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

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          Abstract

          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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          Most cited references24

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          LncRNA profile study reveals a three-lncRNA signature associated with the survival of patients with oesophageal squamous cell carcinoma

          Background Oesophageal cancer is one of the most deadly forms of cancer worldwide. Long non-coding RNAs (lncRNAs) are often found to have important regulatory roles. Objective To assess the lncRNA expression profile of oesophageal squamous cell carcinoma (OSCC) and identify prognosis-related lncRNAs. Method LncRNA expression profiles were studied by microarray in paired tumour and normal tissues from 119 patients with OSCC and validated by qRT-PCR. The 119 patients were divided randomly into training (n=60) and test (n=59) groups. A prognostic signature was developed from the training group using a random Forest supervised classification algorithm and a nearest shrunken centroid algorithm, then validated in a test group and further, in an independent cohort (n=60). The independence of the signature in survival prediction was evaluated by multivariable Cox regression analysis. Results LncRNAs showed significantly altered expression in OSCC tissues. From the training group, we identified a three-lncRNA signature (including the lncRNAs ENST00000435885.1, XLOC_013014 and ENST00000547963.1) which classified the patients into two groups with significantly different overall survival (median survival 19.2 months vs >60 months, p 60 months, p=0.0030) and independent cohort (median survival 25.8 months vs >48 months, p=0.0187) and showed similar prognostic values in both. Multivariable Cox regression analysis showed that the signature was an independent prognostic factor for patients with OSCC. Stratified analysis suggested that the signature was prognostic within clinical stages. Conclusions Our results suggest that the three-lncRNA signature is a new biomarker for the prognosis of patients with OSCC, enabling more accurate prediction of survival.
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            Long non-coding RNA expression profiles predict clinical phenotypes in glioma.

            Glioma is the commonest form of primary brain tumor in adults with varying malignancy grades and histological subtypes. Long non-coding RNAs (lncRNAs) are a novel class of non-protein-coding transcripts that have been shown to play important roles in cancer development. To discover novel tumor-related lncRNAs and determine their associations with glioma subtypes, we first applied a lncRNA classification pipeline to identify 1970 lncRNAs that were represented on Affymetrix HG-U133 Plus 2.0 array. We then analyzed the lncRNA expression patterns in a set of previously published glioma gene expression profiles of 268 clinical specimens, and identified sets of lncRNAs that were unique to different histological subtypes (astrocytic versus oligodendroglial tumors) and malignancy grades. These lncRNAs signatures were then subject to validation in another non-overlapping, independent data set that contained 157 glioma samples. This is the first reported study that correlates lncRNA expression profiles with malignancy grade and histological differentiation in human gliomas. Our findings indicate the potential roles of lncRNAs in the biogenesis, development and differentiation of gliomas, and provide an important platform for future studies. Copyright © 2012 Elsevier Inc. All rights reserved.
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              Inferring novel lncRNA-disease associations based on a random walk model of a lncRNA functional similarity network.

              Accumulating evidence demonstrates that long non-coding RNAs (lncRNAs) play important roles in the development and progression of complex human diseases, and predicting novel human lncRNA-disease associations is a challenging and urgently needed task, especially at a time when increasing amounts of lncRNA-related biological data are available. In this study, we proposed a global network-based computational framework, RWRlncD, to infer potential human lncRNA-disease associations by implementing the random walk with restart method on a lncRNA functional similarity network. The performance of RWRlncD was evaluated by experimentally verified lncRNA-disease associations, based on leave-one-out cross-validation. We achieved an area under the ROC curve of 0.822, demonstrating the excellent performance of RWRlncD. Significantly, the performance of RWRlncD is robust to different parameter selections. Predictively highly-ranked lncRNA-disease associations in case studies of prostate cancer and Alzheimer's disease were manually confirmed by literature mining, providing evidence of the good performance and potential value of the RWRlncD method in predicting lncRNA-disease associations.
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                Author and article information

                Journal
                Oncotarget
                Oncotarget
                Oncotarget
                ImpactJ
                Oncotarget
                Impact Journals LLC
                1949-2553
                24 October 2017
                9 August 2017
                : 8
                : 51
                : 88613-88621
                Affiliations
                1 Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
                2 Department of Biological Sciences and Arnie Charbonneau Cancer Institute, University of Calgary, Calgary, Canada
                3 Medical Department of Breast Oncology, The Tumor Hospital of Harbin Medical University, Harbin, China
                4 School of Nursing, Harbin Medical University, Harbin, China
                Author notes
                Correspondence to: Ping Lin, pinglin62@ 123456sina.com
                Article
                20101
                10.18632/oncotarget.20101
                5687631
                ba4c71a4-458c-45c5-a448-9d61969d3077
                Copyright: © 2017 Li et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License 3.0 (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 15 June 2017
                : 13 July 2017
                Categories
                Research Paper

                Oncology & Radiotherapy
                acute myocardial infarction,early diagnosis,long non-coding rnas,expression profiles,biomarkers

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