemokin signaling pathway. Our results are in line with the previous findings, suggesting that differential expression of lncRNAs would be helpful to understand the molecular mechanism of AMI and might be useful biomarkers for noninvasive diagnostic application. Further studies are still needed to verify our findings and hypothesis.

Project description:BackgroundMyocardial infarction (MI), a critical condition, substantially affects patient outcomes and mortality rates. Long non-coding RNAs (lncRNAs) play a critical role in the onset and progression of MI. This study aimed to explore the related research on MI-related lncRNAs from a bibliometric perspective, providing new clues and directions for researchers in the field.MethodsA comprehensive search was conducted on 7 August 2023, using the Web of Science Core Collection (WoSCC) database to compile a dataset of all English-language scientific journals. The search gathered all relevant publications from January 2000 to August 2023 that pertain to MI-related lncRNAs. Data on countries, institutions, journals, authors, and keywords were collected, sorted, statistically analyzed, and visualized using CiteSpace 6.2.R4, VOSviewer 1.6.19, an online bibliometric analysis platform (http://bibliometric.com), and the bibliometric package in R-Studio 4.3.1. Articles were screened by two independent reviewers.ResultsBetween January 2000 and August 2023, a total of 1,452 papers were published in the research field of MI-related lncRNAs. The year with the most publications was 2020, accounting for 256 papers. The publication volume displayed an exponential growth trend, fitting the equation y = 2.0215e0.2786x, R^2 = 0.97. In this domain, China leads in both the number of published papers (N = 1,034) and total citations, followed by the United States, Germany, Iran, and Italy. The most productive institution is Harbin Medical University (N = 144). The European Review for Medical and Pharmacological Sciences had the highest number of publications (N = 46), while Circulation Research had the most citations (TC = 4,537), indicating its irreplaceable standing in this field. Research mainly focuses on the cardiovascular system, cellular biology, physiology, etc. The most productive author is Zhang Y. Apart from "Myocardial Infarction" and "LncRNA," the most frequent keywords include "expression," "atherosclerosis," and "apoptosis." Cluster analysis suggests current research themes concentrate on cardiovascular diseases and gene expression, cardiac ischemia/reperfusion injury and protection, expression and proliferation, atherosclerosis and inflammatory response, among others. Keyword bursts indicate recent hot topics as targeting, autophagy, etc.ConclusionThis bibliometric analysis reveals that research on MI-related lncRNAs has rapidly expanded between January 2000 and August 2023, primarily led by China and the United States. Our study highlights the significant biological roles of lncRNAs in the pathogenesis and progression of MI, including their involvement in gene expression regulation, atherosclerosis development, and apoptosis. These findings underscore the potential of lncRNAs as therapeutic targets and biomarkers for MI. Additionally, our study provides insights into the features and quality of related publications, as well as the future directions in this research field. There is a long road ahead, highlighting the urgent need for enhanced global academic exchange.

Dataset Information

Identification of long non-coding RNAs biomarkers for early diagnosis of myocardial infarction from the dysregulated coding-non-coding co-expression network.

Publications

Identification of long non-coding RNAs biomarkers for early diagnosis of myocardial infarction from the dysregulated coding-non-coding co-expression network.

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