Research Papers:

Differential expression profiles of long non-coding RNAs as potential biomarkers for the early diagnosis of acute myocardial infarction

Ling Li, Yingying Cong, Xueqin Gao, Yini Wang and Ping Lin _

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Oncotarget. 2017; 8:88613-88621. https://doi.org/10.18632/oncotarget.20101

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Ling Li1, Yingying Cong2,3, Xueqin Gao1, Yini Wang1 and Ping Lin4

1Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China

2Department of Biological Sciences and Arnie Charbonneau Cancer Institute, University of Calgary, Calgary, Canada

3Medical Department of Breast Oncology, The Tumor Hospital of Harbin Medical University, Harbin, China

4School of Nursing, Harbin Medical University, Harbin, China

Correspondence to:

Ping Lin, email: [email protected]

Keywords: acute myocardial infarction, early diagnosis, long non-coding RNAs, expression profiles, biomarkers

Received: June 15, 2017     Accepted: July 13, 2017     Published: August 09, 2017


Acute myocardial infarction (AMI) is a major cause of morbidity and mortality worldwide. The early diagnosis of AMI is crucial for deciding the course of treatment and saving lives. Long non-coding RNAs (lncRNAs) are recently discovered ncRNA class and their dysregulated expression has been implicated in cardiovascular diseases. In this study, we analyzed lncRNA expression pattern by using two microarray datasets of AMI and healthy samples from the Gene Expression Omnibus (GEO) database and tried to identify novel AMI-related lncRNAs and investigate the predictive roles of lncRNAs in the early diagnosis of AMI. From the discovery cohort, 11 differentially expressed lncRNAs were identified as candidate biomarkers that were validated in the discovery cohort, internal cohort and an independent cohort, respectively. Hierarchical clustering analysis suggested that the expression pattern of these 11 candidate lncRNA biomarkers was closely associated with disease status of samples. Then a lncRNA risk classifier was developed by integrating expression value of 11 differentially expressed lncRNAs using support vector machine (SVM) algorithm. The results of leaving one out cross-validation (LOOCV) suggested that the lncRNA risk classifier has a good discrimination between AMI patients and healthy samples with the area under ROC curve (AUC) of 0.955, 0.92 and 0.701 in three cohorts, respectively. Functional enrichment analysis suggested that these 11 candidate lncRNA biomarkers might be involved in inflammation- and immune-related biological processes. Our study indicates the potential roles in the early diagnosis of AMI and will improve our understanding of the molecular mechanism of the occurrence and recurrence of AMI.

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