Finding microRNA regulatory modules in human genome using rule induction

Tran, Dang Hung; Satou, Kenji; Ho, Tu Bao

doi:10.1186/1471-2105-9-s12-s5

Cited by 77 publications

(55 citation statements)

References 44 publications

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“…Previously, Yoon and De Micheli (2005) and Tran et al (2008) used computational methods to predict miRNA regulatory modules. Yoon based his approach solely on miRNA:mRNA interactions as predicted by TargetScan, without expression data.…”

mentioning

confidence: 99%

Reprogramming of miRNA networks in cancer and leukemia

Volinia¹,

Galasso²,

Costinean³

et al. 2010

Genome Res.

330

276

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We studied miRNA profiles in 4419 human samples (3312 neoplastic, 1107 nonmalignant), corresponding to 50 normal tissues and 51 cancer types. The complexity of our database enabled us to perform a detailed analysis of microRNA (miRNA) activities. We inferred genetic networks from miRNA expression in normal tissues and cancer. We also built, for the first time, specialized miRNA networks for solid tumors and leukemias. Nonmalignant tissues and cancer networks displayed a change in hubs, the most connected miRNAs. hsa-miR-103/106 were downgraded in cancer, whereas hsa-miR-30 became most prominent. Cancer networks appeared as built from disjointed subnetworks, as opposed to normal tissues. A comparison of these nets allowed us to identify key miRNA cliques in cancer. We also investigated miRNA copy number alterations in 744 cancer samples, at a resolution of 150 kb. Members of miRNA families should be similarly deleted or amplified, since they repress the same cellular targets and are thus expected to have similar impacts on oncogenesis. We correctly identified hsa-miR-17/92 family as amplified and the hsa-miR-143/145 cluster as deleted. Other miRNAs, such as hsa-miR-30 and hsa-miR-204, were found to be physically altered at the DNA copy number level as well. By combining differential expression, genetic networks, and DNA copy number alterations, we confirmed, or discovered, miRNAs with comprehensive roles in cancer. Finally, we experimentally validated the miRNA network with acute lymphocytic leukemia originated in Mir155 transgenic mice. Most of miRNAs deregulated in these transgenic mice were located close to hsa-miR-155 in the cancer network

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mentioning

confidence: 99%

Reprogramming of miRNA networks in cancer and leukemia

Volinia¹,

Galasso²,

Costinean³

et al. 2010

Genome Res.

330

276

View full text Add to dashboard Cite

show abstract

“…The rule-based learning method for identification of MRMs proposed by Tran et al 72 is based on the assumption that genes regulated by the same microRNAs should show similar expression profiles. Their method first uses PicTar for microRNA target prediction.…”

Section: Computational Approaches To Identify Functional Microrna Tarmentioning

confidence: 99%

Computational methods for the identification of microRNA targets

Dai

Zhou²

2010

OAB

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MicroRNAs are pivotal regulators of development and cellular homeostasis. They act as post-transcriptional regulators, which control the stability and translation efficiency of their target mRNAs. The prediction of microRNA targets and detection of microRNA-mRNA regulatory modules (MRMs) are crucial components for understanding of microRNA functions. Numerous computational methods for microRNA target prediction have been developed. Computationally-predicted targets have been recently used in the integrative analysis of microRNA and mRNA expression analysis to identify microRNA targets and MRMs. In this article we review these recent developments in the integrative analysis methods. We also discuss the remaining challenges and our insights on future directions.

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“…Networks with high modularity have dense connections between the nodes within modules but sparse connections between nodes in different modules. To date, a number of methods have been developed to infer miRNA-mRNA regulation (or correlation) modules using the genome-wide transcription and sequence affinity information (Joung et al , 2007; Tran et al , 2008; Michoel et al , 2009; Peng et al , 2009; Jayaswal et al , 2011). In a previous study (Zhang et al , 2012a), we applied a clustering-based algorithm to the miRNA and mRNA gene expression data of human prostate cancer cells, finding highly-informative modules specific for primary tumors (PPCs).…”

Section: Introductionmentioning

confidence: 99%

The modularity and dynamicity of miRNA–mRNA interactions in high-grade serous ovarian carcinomas and the prognostic implication

Zhang

Edwards

Fan³

et al. 2016

Computational Biology and Chemistry

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Ovarian carcinoma is the fifth-leading cause of cancer death among women in the United States. Major reasons for this persistent mortality include the poor understanding of the underlying biology and a lack of reliable biomarkers. Previous studies have shown that aberrantly expressed MicroRNAs (miRNAs) are involved in carcinogenesis and tumor progression by post-transcriptionally regulating gene expression. However, the interference of miRNAs in tumorigenesis is quite complicated and far from being fully understood. In this work, by an integrative analysis of mRNA expression, miRNA expression and clinical data published by The Cancer Genome Atlas (TCGA), we studied the modularity and dynamicity of miRNA-mRNA interactions and the prognostic implications in high-grade serous ovarian carcinomas. With the top transcriptional correlations (Bonferroni-adjusted p-value < 0.01) as inputs, we identified five miRNA-mRNA module pairs (MPs), each of which included one positive-connection (correlation) module and one negative-connection (correlation) module. The number of miRNAs or mRNAs in each module varied from 3 to 7 or from 2 to 873. Among the four major negative-connection modules, three fit well with the widely accepted miRNA-mediated post-transcriptional regulation theory. These modules were enriched with the genes relevant to cell cycle and immune response. Moreover, we proposed two novel algorithms to reveal the group or sample specific dynamic regulations between these two RNA classes. The obtained miRNA-mRNA dynamic network contains 3350 interactions captured across different cancer progression stages or tumor grades. We found that those dynamic interactions tended to concentrate on a few miRNAs (e.g. miRNA-936), and were more likely present on the miRNA-mRNA pairs outside the discovered modules. In addition, we also pinpointed a robust prognostic signature consisting of 56 modular protein-coding genes, whose co-expression patterns were predictive for the survival time of ovarian cancer patients in multiple independent cohorts.

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Finding microRNA regulatory modules in human genome using rule induction

Cited by 77 publications

References 44 publications

Reprogramming of miRNA networks in cancer and leukemia

Reprogramming of miRNA networks in cancer and leukemia

Computational methods for the identification of microRNA targets

The modularity and dynamicity of miRNA–mRNA interactions in high-grade serous ovarian carcinomas and the prognostic implication

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