Studying Dynamic Features in Myocardial Infarction Progression by Integrating miRNA-Transcription Factor Co-Regulatory Networks and Time-Series RNA Expression Data from Peripheral Blood Mononuclear Cells

Systematic analysis of lncRNA and microRNA dynamic features reveals diagnostic and prognostic biomarkers of myocardial infarction

Analyses of long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) implicated in myocardial infarction (MI) have increased our understanding of gene regulatory mechanisms in MI. However, it is not known how their expression fluctuates over the different stages of MI progression. In this study, we used time-series gene expression data to examine global lncRNA and miRNA expression patterns during the acute phase of MI and at three different time points thereafter. We observed that the largest expression peak for mRNAs, lncRNAs, and miRNAs occurred during the acute phase of MI and involved mainly protein-coding, rather than non-coding RNAs. Functional analysis indicated that the lncRNAs and miRNAs most sensitive to MI and most unstable during MI progression were usually related to fewer biological functions. Additionally, we developed a novel computational method for identifying dysregulated competing endogenous lncRNA-miRNA-mRNA triplets (LmiRM-CTs) during MI onset and progression. As a result, a new panel of candidate diagnostic biomarkers defined by seven lncRNAs was suggested to have high classification performance for patients with or without MI, and a new panel of prognostic biomarkers defined by two lncRNAs evidenced high discriminatory capability for MI patients who developed heart failure from those who did not.

Beyond Reperfusion: Acute Ventricular Unloading and Cardioprotection During Myocardial Infarction

Heart failure is a major cause of morbidity and mortality around the world, and myocardial infarction is its leading cause. Myocardial infarction destroys viable myocardium, and this dead tissue is replaced by a non-contractile scar that results in impaired cardiac function and a significantly increased likelihood of the patient developing heart failure. Limiting infarct scar size has been the target of pre-clinical and clinical investigations for decades. However, beyond reperfusion, few therapies have translated into the clinic that limit its formation. New approaches are needed. This review will focus on new clinical and pre-clinical data demonstrating that acute ventricular unloading prior to reperfusion by means of percutaneous left ventricular support devices reduces ischemia-reperfusion injury and limits infarct scar size. Emphasis will be given to summarizing our current mechanistic understanding of this new therapeutic approach to treating myocardial infarction.

The expression of microRNA-23a regulates acute myocardial infarction in patients and in vitro through targeting PTEN

Cardiovascular disease is responsible for one of the highest rates of fatality worldwide. The present study investigated the presence and influence of microRNA (miRNA)-23a in the regulation of acute myocardial infarction (AMI). A total of 6 patients with AMI and 6 normal volunteers without myocardial disease were included, and blood samples were taken to analyze the expression of miRNA‑23a by reverse transcription‑quantitative polymerase chain reaction. miRNA‑23a expression in patients with AMI was downregulated compared with the normal group. In H9C2 cells treated with H2O2, upregulation of miRNA‑23a expression increased the superoxide dismutase, glutathione and catalase activity levels, and suppressed the malonaldehyde activity level, as determined by ELISA. Western blot analysis and a caspase‑3 substrate assay demonstrated that upregulation of miRNA‑23a expression suppressed the Bcl‑2‑associated X (Bax)/Bcl‑2 protein expression ratio, caspase‑3 activity level and tumor suppressor p53 (p53) protein expression in H2O2‑induced H9C2 cells. Furthermore, downregulation of phosphatase and tensin homolog (PTEN), by the PTEN inhibitor bpV(HOpic), increased miRNA‑23a expression and suppressed the Bax/Bcl‑2 protein expression ratio, caspase‑3 activity level and p53 protein expression in H2O2‑induced H9C2 cells. Therefore, the results of the present study indicate that the expression of miRNA‑23a may regulate AMI through targeting PTEN in patients and in vitro, and PTEN/miRNA‑23a may therefore be potential targets for the clinical treatment of AMI.

Characterization of dysregulated lncRNA-mRNA network based on ceRNA hypothesis to reveal the occurrence and recurrence of myocardial infarction

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 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.

Modeling tumor progression via the comparison of stage-specific graphs

Can we use graph mining algorithms to find patterns in tumor molecular mechanisms? Can we model disease progression with multiple time-specific graph comparison algorithms? In this paper, we will focus on this area. Our main contributions are 1) we proposed the Temporal-Omics (Temp-O) workflow to model tumor progression in non-small cell lung cancer (NSCLC) using graph comparisons between multiple stage-specific graphs, and 2) we showed that temporal structures are meaningful in the tumor progression of NSCLC. Other identified temporal structures that were not highlighted in this paper may also be used to gain insights to possible novel mechanisms. Importantly, the Temp-O workflow is generic; while we applied it on NSCLC, it can be applied in other cancers and diseases. We used gene expression data from tumor samples across disease stages to model lung cancer progression, creating stage-specific tumor graphs. Validating our findings in independent datasets showed that differences in temporal network structures capture diverse mechanisms in NSCLC. Furthermore, results showed that structures are consistent and potentially biologically important as we observed that genes with similar protein names were captured in the same cliques for all cliques in all datasets. Importantly, the identified temporal structures are meaningful in the tumor progression of NSCLC as they agree with the molecular mechanism in the tumor progression or carcinogenesis of NSCLC. In particular, the identified major histocompatibility complex of class II temporal structures capture mechanisms concerning carcinogenesis; the proteasome temporal structures capture mechanisms that are in early or late stages of lung cancer; the ribosomal cliques capture the role of ribosome biosynthesis in cancer development and sustainment. Further, on a large independent dataset we validated that temporal network structures identified proteins that are prognostic for overall survival in NSCLC adenocarcinoma.