A review of computational approaches detecting microRNAs involved in cancer

Cantini, Laura; Caselle, Michele; Forget, Antoine; Zinovyev, Andrei; Barillot, Emmanuel; Martignetti, Loredana

doi:10.2741/4571

Cited by 15 publications

(15 citation statements)

References 102 publications

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“…16 Integrative computational bioinformatics approaches have been utilized as an effective tool to detect the potential outlier miRNAs in cancer. 20,21 Furthermore, as a rational approach, preliminary detection of candidate miRNAs derived from large-scale expression profiling data and low-throughput experimental verification for the selected outlier miRNAs can be used to choose candidate miRNAs. 22,23 In the present study, according to literature reviews and data mining for were culled to be analyzed and confirmed by RT-qPCR.…”

Section: Discussionmentioning

confidence: 99%

Aberrant expression of a five‐microRNA signature in breast carcinoma as a promising biomarker for diagnosis

Bitaraf

Babashah

Garshasbi

2019

Clinical Laboratory Analysis

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Background Breast cancer (BC) is the most common malignancy among females with dismal quality of life in patients. It has been proven that epigenetic factors, especially microRNAs, are involved in breast carcinogenesis and progression. This study aimed to assess the expression and clinical performances of a five‐microRNA signature (miR‐127‐3p, miR‐133a‐3p, miR‐155‐5p, miR‐199b‐5p, and miR‐342‐5p) in breast cancer and adjacent normal tissues to identify a potential biomarker for BC and investigate the relationship between their expression and clinicopathological features of BC patients as well. Methods In this case‐control investigation, we recruited 50 pairs of tumor and matched non‐tumor surgical specimens from patients diagnosed with BC. Expression levels of miR‐127‐3p, miR‐133a‐3p, miR‐155‐5p, miR‐199b‐5p, and miR‐342‐5p were measured in BC and adjacent normal tissues by RT‐qPCR. Results We found that miR‐127‐3p, miR‐133a‐3p, miR‐199b‐5p, and miR‐342‐5p were significantly down‐regulated, while miR‐155‐5p was significantly up‐regulated in BC tumor tissues compared with the corresponding adjacent normal tissues. The decreased expression of miR‐127‐3p, miR‐133a‐3p, miR‐342‐5p, and up‐regulation of miR‐155‐5p showed a significant correlation with disease stage. We also found a significant down‐regulation of miR‐127‐3p, miR‐199b‐5p, and miR‐342‐5p compared in HER‐2‐negative patients. Our results indicated that miR‐155‐5p had a higher expression level in HER‐2‐positive patients. Receiver operating characteristic (ROC) curve analysis demonstrated that all these five microRNAs can serve as potential biomarkers to distinguish between tumor and non‐tumor breast tissue samples. Conclusions The present findings suggested that dysregulation of this five‐miRNA signature might be considered as a promising and functional biomarker for BC diagnosis.

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Section: Discussionmentioning

confidence: 99%

Aberrant expression of a five‐microRNA signature in breast carcinoma as a promising biomarker for diagnosis

Bitaraf

Babashah

Garshasbi

2019

Clinical Laboratory Analysis

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“…Since >30% of annotated human miRNAs are organized in genomic clusters, we can expect to find other oncogenic/tumor suppressor polycistronic miRNAs that are co-expressed to jointly regulate molecular pathways involved in cancer malignancy. Existing computational approaches for the identification of master miRNA regulators involved in cancer onset and subtyping are typically designed to detect the effect of a single miRNA (see review in (19)). However, miRNAs have been shown to frequently act in a combined manner, jointly regulating proteins in close proximity of the protein-protein interaction network (20) and functionally related genes (21–25).…”

Section: Introductionmentioning

confidence: 99%

Identification of microRNA clusters cooperatively acting on epithelial to mesenchymal transition in triple negative breast cancer

Cantini

Bertoli

Cava

et al. 2019

Nucleic Acids Research

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MicroRNAs play important roles in many biological processes. Their aberrant expression can have oncogenic or tumor suppressor function directly participating to carcinogenesis, malignant transformation, invasiveness and metastasis. Indeed, miRNA profiles can distinguish not only between normal and cancerous tissue but they can also successfully classify different subtypes of a particular cancer. Here, we focus on a particular class of transcripts encoding polycistronic miRNA genes that yields multiple miRNA components. We describe ‘clustered MiRNA Master Regulator Analysis (ClustMMRA)’, a fully redesigned release of the MMRA computational pipeline (MiRNA Master Regulator Analysis), developed to search for clustered miRNAs potentially driving cancer molecular subtyping. Genomically clustered miRNAs are frequently co-expressed to target different components of pro-tumorigenic signaling pathways. By applying ClustMMRA to breast cancer patient data, we identified key miRNA clusters driving the phenotype of different tumor subgroups. The pipeline was applied to two independent breast cancer datasets, providing statistically concordant results between the two analyses. We validated in cell lines the miR-199/miR-214 as a novel cluster of miRNAs promoting the triple negative breast cancer (TNBC) phenotype through its control of proliferation and EMT.

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“…In cancer as well as many other diseases, the involvement of miRs is becoming more and more evident with the help of the latest computational tools, which can reliably predict miR-disease associations to expedite experimental discoveries [80,81,82]. Besides ranking-based methods that focus primarily on differential fold changes in microarrays [83], various computational platforms using data-driven approaches (based on publicly available miR-disease databases) with network-based or machine learning algorithms to find novel miR-disease associations have been recently developed and validated [82,84,85,86,87,88,89,90,91,92].…”

Section: Discussionmentioning

confidence: 99%

Mechanistic Computational Models of MicroRNA-Mediated Signaling Networks in Human Diseases

Zhao

Zhang

Popel

2019

IJMS

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MicroRNAs (miRs) are endogenous non-coding RNA molecules that play important roles in human health and disease by regulating gene expression and cellular processes. In recent years, with the increasing scientific knowledge and new discovery of miRs and their gene targets, as well as the plentiful experimental evidence that shows dysregulation of miRs in a wide variety of human diseases, the computational modeling approach has emerged as an effective tool to help researchers identify novel functional associations between differential miR expression and diseases, dissect the phenotypic expression patterns of miRs in gene regulatory networks, and elucidate the critical roles of miRs in the modulation of disease pathways from mechanistic and quantitative perspectives. Here we will review the recent systems biology studies that employed different kinetic modeling techniques to provide mechanistic insights relating to the regulatory function and therapeutic potential of miRs in human diseases. Some of the key computational aspects to be discussed in detail in this review include (i) models of miR-mediated network motifs in the regulation of gene expression, (ii) models of miR biogenesis and miR–target interactions, and (iii) the incorporation of such models into complex disease pathways in order to generate mechanistic, molecular- and systems-level understanding of pathophysiology. Other related bioinformatics tools such as computational platforms that predict miR-disease associations will also be discussed, and we will provide perspectives on the challenges and opportunities in the future development and translational application of data-driven systems biology models that involve miRs and their regulatory pathways in human diseases.

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A review of computational approaches detecting microRNAs involved in cancer

Cited by 15 publications

References 102 publications

Aberrant expression of a five‐microRNA signature in breast carcinoma as a promising biomarker for diagnosis

Aberrant expression of a five‐microRNA signature in breast carcinoma as a promising biomarker for diagnosis

Identification of microRNA clusters cooperatively acting on epithelial to mesenchymal transition in triple negative breast cancer

Mechanistic Computational Models of MicroRNA-Mediated Signaling Networks in Human Diseases

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