MicroRNAs in inflammatory lung disease - master regulators or target practice?

MicroRNAs (miRNAs) have emerged as a class of regulatory RNAs with immense significance in numerous biological processes. When aberrantly expressed miRNAs have been shown to play a role in the pathogenesis of several disease states. Extensive research has explored miRNA involvement in the development and fate of immune cells and in both the innate and adaptive immune responses whereby strong evidence links miRNA expression to signalling pathways and receptors with critical roles in the inflammatory response such as NF-κB and the toll-like receptors, respectively. Recent studies have revealed that unique miRNA expression profiles exist in inflammatory lung diseases such as cystic fibrosis, chronic obstructive pulmonary disease, asthma, idiopathic pulmonary fibrosis and lung cancer. Evaluation of the global expression of miRNAs provides a unique opportunity to identify important target gene sets regulating susceptibility and response to infection and treatment, and control of inflammation in chronic airway disorders. Over 800 human miRNAs have been discovered to date, however the biological function of the majority remains to be uncovered. Understanding the role that miRNAs play in the modulation of gene expression leading to sustained chronic pulmonary inflammation is important for the development of new therapies which focus on prevention of disease progression rather than symptom relief. Here we discuss the current understanding of miRNA involvement in innate immunity, specifically in LPS/TLR4 signalling and in the progression of the chronic inflammatory lung diseases cystic fibrosis, COPD and asthma. miRNA in lung cancer and IPF are also reviewed.     

循环miRNA 表达谱在肺癌早期诊断、判断预后和指导化疗中的应用

MicroRNA(miRNA)是一种高保守,长度大概21-23个核苷酸,非蛋白编码RNA,起着调节基因表达的作用。近年来有关miRNA与肺癌的关系已经得到证实,并且成为当前研究的热点。miRNA能整体调节基因表达,这使得miRNA表达谱在作为生物信号方面比蛋白编码基因更具有提示作用。最近发现miRNA以被保护的状态存在于循环血液中,这使得miRNA表达的发现具有非侵袭性、重现性以及易检测性。研究显示血浆miRNA表达谱可作为肺癌生物信号分子,在肺癌早期诊断、判断预后和指导化疗药物应用等方面具有重要作用。本文将对血浆miRNA与肺癌的研究进展,以及在肺癌早期诊断、判断预后和指导化疗药物应用等方面作一综述。     

MiRNAs as potential biomarker of kidney diseases: A review

MicroRNAs (miRNAs) are 22 nucleotides short, non-coding and tissue-specific single-stranded RNA which modulates target gene expression. Presently, shreds of evidence confirmed that miRNAs play a key role in kidney pathophysiology. The objectives of the present review are to summarize new research data towards the latest developments in the potential use of miRNAs as a diagnostic biomarker for kidney diseases. This holistic information will update the existing knowledge of kidney disease biomarkers. “miRNA profile for Diabetic Kidney disease, Acute kidney injury, Renal fibrosis, hemodialysis, transplants, FSGS, IgAN, etc.” are the search keywords which have been used in this review. The search outcome gave an exciting insightful perception of miRNAs competence as a biomarker. Also it is observed that various samples as plasma, urine and biopsies were used for profiling the miRNA expression. The miRNAs were not only used for diagnostic biomarkers but also for therapeutic targets. Each kidney disease showed different miRNAs expression profile and few miRNAs quite common with some kidney diseases. miRNAs are simple and efficient diagnostic biomarkers for kidney diseases.     

Identification of candidate microRNA biomarkers in renal fibrosis: a meta-analysis of profiling studies

Abstract

The prognostic, diagnostic and therapeutic value of microRNA (miRNA) expression aberrations in renal fibrosis has been studied in recent years. However, the miRNA expression profiling efforts have led to inconsistent results between the studies. The aim of this study was to perform a meta-analysis on the renal fibrosis miRNA expression profiling studies to identify candidate diagnostic biomarkers. We performed comprehensive literature searches in several databases to identify miRNA expression studies of renal fibrosis in animal models and humans. The miRNAs expression data were extracted from 20 included studies, and both miRNA vote-counting strategy and Robust Rank Aggregation method were utilized to identify significant miRNA meta-signatures. The predicted and validated targets of miRNA meta-signature were obtained by using MultiMiR package in 11 databases. Then a gene set enrichment analysis (KEGG, PANTHER pathways and GO processes) were carried out with GeneCodis web tool to recognize pathways that are most strongly influenced by modified expressions of these miRNAs. We recognized in both meta-analysis approaches a significant miRNA meta-signature of five up-regulated (miR-142-3p, miR-223-3p, miR-21-5p, miR-142-5p and miR-214-3p) and two down-regulated (miR-29c-3p and miR-200a-3p) miRNAs. Enrichment analysis confirmed that miRNA meta-signature cooperatively target functionally related genes in signalling and developmental pathways in renal fibrosis. This meta-analysis identified seven highly significant and consistently dysregulated miRNAs from 20 datasets, as the focus of future investigations to discover their potential influence to renal fibrosis and their clinical utility as biomarkers and/or as therapeutic mediators against chronic kidney disease..     

Circulating miRNA and cancer diagnosis

miRNAs are a class of small RNA molecules with regulatory function, and play an important role in tumor development and progression. It has been demonstrated that tumor-derived miRNAs exist in the circulating nucleic acids of cancer patients. This phenomenon implies that detection of the circulating miRNA may be an effective method for non-invasive diagnosis of cancer. In this review, we summarize the applications of the circulating miRNA as biomarkers in cancer diagnosis, as well as the latest research progress in this area.     

Cellular and extracellular miRNAs are blood‐compartment‐specific diagnostic targets in sepsis

Septic shock is a common medical condition with a mortality approaching 50% where early diagnosis and treatment are of particular importance for patient survival. Novel biomarkers that serve as prompt indicators of sepsis are urgently needed. High‐throughput technologies assessing circulating microRNAs represent an important tool for biomarker identification, but the blood‐compartment specificity of these miRNAs has not yet been investigated. We characterized miRNA profiles from serum exosomes, total serum and blood cells (leukocytes, erythrocytes, platelets) of sepsis patients by next‐generation sequencing and RT‐qPCR (n = 3 × 22) and established differences in miRNA expression between blood compartments. In silico analysis was used to identify compartment‐specific signalling functions of differentially regulated miRNAs in sepsis‐relevant pathways. In septic shock, a total of 77 and 103 miRNAs were down‐ and up‐regulated, respectively. A majority of these regulated miRNAs (14 in serum, 32 in exosomes and 73 in blood cells) had not been previously associated with sepsis. We found a distinctly compartment‐specific regulation of miRNAs between sepsis patients and healthy volunteers. Blood cellular miR‐199b‐5p was identified as a potential early indicator for sepsis and septic shock. miR‐125b‐5p and miR‐26b‐5p were uniquely regulated in exosomes and serum, respectively, while one miRNA (miR‐27b‐3p) was present in all three compartments. The expression of sepsis‐associated miRNAs is compartment‐specific. Exosome‐derived miRNAs contribute significant information regarding sepsis diagnosis and survival prediction and could serve as newly identified targets for the development of novel sepsis biomarkers.     

MCLPMDA: A novel method for miRNA‐disease association prediction based on matrix completion and label propagation

MiRNAs are a class of small non‐coding RNAs that are involved in the development and progression of various complex diseases. Great efforts have been made to discover potential associations between miRNAs and diseases recently. As experimental methods are in general expensive and time‐consuming, a large number of computational models have been developed to effectively predict reliable disease‐related miRNAs. However, the inherent noise and incompleteness in the existing biological datasets have inevitably limited the prediction accuracy of current computational models. To solve this issue, in this paper, we propose a novel method for miRNA‐disease association prediction based on matrix completion and label propagation. Specifically, our method first reconstructs a new miRNA/disease similarity matrix by matrix completion algorithm based on known experimentally verified miRNA‐disease associations and then utilizes the label propagation algorithm to reliably predict disease‐related miRNAs. As a result, MCLPMDA achieved comparable performance under different evaluation metrics and was capable of discovering greater number of true miRNA‐disease associations. Moreover, case study conducted on Breast Neoplasms further confirmed the prediction reliability of the proposed method. Taken together, the experimental results clearly demonstrated that MCLPMDA can serve as an effective and reliable tool for miRNA‐disease association prediction.     

SNMDA: A novel method for predicting microRNA‐disease associations based on sparse neighbourhood

miRNAs are a class of small noncoding RNAs that are associated with a variety of complex biological processes. Increasing studies have shown that miRNAs have close relationships with many human diseases. The prediction of the associations between miRNAs and diseases has thus become a hot topic. Although traditional experimental methods are reliable, they could only identify a limited number of associations as they are time‐consuming and expensive. Consequently, great efforts have been made to effectively predict reliable disease‐related miRNAs based on computational methods. In this study, we present a novel approach to predict the potential microRNA‐disease associations based on sparse neighbourhood. Specifically, our method takes advantage of the sparsity of the miRNA‐disease association network and integrates the sparse information into the current similarity matrices for both miRNAs and diseases. To demonstrate the utility of our method, we applied global LOOCV, local LOOCV and five‐fold cross‐validation to evaluate our method, respectively. The corresponding AUCs are 0.936, 0.882 and 0.934. Three types of case studies on five common diseases further confirm the performance of our method in predicting unknown miRNA‐disease associations. Overall, results show that SNMDA can predict the potential associations between miRNAs and diseases effectively.     

MicroRNA expression profile of human periodontal ligament cells under the influence of Porphyromonas gingivalis LPS

Periodontitis is a chronic inflammatory disease which is caused by bacterial infection and leads to the destruction of periodontal tissues and resorption of alveolar bone. Thus, special attention should be paid to the mechanism under lipopolysaccharide (LPS)‐induced periodontitis because LPS is the major cause of periodontitis. However, to date, miRNA expression in the LPS‐induced periodontitis has not been well characterized. In this study, we investigated miRNA expression patterns in LPS‐treated periodontal ligament cells (PDLCs). Through miRNA array and differential analysis, 22 up‐regulated miRNAs and 28 down‐regulated miRNAs in LPS‐treated PDLCs were identified. Seven randomly selected up‐regulated (miR‐21‐5p, 498, 548a‐5p) and down‐regulated (miR‐495‐3p, 539‐5p, 34c‐3p and 7a‐2‐3p) miRNAs were examined by qRT‐PCR, and the results proved the accuracy of the miRNA array. Moreover, targets of these deregulated miRNAs were analysed using the miRWalk database. Database for Annotation, Visualization and Integration Discovery software were performed to analyse the Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes pathway of differential expression miRNAs, and the results shown that Toll‐like receptor signalling pathway, cAMP signalling pathway, transforming growth factor‐beta signalling pathway, mitogen‐activated protein kinase (MAPK) signalling pathway and other pathways were involved in the molecular mechanisms underlying LPS‐induced periodontitis. In conclusion, this study provides clues for enhancing our understanding of the mechanisms and roles of miRNAs as key regulators of LPS‐induced periodontitis.     

MicroRNA dysregulation in rhabdomyosarcoma: a new player enters the game

Rhabdomyosarcoma (RMS) is the most common of the soft tissue sarcomas with resultant high morbidity, frequently occuring in paediatric patients and young adults. While the molecular basis of RMS has received considerable attention, exact mechanisms underlying its development and metastasis remain unclear. MicroRNAs (miRNAs) are endogenously expressed small non‐coding RNAs that negatively regulate gene expression via translational inhibition or mRNA degradation. Deregulated expression of miRNA has been implicated in initiation, progression, and metastasis of RMS. miRNAs have emerged as key regulators of several physiological and pathophysiological processes and have opened new avenues for diagnosis and treatment of RMS. This review summarizes deregulation and functional roles of miRNAs in RMS and their potential applications for diagnosis, prognosis and treatment of this malignancy. As a rapidly evolving field in basic and translational medicine, it is hopeful that miRNA research will ultimately improve management of RMS.     

循环miRNAs在几种肝病检测中的研究进展

micro RNAs(mi RNAs)是一类单链非编码的内源性小RNA分子,参与细胞、组织甚至个体的生长发育。以往人们主要研究的是肝脏组织中mi RNAs在肝脏相关疾病检测中的应用,随着2008年血液中mi RNAs的发现,循环mi RNAs在疾病检测中的研究逐渐成为热点。micro RNAs普遍存在于血液中,与肝癌、肝硬化等多种肝脏疾病的发生发展密切相关。本文介绍了循环mi RNAs的来源及其特异性、稳定性等特性,简述了循环mi RNAs传统和最新的检测方法及特点,总结了循环mi RNAs在肝癌、肝炎、肝硬化、药物性肝损伤等肝脏疾病检测中的研究进展。近年研究表明循环mi RNAs检测具有采样方便、稳定性好、灵敏度高、可连续监测等优势,在肝脏相关疾病的诊断和预测中发挥了越来越重要的作用,其临床意义及应用前景已引起高度关注。本文重点综述在前人的研究结果中有望成为肝脏相关疾病诊断和预后判断的循环mi RNAs分子标志物发现目前的很多研究只是停留在实验室阶段,尚缺乏简单有效的诊断方法和统一的诊断标准,未来研究的热点应集中在如何进一步提高有潜力的mi RNAs分子标志物的敏感性、特异性和标准化,使其真正应用于临床诊断。     

GIMDA: Graphlet interaction‐based MiRNA‐disease association prediction

MicroRNAs (miRNAs) have been confirmed to be closely related to various human complex diseases by many experimental studies. It is necessary and valuable to develop powerful and effective computational models to predict potential associations between miRNAs and diseases. In this work, we presented a prediction model of Graphlet Interaction for MiRNA‐Disease Association prediction (GIMDA) by integrating the disease semantic similarity, miRNA functional similarity, Gaussian interaction profile kernel similarity and the experimentally confirmed miRNA‐disease associations. The related score of a miRNA to a disease was calculated by measuring the graphlet interactions between two miRNAs or two diseases. The novelty of GIMDA lies in that we used graphlet interaction to analyse the complex relationships between two nodes in a graph. The AUCs of GIMDA in global and local leave‐one‐out cross‐validation (LOOCV) turned out to be 0.9006 and 0.8455, respectively. The average result of five‐fold cross‐validation reached to 0.8927 ± 0.0012. In case study for colon neoplasms, kidney neoplasms and prostate neoplasms based on the database of HMDD V2.0, 45, 45, 41 of the top 50 potential miRNAs predicted by GIMDA were validated by dbDEMC and miR2Disease. Additionally, in the case study of new diseases without any known associated miRNAs and the case study of predicting potential miRNA‐disease associations using HMDD V1.0, there were also high percentages of top 50 miRNAs verified by the experimental literatures.     

MicroRNA signatures associated with thioacetamide-induced liver fibrosis in mice

Multiple etiologies of liver injury are associated with fibrosis in which the key event is the activation of hepatic stellate cells (HSCs). Although microRNAs (miRNAs) are reportedly involved in fibrogenesis, the complete array of miRNA signatures associated with the disease has yet to be elucidated. Here, deep sequencing analysis revealed that compared to controls, 80 miRNAs were upregulated and 21 miRNAs were downregulated significantly in the thioacetamide (TAA)-induced mouse fibrotic liver. Interestingly, 58 of the upregulated miRNAs were localized to an oncogenic miRNA megacluster upregulated in liver cancer. Differential expression of some of the TAA-responsive miRNAs was confirmed, and their human orthologs were similarly deregulated in TGF-β1-activated HSCs. Moreover, a functional analysis of the experimentally validated high-confidence miRNA targets revealed significant enrichment for the GO terms and KEGG pathways involved in HSC activation and liver fibrogenesis. This is the first comprehensive report of miRNAs profiles during TAA-induced mouse liver fibrosis.     

Novel miRNA markers for the diagnosis and prognosis of endometrial cancer

As endometrial cancer (EC) is a major threat to female health worldwide, the ability to provide an accurate diagnosis and prognosis of EC is promising to improve its treatment guidance. Since the discovery of miRNAs, it has been realized that miRNAs are associated with every cell function, including malignant transformation and metastasis. This study aimed to explore diagnostic and prognostic miRNA markers of EC. In this study, differential analysis and machine learning were performed, followed by correlation analysis of miRNA‐mRNA based on the miRNA and mRNA expression data. Nine miRNAs were identified as diagnostic markers, and a diagnostic classifier was established to distinguish between EC and normal endometrium tissue with overall correct rates >95%. Five specific prognostic miRNA markers were selected to construct a prognostic model, which was confirmed more effective in identifying EC patients at high risk of mortality compared with the FIGO staging system. This study demonstrates that the expression patterns of miRNAs may hold promise for becoming diagnostic and prognostic biomarkers and novel therapeutic targets for EC.