Grant Details
Grant Number: |
1R03CA167695-01A1 Interpret this number |
Primary Investigator: |
Zhao, Zhongming |
Organization: |
Vanderbilt University |
Project Title: |
Investigating Micrornas and Their Regulatory Networks in Glioblastoma |
Fiscal Year: |
2013 |
Abstract
DESCRIPTION (provided by applicant): Glioblastoma multiforme (GBM) is the most common and lethal brain tumor in humans. It is highly resistant to radiation and chemotherapy. Understanding its molecular mechanisms is critical in order to develop more effective treatments. Recent studies revealed that microRNAs (miRNAs) play critical roles in the pathogenesis of GBM. To date, more than 100 dysregulated miRNAs have been found in GBM from various miRNA expression studies, which provide us with astonishing insights into the patterns of miRNA expression in GBM. However, the results reported so far have been inconsistent, presenting a great challenge in deciphering the underlying miRNA regulatory mechanisms in GBM. Therefore, a systematic examination of previous miRNA data is immediately needed and executable. In this project, we will develop innovative strategies to identify functionally important miRNAs significantly associated with GBM in the context of miRNA regulatory networks. The project will start by prioritizing miRNAs through integrating results from multiple studies using a mixed effects model, then build GBM-specific regulatory networks comprised of weighted molecules, i.e. GBM miRNAs, GBM genes and human transcription factors (TFs), and finally perform dense module search (DMS) of the regulatory networks to detect functionally critical miRNAs in GBM regulatory networks. We propose three specific aims. (1) To develop a novel, statistical integrative framework for the meta-analysis of miRNA expression data from multiple studies using a mixed effects model. Compared to traditional pooled analysis, we will integrate all possible effect sizes of each miRNA into a mixed
effects model and calculate a P-value as its overall effect size to GBM. (2) To develop a novel computational pipeline to construct GBM-specific miRNA- mediated regulatory networks consisting of GBM miRNAs, GBM genes, and TFs. (3) To develop a novel dense module search (DMS) algorithm for identifying functionally important miRNAs in GBM. In this DMS algorithm, a module is defined as a set of FFLs, each of which includes miRNA, gene(s), and TF, and their regulatory relationships. This project constitutes a pioneering effort to establish an integrative and comprehensive modeling framework, as well as practical computational methods for detecting functionally important miRNAs in complex diseases and demonstrates it in GBM. Successful completion of this project will greatly enhance our understanding of the regulatory systems in GBM, which will likely lead to the development of effective prevention, diagnosis, and treatment strategies.
Publications
The Potential Roles of Long Noncoding RNAs (lncRNA) in Glioblastoma Development.
Authors: Liu S.
, Mitra R.
, Zhao M.M.
, Fan W.
, Eischen C.M.
, Yin F.
, Zhao Z.
.
Source: Molecular cancer therapeutics, 2016 Dec; 15(12), p. 2977-2986.
EPub date: 2016-10-26.
PMID: 27784795
Related Citations
MicroRNA-31 initiates lung tumorigenesis and promotes mutant KRAS-driven lung cancer.
Authors: Edmonds M.D.
, Boyd K.L.
, Moyo T.
, Mitra R.
, Duszynski R.
, Arrate M.P.
, Chen X.
, Zhao Z.
, Blackwell T.S.
, Andl T.
, et al.
.
Source: The Journal of clinical investigation, 2016 Jan; 126(1), p. 349-64.
EPub date: 2015-12-14.
PMID: 26657862
Related Citations
A cross-cancer differential co-expression network reveals microRNA-regulated oncogenic functional modules.
Authors: Lin C.C.
, Mitra R.
, Cheng F.
, Zhao Z.
.
Source: Molecular bioSystems, 2015 Dec; 11(12), p. 3244-52.
PMID: 26448606
Related Citations
microRNA regulation in cancer: One arm or two arms?
Authors: Mitra R.
, Sun J.
, Zhao Z.
.
Source: International journal of cancer, 2015-09-15; 137(6), p. 1516-8.
EPub date: 2015-04-20.
PMID: 25758934
Related Citations
Regulation rewiring analysis reveals mutual regulation between STAT1 and miR-155-5p in tumor immunosurveillance in seven major cancers.
Authors: Lin C.C.
, Jiang W.
, Mitra R.
, Cheng F.
, Yu H.
, Zhao Z.
.
Source: Scientific reports, 2015-07-09; 5, p. 12063.
EPub date: 2015-07-09.
PMID: 26156524
Related Citations
Concordant dysregulation of miR-5p and miR-3p arms of the same precursor microRNA may be a mechanism in inducing cell proliferation and tumorigenesis: a lung cancer study.
Authors: Mitra R.
, Lin C.C.
, Eischen C.M.
, Bandyopadhyay S.
, Zhao Z.
.
Source: RNA (New York, N.Y.), 2015 Jun; 21(6), p. 1055-65.
EPub date: 2015-04-07.
PMID: 25852169
Related Citations
Clinically relevant genes and regulatory pathways associated with NRASQ61 mutations in melanoma through an integrative genomics approach.
Authors: Jiang W.
, Jia P.
, Hutchinson K.E.
, Johnson D.B.
, Sosman J.A.
, Zhao Z.
.
Source: Oncotarget, 2015-02-10; 6(4), p. 2496-508.
PMID: 25537510
Related Citations
MBSTAR: multiple instance learning for predicting specific functional binding sites in microRNA targets.
Authors: Bandyopadhyay S.
, Ghosh D.
, Mitra R.
, Zhao Z.
.
Source: Scientific reports, 2015-01-23; 5, p. 8004.
EPub date: 2015-01-23.
PMID: 25614300
Related Citations
Snowball: resampling combined with distance-based regression to discover transcriptional consequences of a driver mutation.
Authors: Xu Y.
, Guo X.
, Sun J.
, Zhao Z.
.
Source: Bioinformatics (Oxford, England), 2015-01-01; 31(1), p. 84-93.
EPub date: 2014-09-05.
PMID: 25192743
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Algorithms for network-based identification of differential regulators from transcriptome data: a systematic evaluation.
Authors: Yu H.
, Mitra R.
, Yang J.
, Li Y.
, Zhao Z.
.
Source: Science China. Life sciences, 2014 Nov; 57(11), p. 1090-102.
EPub date: 2014-10-18.
PMID: 25326829
Related Citations
Reproducible combinatorial regulatory networks elucidate novel oncogenic microRNAs in non-small cell lung cancer.
Authors: Mitra R.
, Edmonds M.D.
, Sun J.
, Zhao M.
, Yu H.
, Eischen C.M.
, Zhao Z.
.
Source: RNA (New York, N.Y.), 2014 Sep; 20(9), p. 1356-68.
EPub date: 2014-07-14.
PMID: 25024357
Related Citations
Studying tumorigenesis through network evolution and somatic mutational perturbations in the cancer interactome.
Authors: Cheng F.
, Jia P.
, Wang Q.
, Lin C.C.
, Li W.H.
, Zhao Z.
.
Source: Molecular biology and evolution, 2014 Aug; 31(8), p. 2156-69.
EPub date: 2014-05-31.
PMID: 24881052
Related Citations
A meta-analysis of somatic mutations from next generation sequencing of 241 melanomas: a road map for the study of genes with potential clinical relevance.
Authors: Xia J.
, Jia P.
, Hutchinson K.E.
, Dahlman K.B.
, Johnson D.
, Sosman J.
, Pao W.
, Zhao Z.
.
Source: Molecular cancer therapeutics, 2014 Jul; 13(7), p. 1918-28.
EPub date: 2014-04-22.
PMID: 24755198
Related Citations
Quantitative network mapping of the human kinome interactome reveals new clues for rational kinase inhibitor discovery and individualized cancer therapy.
Authors: Cheng F.
, Jia P.
, Wang Q.
, Zhao Z.
.
Source: Oncotarget, 2014-06-15; 5(11), p. 3697-710.
PMID: 25003367
Related Citations
VarWalker: personalized mutation network analysis of putative cancer genes from next-generation sequencing data.
Authors: Jia P.
, Zhao Z.
.
Source: PLoS computational biology, 2014 Feb; 10(2), p. e1003460.
EPub date: 2014-02-06.
PMID: 24516372
Related Citations
Network.assisted analysis to prioritize GWAS results: principles, methods and perspectives.
Authors: Jia P.
, Zhao Z.
.
Source: Human genetics, 2014 Feb; 133(2), p. 125-38.
PMID: 24122152
Related Citations
A tri-component conservation strategy reveals highly confident microRNA-mRNA interactions and evolution of microRNA regulatory networks.
Authors: Lin C.C.
, Mitra R.
, Zhao Z.
.
Source: PloS one, 2014; 9(7), p. e103142.
EPub date: 2014-07-23.
PMID: 25054916
Related Citations
Synergetic regulatory networks mediated by oncogene-driven microRNAs and transcription factors in serous ovarian cancer.
Authors: Zhao M.
, Sun J.
, Zhao Z.
.
Source: Molecular bioSystems, 2013 Dec; 9(12), p. 3187-98.
EPub date: 2013-10-16.
PMID: 24129674
Related Citations
Dynamic protein interaction modules in human hepatocellular carcinoma progression.
Authors: Yu H.
, Lin C.C.
, Li Y.Y.
, Zhao Z.
.
Source: BMC systems biology, 2013; 7 Suppl 5(Suppl 5), p. S2.
EPub date: 2013-12-09.
PMID: 24564909
Related Citations
DCGL v2.0: an R package for unveiling differential regulation from differential co-expression.
Authors: Yang J.
, Yu H.
, Liu B.H.
, Zhao Z.
, Liu L.
, Ma L.X.
, Li Y.X.
, Li Y.Y.
.
Source: PloS one, 2013; 8(11), p. e79729.
EPub date: 2013-11-20.
PMID: 24278165
Related Citations
CNVannotator: a comprehensive annotation server for copy number variation in the human genome.
Authors: Zhao M.
, Zhao Z.
.
Source: PloS one, 2013; 8(11), p. e80170.
EPub date: 2013-11-14.
PMID: 24244640
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