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CeModule: an integrative framework for discovering regulatory patterns from genomic data in cancer

BACKGROUND: Non-coding RNAs (ncRNAs) are emerging as key regulators and play critical roles in a wide range of tumorigenesis. Recent studies have suggested that long non-coding RNAs (lncRNAs) could interact with microRNAs (miRNAs) and indirectly regulate miRNA targets through competing interactions....

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Autores principales: Xiao, Qiu, Luo, Jiawei, Liang, Cheng, Cai, Jie, Li, Guanghui, Cao, Buwen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6367773/
https://www.ncbi.nlm.nih.gov/pubmed/30732558
http://dx.doi.org/10.1186/s12859-019-2654-3
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author Xiao, Qiu
Luo, Jiawei
Liang, Cheng
Cai, Jie
Li, Guanghui
Cao, Buwen
author_facet Xiao, Qiu
Luo, Jiawei
Liang, Cheng
Cai, Jie
Li, Guanghui
Cao, Buwen
author_sort Xiao, Qiu
collection PubMed
description BACKGROUND: Non-coding RNAs (ncRNAs) are emerging as key regulators and play critical roles in a wide range of tumorigenesis. Recent studies have suggested that long non-coding RNAs (lncRNAs) could interact with microRNAs (miRNAs) and indirectly regulate miRNA targets through competing interactions. Therefore, uncovering the competing endogenous RNA (ceRNA) regulatory mechanism of lncRNAs, miRNAs and mRNAs in post-transcriptional level will aid in deciphering the underlying pathogenesis of human polygenic diseases and may unveil new diagnostic and therapeutic opportunities. However, the functional roles of vast majority of cancer specific ncRNAs and their combinational regulation patterns are still insufficiently understood. RESULTS: Here we develop an integrative framework called CeModule to discover lncRNA, miRNA and mRNA-associated regulatory modules. We fully utilize the matched expression profiles of lncRNAs, miRNAs and mRNAs and establish a model based on joint orthogonality non-negative matrix factorization for identifying modules. Meanwhile, we impose the experimentally verified miRNA-lncRNA interactions, the validated miRNA-mRNA interactions and the weighted gene-gene network into this framework to improve the module accuracy through the network-based penalties. The sparse regularizations are also used to help this model obtain modular sparse solutions. Finally, an iterative multiplicative updating algorithm is adopted to solve the optimization problem. CONCLUSIONS: We applied CeModule to two cancer datasets including ovarian cancer (OV) and uterine corpus endometrial carcinoma (UCEC) obtained from TCGA. The modular analysis indicated that the identified modules involving lncRNAs, miRNAs and mRNAs are significantly associated and functionally enriched in cancer-related biological processes and pathways, which may provide new insights into the complex regulatory mechanism of human diseases at the system level. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-2654-3) contains supplementary material, which is available to authorized users.
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spelling pubmed-63677732019-02-15 CeModule: an integrative framework for discovering regulatory patterns from genomic data in cancer Xiao, Qiu Luo, Jiawei Liang, Cheng Cai, Jie Li, Guanghui Cao, Buwen BMC Bioinformatics Research Article BACKGROUND: Non-coding RNAs (ncRNAs) are emerging as key regulators and play critical roles in a wide range of tumorigenesis. Recent studies have suggested that long non-coding RNAs (lncRNAs) could interact with microRNAs (miRNAs) and indirectly regulate miRNA targets through competing interactions. Therefore, uncovering the competing endogenous RNA (ceRNA) regulatory mechanism of lncRNAs, miRNAs and mRNAs in post-transcriptional level will aid in deciphering the underlying pathogenesis of human polygenic diseases and may unveil new diagnostic and therapeutic opportunities. However, the functional roles of vast majority of cancer specific ncRNAs and their combinational regulation patterns are still insufficiently understood. RESULTS: Here we develop an integrative framework called CeModule to discover lncRNA, miRNA and mRNA-associated regulatory modules. We fully utilize the matched expression profiles of lncRNAs, miRNAs and mRNAs and establish a model based on joint orthogonality non-negative matrix factorization for identifying modules. Meanwhile, we impose the experimentally verified miRNA-lncRNA interactions, the validated miRNA-mRNA interactions and the weighted gene-gene network into this framework to improve the module accuracy through the network-based penalties. The sparse regularizations are also used to help this model obtain modular sparse solutions. Finally, an iterative multiplicative updating algorithm is adopted to solve the optimization problem. CONCLUSIONS: We applied CeModule to two cancer datasets including ovarian cancer (OV) and uterine corpus endometrial carcinoma (UCEC) obtained from TCGA. The modular analysis indicated that the identified modules involving lncRNAs, miRNAs and mRNAs are significantly associated and functionally enriched in cancer-related biological processes and pathways, which may provide new insights into the complex regulatory mechanism of human diseases at the system level. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-2654-3) contains supplementary material, which is available to authorized users. BioMed Central 2019-02-07 /pmc/articles/PMC6367773/ /pubmed/30732558 http://dx.doi.org/10.1186/s12859-019-2654-3 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Xiao, Qiu
Luo, Jiawei
Liang, Cheng
Cai, Jie
Li, Guanghui
Cao, Buwen
CeModule: an integrative framework for discovering regulatory patterns from genomic data in cancer
title CeModule: an integrative framework for discovering regulatory patterns from genomic data in cancer
title_full CeModule: an integrative framework for discovering regulatory patterns from genomic data in cancer
title_fullStr CeModule: an integrative framework for discovering regulatory patterns from genomic data in cancer
title_full_unstemmed CeModule: an integrative framework for discovering regulatory patterns from genomic data in cancer
title_short CeModule: an integrative framework for discovering regulatory patterns from genomic data in cancer
title_sort cemodule: an integrative framework for discovering regulatory patterns from genomic data in cancer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6367773/
https://www.ncbi.nlm.nih.gov/pubmed/30732558
http://dx.doi.org/10.1186/s12859-019-2654-3
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