Identification of miRNA-mRNA Modules in Colorectal Cancer Using Rough Hypercuboid Based Supervised Clustering

Paul, Sushmita; Lakatos, Petra; Hartmann, Arndt; Schneider‐Stock, Regine; Vera, Julio

doi:10.1038/srep42809

Cited by 13 publications

(9 citation statements)

References 104 publications

(108 reference statements)

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“…Such studies usually also perform miRNA–mRNA pair selection based on miRNA–mRNA interaction experimental databases or prediction algorithms, functional enrichment analyses of the genes or proteins, disease association, and other analyses in order to relate the miRNA and mRNAs in modules to the cancer types/subtypes of interest or survival probability. Specifically, in a study of colorectal cancer, the rough hypercuboid based supervised clustering algorithm (RH-SAC) was used to generate clusters of functionally similar miRNAs or mRNAs whose coherent expression can further efficiently classify the samples [29]. In a study of multiple myelomas, through integrative analysis of GO biological processes, miRNA–mRNA targeting relationship, and matched miRNA and mRNA expression data, the ping-pong algorithm and multiobjective genetic algorithm were integrated to detect subtype-specific miRNA–mRNA regulatory modules [30].…”

Section: Discussionmentioning

confidence: 99%

Identifying Interaction Clusters for MiRNA and MRNA Pairs in TCGA Network

Dai

Ding

Liu³

et al. 2019

Genes

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Existing methods often fail to recognize the conversions for the biological roles of the pairs of genes and microRNAs (miRNAs) between the tumor and normal samples. We have developed a novel cluster scoring method to identify messenger RNA (mRNA) and miRNA interaction pairs and clusters while considering tumor and normal samples jointly. Our method has identified 54 significant clusters for 15 cancer types selected from The Cancer Genome Atlas project. We also determined the shared clusters across tumor types and/or subtypes. In addition, we compared gene and miRNA overlap between lists identified in our liver hepatocellular carcinoma (LIHC) study and regulatory relationships reported from human and rat nonalcoholic fatty liver disease studies (NAFLD). Finally, we analyzed biological functions for the single significant cluster in LIHC and uncovered a significantly enriched pathway (phospholipase D signaling pathway) with six genes represented in the cluster, symbols: DGKQ, LPAR2, PDGFRB, PIK3R3, PTGFR and RAPGEF3.

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Section: Discussionmentioning

confidence: 99%

Identifying Interaction Clusters for MiRNA and MRNA Pairs in TCGA Network

Dai

Ding

Liu³

et al. 2019

Genes

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“…Statistical approaches use statistical tests to find significant modules ( Liu et al, 2010 ; Jayaswal et al, 2011 ; Yan et al, 2012 ; Hecker et al, 2013 ). Rule induction approaches use machine-learning methods to search for subgroups ( Tran, Satou & Ho, 2008 ; Song et al, 2015 ; Paul et al, 2017 ). Probability-based approaches either use population-based probabilistic learning or probabilistic graphical model to infer regulatory information ( Joung et al, 2007 ; Joung & Fei, 2009 ).…”

Section: Introductionmentioning

confidence: 99%

miRcorrNet: machine learning-based integration of miRNA and mRNA expression profiles, combined with feature grouping and ranking

Yousef

Goy

Mitra

et al. 2021

PeerJ

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A better understanding of disease development and progression mechanisms at the molecular level is critical both for the diagnosis of a disease and for the development of therapeutic approaches. The advancements in high throughput technologies allowed to generate mRNA and microRNA (miRNA) expression profiles; and the integrative analysis of these profiles allowed to uncover the functional effects of RNA expression in complex diseases, such as cancer. Several researches attempt to integrate miRNA and mRNA expression profiles using statistical methods such as Pearson correlation, and then combine it with enrichment analysis. In this study, we developed a novel tool called miRcorrNet, which performs machine learning-based integration to analyze miRNA and mRNA gene expression profiles. miRcorrNet groups mRNAs based on their correlation to miRNA expression levels and hence it generates groups of target genes associated with each miRNA. Then, these groups are subject to a rank function for classification. We have evaluated our tool using miRNA and mRNA expression profiling data downloaded from The Cancer Genome Atlas (TCGA), and performed comparative evaluation with existing tools. In our experiments we show that miRcorrNet performs as good as other tools in terms of accuracy (reaching more than 95% AUC value). Additionally, miRcorrNet includes ranking steps to separate two classes, namely case and control, which is not available in other tools. We have also evaluated the performance of miRcorrNet using a completely independent dataset. Moreover, we conducted a comprehensive literature search to explore the biological functions of the identified miRNAs. We have validated our significantly identified miRNA groups against known databases, which yielded about 90% accuracy. Our results suggest that miRcorrNet is able to accurately prioritize pan-cancer regulating high-confidence miRNAs. miRcorrNet tool and all other supplementary files are available at https://github.com/malikyousef/miRcorrNet.

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“…(5). Some studies have demonstrated the abnormally high expression of miR-93-5p in liver (6), breast (7) and lung cancer (8), and it is able to promote cell proliferation and migration by binding to various target genes. Moreover, it has been reported that miR-93-5p may be a potential biomarker for the detection of the presence of cancer (9).…”

Section: Introductionmentioning

confidence: 99%

miR‑93‑5p regulates the occurrence and development of esophageal carcinoma epithelial cells by targeting TGFβR2

et al. 2020

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Emerging studies have indicated that the dysregulation of microRNAs (miRNAs or miRs) plays a vital role in the development and metastasis of tumors. However, the role of miR-93-5p in esophageal carcinoma (Ec) has not been extensively reported. The present study thus focused on the role of miR-93-5p and its downstream target in the occurrence and development of EC. Firstly, miRNA expression profiles associated with Ec were accessed from the TcGA_EScA dataset and analyzed. Subsequently, the expression patterns of miR-93-5p and TGFβR2 were characterized in the human esophageal cell line, Het-1A, and the human Ec cell lines, TE-1, Eca-109 and Ec9706, by RT-qPcR and western blot analysis. WST-1 assay, flow cytometry, Transwell assay, wound healing assay and bioinformatics analysis were used to explore their functions in Ec cells. Finally, a dual-luciferase reporter assay was employed to determine the targeted association between miR-93-5p and TGFβR2. The results revealed that the expression of miR-93-5p was markedly higher in Ec cell lines compared with that in the normal cell line. The overexpression of miR-93-5p facilitated cell proliferation, migration and invasion, and inhibited cell apoptosis. Additionally, TGFβR2 was identified as a functional target of miR-93-5p in EC cells, as judged by a series of in vitro experiments. Furthermore, it was found that the simultaneous overexpression of miR-93-5p and TGFβR2 almost had no effect on the biological behaviors of Ec cells. On the whole, the present study demonstrates that miR-93-5p promotes the proliferation, migration and invasion, and inhibits the apoptosis of Ec cells by targeting TGFβR2.

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Identification of miRNA-mRNA Modules in Colorectal Cancer Using Rough Hypercuboid Based Supervised Clustering

Cited by 13 publications

References 104 publications

Identifying Interaction Clusters for MiRNA and MRNA Pairs in TCGA Network

Identifying Interaction Clusters for MiRNA and MRNA Pairs in TCGA Network

miRcorrNet: machine learning-based integration of miRNA and mRNA expression profiles, combined with feature grouping and ranking

miR‑93‑5p regulates the occurrence and development of esophageal carcinoma epithelial cells by targeting TGFβR2

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