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Characterization of Dysregulated Lncrna-Mrna Network Based on Cerna Hypothesis to Reveal the Occurrence and Recurrence of Myocar

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Characterization of Dysregulated Lncrna-Mrna Network Based on Cerna Hypothesis to Reveal the Occurrence and Recurrence of Myocar Zhang et al. Cell Death Discovery (2018) 4:35 DOI 10.1038/s41420-018-0036-7 Cell Death Discovery ARTICLE Open Access Characterization of dysregulated lncRNA- mRNA network based on ceRNA hypothesis to reveal the occurrence and recurrence of myocardial infarction Guangde Zhang1,HaoranSun2, Yawei Zhang2, Hengqiang Zhao2, Wenjing Fan1,JianfeiLi3,YingliLv2, Qiong Song2, Jiayao Li2,MingyuZhang1 and Hongbo Shi2 Abstract Accumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) acting as competing endogenous RNAs (ceRNAs) play important roles in initiation and development of human diseases. However, the mechanism of ceRNA regulated by lncRNA in myocardial infarction (MI) remained unclear. In this study, we performed a multi-step computational method to construct dysregulated lncRNA-mRNA networks for MI occurrence (DLMN_MI_OC) and recurrence (DLMN_MI_Re) based on “ceRNA hypothesis”. We systematically integrated lncRNA and mRNA expression profiles and miRNA-target regulatory interactions. The constructed DLMN_MI_OC and DLMN_MI_Re both exhibited biological network characteristics, and functional analysis demonstrated that the networks were specific for MI. Additionally, we identified some lncRNA-mRNA ceRNA modules involved in MI occurrence and recurrence. Finally, two new panel biomarkers defined by four lncRNAs (RP1-239B22.5, AC135048.13, RP11-4O1.2, RP11-285F7.2) from 1234567890():,; 1234567890():,; DLMN_MI_OC and three lncRNAs (RP11-363E7.4, CTA-29F11.1, RP5-894A10.6) from DLMN_MI_Re with high classification performance were, respectively, identified in distinguishing controls from patients, and patients with recurrent events from those without recurrent events. This study will provide us new insight into ceRNA-mediated regulatory mechanisms involved in MI occurrence and recurrence, and facilitate the discovery of candidate diagnostic and prognosis biomarkers for MI. Introduction make coronary artery conditions more severely and Myocardial infarction (MI) is one of the most serious greatly increase the risk of death in patients3. Thus stra- types of coronary artery disease, which often lead to tegies for prediction of recurrent events will prolong myocardial cell death due to prolonged ischemia1.Itisa survival in post-MI patients4. Non-high-density lipopro- leading cause of morbidity, mortality, and cost to society2. tein cholesterol value was recently found to be a strong The recurrence of MI following first-time occurrence will predictor of recurrent MI5, and phospholipase A2 expression in coronary thrombus has been reported to be related with recurrence of cardiac events after MI6. Correspondence: Mingyu Zhang ([email protected])or Although some biochemical markers such as cardiac Hongbo Shi ([email protected]) troponins T and I, creatine kinase-MB are clinically used 1Department of Cardiology, The Fourth Affiliated Hospital of Harbin Medical University, 150001 Harbin, Heilongjiang, China for diagnosis of MI, they only indicate myocardial 2College of Bioinformatics Science and Technology, Harbin Medical University, damage, and the molecular mechanisms underlying MI 150081 Harbin, Heilongjiang, China and recurrence of MI are not reflected. Full list of author information is available at the end of the article These authors contributed equally: Guangde Zhang, Hongbo Shi. Edited by N Barlev © The Author(s) 2018. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a linktotheCreativeCommons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Official journal of the Cell Death Differentiation Association Zhang et al. Cell Death Discovery (2018) 4:35 Page 2 of 13 Fig. 1 An integrative pipeline to construct the dysregulated lncRNA-mRNA ceRNA network for MI occurrence and recurrence. First, starting from significantly differentially expressed (SDE) mRNAs and lncRNAs, experimentally verified miRNA–mRNA and miRNA–lncRNA interaction relationships were integrated, and hypergeometric test (p-value < 0.05) was performed to identify candidate lncRNA–mRNA competing interactions. Second, the Pearson correlation coefficient (PCC) was further utilized to screen mRNA–lncRNA competing interactions by using expression profiles. Finally, dysregulated lncRNA-mRNA ceRNA network for MI occurrence and recurrence were constructed by merging the dysregulated lncRNA–mRNA ceRNA interactions, respectively There have been numerous researches documented that untranslated regions of target mRNAs8. miRNAs have less than 2% of the human genome encodes protein- been reported to be implicated in numerous diseases9, 10, coding genes, and non-coding RNAs (ncRNAs) constitute including MI11. While long non-coding RNAs (lncRNAs) most of the human transcriptome7. ncRNAs include short represent a major class of ncRNAs, with greater than 200 ncRNAs and long ncRNAs. microRNAs (miRNAs) are a nucleotides in length, which could regulate genes at class of important short ncRNAs with approximately 22 transcriptional, post-transcriptional, and epigenetic nucleotides in length, which have been extensively stu- levels12. The dysregulation of lncRNA expression is died. It mainly inhibit gene expression by binding to the 3′ therefore associated with various diseases13, including Official journal of the Cell Death Differentiation Association Zhang et al. Cell Death Discovery (2018) 4:35 Page 3 of 13 cancers14, 15, neurodegeneration diseases16, and cardio- were identified (p < 0.01). Among these, 437 mRNAs vascular diseases17, 18. For example, knockdown of a novel and 34 lncRNAs were upregulated and 564 mRNAs lncRNA, Mirt1, was recently reported to improved cardiac and 21 lncRNAs were downregulated. The expression functions, decreased cardiomyocytes apoptosis, and atte- profiles of mRNAs and lncRNAs between MI nuated inflammatory cell infiltration in cardiac fibroblasts patients with recurrent events and those without in acute MI mice19. In rat cardiac muscle H9c2 cells, recurrent events were also analyzed using the R “limma” downregulation of lncRNA KCNQ1OT1 has been found package. Considering the limited number of MI to prevent myocardial ischemia/reperfusion injury fol- patients with recurrent events, the mRNAs and lncRNAs lowing acute MI20. However, little is known about with p < 0.05 were selected as SDE mRNAs and lncRNAs. lncRNAs in MI. Thus, 859 SDE mRNAs and 60 SDE lncRNAs were Theoretical and experimental studies have demon- obtained. Of these, 538 mRNAs and 39 lncRNAs were strated that a large number of miRNA-binding sites exist upregulated and 321 mRNAs and 21 lncRNAs were on different types of RNA transcripts, indicating that downregulated. diverse RNA transcripts containing the miRNA-binding sites can regulate each other through competing for Construction of the DLMN_MI_OC and DLMN_MI_Re shared miRNAs, thus acting as competing endogenous The DLMN_MI_OC and DLMN_MI_Re were both – RNAs (ceRNAs)21 23. Importantly, lncRNAs could com- constructed based on ceRNA theory by integrating pete with miRNA target mRNAs for miRNA molecules, expression profile data and regulatory relationships of and thus regulate miRNA-mediated target repression21, mRNAs, miRNAs, and lncRNAs. As described in “Mate- 22. This type of ceRNA crosstalk has been widely observed rials and methods”, an lncRNA–mRNA competing in different biological processes and diseases. For exam- interaction pair was selected if the lncRNA and the ple, a lncRNA (lnc-mg) that is specifically enriched in mRNA significantly shared common miRNAs, and the skeletal muscle was recently identified, and it was found expression of the lncRNA and the mRNA is positively to promote myogenesis by acting as a ceRNA for miR- correlated. As a result, we identified 660 and 124 dysre- 125b to affect protein abundance of insulin-like growth gulated lncRNA-mRNA pairs for MI occurrence and factor 224. Additionally, lncRNA MIAT was demonstrated recurrence, respectively (Supplementary Table 1). By to function as a ceRNA to upregulate DAPK2 by reg- merging these dysregulated interactions, the ulating miR-22-3p in diabetic cardiomyopathy25. How- DLMN_MI_OC and DLMN_MI_Re were constructed. As ever, ceRNA mechanisms associated with MI have not demonstrated in Figs. 2a and 3a, the DLMN_MI_OC been investigated. included 251 nodes (235 mRNAs and 16 lncRNAs) and In this study, we systematically integrated regulatory 660 ceRNA interactions, and the DLMN_MI_Re con- interactions among lncRNAs, miRNAs and mRNAs and tained 109 nodes (95 mRNAs and 14 lncRNAs) and 124 expression profile data, and identified lncRNA–mRNA ceRNA interactions. competing interactions in MI based on “ceRNA hypoth- esis”. We then constructed dysregulated lncRNA-mRNA Topological and biological functional analysis of the networks for MI occurrence (DLMN_MI_OC) and DLMN_MI_OC and DLMN_MI_Re recurrence (DLMN_MI_Re). The pipeline of construction To investigate the global view of the DLMN_MI_OC
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