ArticlePloS one2010
Module network inference from a cancer gene expression data set identifies microRNA regulated modules.
Article in PloS one, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Comprehensive analysis of miRNA-mRNA regulatory pairs associated with colorectal cancer and the role in tumor immunity.BMC genomics · 2023Article
- Identifying Tissue- and Cohort-Specific RNA Regulatory Modules in Cancer Cells Using Multitask Learning.Cancers · 2022Article
- Integrative approaches for analysis of mRNA and microRNA high-throughput data.Computational and structural biotechnology journal · 2021Review
- The human papillomavirus E7 oncoprotein as a regulator of transcription.Virus research · 2017Review
- In silico Transcriptional Regulatory Networks Involved in Tomato Fruit Ripening.Frontiers in plant science · 2016Article
- Expression and clinical significance of the microRNA-200 family in gastric cancer.Oncology letters · 2015Article
- Integrative multi-omics module network inference with Lemon-Tree.PLoS computational biology · 2015Article
- A computational approach to identifying gene-microRNA modules in cancer.PLoS computational biology · 2015Article
- Systematic exploration of autonomous modules in noisy microRNA-target networks for testing the generality of the ceRNA hypothesis.BMC genomics · 2014Article
- Identification of co-expression gene networks, regulatory genes and pathways for obesity based on adipose tissue RNA Sequencing in a porcine model.BMC medical genomics · 2014Article
- Identifying miRNAs, targets and functions.Briefings in bioinformatics · 2014Review
- MicroRNAs act complementarily to regulate disease-related mRNA modules in human diseases.RNA (New York, N.Y.) · 2013Article
- Constructing higher-order miRNA-mRNA interaction networks in prostate cancer via hypergraph-based learning.BMC systems biology · 2013Article
- Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review.Pharmacology & therapeutics · 2013Review
- Minireview: miRomics in endocrinology: a novel approach for modeling endocrine diseases.Molecular endocrinology (Baltimore, Md.) · 2013Review
- RMaNI: Regulatory Module Network Inference framework.BMC bioinformatics · 2013Article
- Article
- Comparative transcriptome analysis of two olive cultivars in response to NaCl-stress.PloS one · 2012Article
- miRNA-mRNA correlation-network modules in human prostate cancer and the differences between primary and metastatic tumor subtypes.PloS one · 2012Article
- Robust detection of hierarchical communities from Escherichia coli gene expression data.PLoS computational biology · 2012Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMicroRNAs (miRNAs) are small RNAs that recognize and regulate mRNA target genes. Multiple lines of evidence indicate that they are key regulators of numerous critical functions in development and disease, including cancer. However, defining the place and function of miRNAs in complex regulatory networks is not straightforward. Systems approaches, like the inference of a module network from expression data, can help to achieve this goal. METHODOLOGY/PRINCIPAL
findingsDuring the last decade, much progress has been made in the development of robust and powerful module network inference algorithms. In this study, we analyze and assess experimentally a module network inferred from both miRNA and mRNA expression data, using our recently developed module network inference algorithm based on probabilistic optimization techniques. We show that several miRNAs are predicted as statistically significant regulators for various modules of tightly co-expressed genes. A detailed analysis of three of those modules demonstrates that the specific assignment of miRNAs is functionally coherent and supported by literature. We further designed a set of experiments to test the assignment of miR-200a as the top regulator of a small module of nine genes. The results strongly suggest that miR-200a is regulating the module genes via the transcription factor ZEB1. Interestingly, this module is most likely involved in epithelial homeostasis and its dysregulation might contribute to the malignant process in cancer cells. CONCLUSIONS/SIGNIFICANCE: Our results show that a robust module network analysis of expression data can provide novel insights of miRNA function in important cellular processes. Such a computational approach, starting from expression data alone, can be helpful in the process of identifying the function of miRNAs by suggesting modules of co-expressed genes in which they play a regulatory role. As shown in this study, those modules can then be tested experimentally to further investigate and refine the function of the miRNA in the regulatory network.
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