Integrative Analysis of Gene and MiRNA Expression Profiles with Transcription Factor-miRNA Feed-forward Loops Identifies Regulators in Human Cancers

Overview

Journal Nucleic Acids Res

Publisher Oxford University Press

Specialty Biochemistry

Date 2012 May 31

PMID 22645320

Citations 53

Authors

Zhenyu Yan

Parantu K Shah

Samir B Amin

Mehmet K Samur

Norman Huang

Xujun Wang

Vikas Misra

Hongbin Ji

Dana Gabuzda

Cheng Li

Affiliations

Soon will be listed here.

Abstract

We describe here a novel method for integrating gene and miRNA expression profiles in cancer using feed-forward loops (FFLs) consisting of transcription factors (TFs), miRNAs and their common target genes. The dChip-GemiNI (Gene and miRNA Network-based Integration) method statistically ranks computationally predicted FFLs by their explanatory power to account for differential gene and miRNA expression between two biological conditions such as normal and cancer. GemiNI integrates not only gene and miRNA expression data but also computationally derived information about TF-target gene and miRNA-mRNA interactions. Literature validation shows that the integrated modeling of expression data and FFLs better identifies cancer-related TFs and miRNAs compared to existing approaches. We have utilized GemiNI for analyzing six data sets of solid cancers (liver, kidney, prostate, lung and germ cell) and found that top-ranked FFLs account for ∼20% of transcriptome changes between normal and cancer. We have identified common FFL regulators across multiple cancer types, such as known FFLs consisting of MYC and miR-15/miR-17 families, and novel FFLs consisting of ARNT, CREB1 and their miRNA partners. The results and analysis web server are available at http://www.canevolve.org/dChip-GemiNi.

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References

Garzon R, Marcucci G, Croce C . Targeting microRNAs in cancer: rationale, strategies and challenges. Nat Rev Drug Discov. 2010; 9(10):775-89. PMC: 3904431. DOI: 10.1038/nrd3179. View

Hanahan D, Weinberg R . Hallmarks of cancer: the next generation. Cell. 2011; 144(5):646-74. DOI: 10.1016/j.cell.2011.02.013. View

Fujiwara T, Hiramatsu M, Isagawa T, Ninomiya H, Inamura K, Ishikawa S . ASCL1-coexpression profiling but not single gene expression profiling defines lung adenocarcinomas of neuroendocrine nature with poor prognosis. Lung Cancer. 2011; 75(1):119-25. DOI: 10.1016/j.lungcan.2011.05.028. View

Guo H, Ingolia N, Weissman J, Bartel D . Mammalian microRNAs predominantly act to decrease target mRNA levels. Nature. 2010; 466(7308):835-40. PMC: 2990499. DOI: 10.1038/nature09267. View

El Baroudi M, Cora D, Bosia C, Osella M, Caselle M . A curated database of miRNA mediated feed-forward loops involving MYC as master regulator. PLoS One. 2011; 6(3):e14742. PMC: 3048388. DOI: 10.1371/journal.pone.0014742. View

Shalgi R, Lieber D, Oren M, Pilpel Y . Global and local architecture of the mammalian microRNA-transcription factor regulatory network. PLoS Comput Biol. 2007; 3(7):e131. PMC: 1914371. DOI: 10.1371/journal.pcbi.0030131. View

Wang J, Lu M, Qiu C, Cui Q . TransmiR: a transcription factor-microRNA regulation database. Nucleic Acids Res. 2009; 38(Database issue):D119-22. PMC: 2808874. DOI: 10.1093/nar/gkp803. View

Kozomara A, Griffiths-Jones S . miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res. 2010; 39(Database issue):D152-7. PMC: 3013655. DOI: 10.1093/nar/gkq1027. View

Parkinson H, Sarkans U, Kolesnikov N, Abeygunawardena N, Burdett T, Dylag M . ArrayExpress update--an archive of microarray and high-throughput sequencing-based functional genomics experiments. Nucleic Acids Res. 2010; 39(Database issue):D1002-4. PMC: 3013660. DOI: 10.1093/nar/gkq1040. View

10.

Shalgi R, Brosh R, Oren M, Pilpel Y, Rotter V . Coupling transcriptional and post-transcriptional miRNA regulation in the control of cell fate. Aging (Albany NY). 2010; 1(9):762-70. PMC: 2815735. DOI: 10.18632/aging.100085. View

11.

Meyer N, Penn L . Reflecting on 25 years with MYC. Nat Rev Cancer. 2008; 8(12):976-90. DOI: 10.1038/nrc2231. View

12.

Taylor B, Schultz N, Hieronymus H, Gopalan A, Xiao Y, Carver B . Integrative genomic profiling of human prostate cancer. Cancer Cell. 2010; 18(1):11-22. PMC: 3198787. DOI: 10.1016/j.ccr.2010.05.026. View

13.

Calin G, Sevignani C, Dumitru C, Hyslop T, Noch E, Yendamuri S . Human microRNA genes are frequently located at fragile sites and genomic regions involved in cancers. Proc Natl Acad Sci U S A. 2004; 101(9):2999-3004. PMC: 365734. DOI: 10.1073/pnas.0307323101. View

14.

Yanaihara N, Caplen N, Bowman E, Seike M, Kumamoto K, Yi M . Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell. 2006; 9(3):189-98. DOI: 10.1016/j.ccr.2006.01.025. View

15.

Friard O, Re A, Taverna D, De Bortoli M, Cora D . CircuitsDB: a database of mixed microRNA/transcription factor feed-forward regulatory circuits in human and mouse. BMC Bioinformatics. 2010; 11:435. PMC: 2936401. DOI: 10.1186/1471-2105-11-435. View

16.

Barabasi A, Oltvai Z . Network biology: understanding the cell's functional organization. Nat Rev Genet. 2004; 5(2):101-13. DOI: 10.1038/nrg1272. View

17.

Mestdagh P, Fredlund E, Pattyn F, Schulte J, Muth D, Vermeulen J . MYCN/c-MYC-induced microRNAs repress coding gene networks associated with poor outcome in MYCN/c-MYC-activated tumors. Oncogene. 2009; 29(9):1394-404. DOI: 10.1038/onc.2009.429. View

18.

Cho W . OncomiRs: the discovery and progress of microRNAs in cancers. Mol Cancer. 2007; 6:60. PMC: 2098778. DOI: 10.1186/1476-4598-6-60. View

19.

Re A, Cora D, Taverna D, Caselle M . Genome-wide survey of microRNA-transcription factor feed-forward regulatory circuits in human. Mol Biosyst. 2009; 5(8):854-67. PMC: 2898627. DOI: 10.1039/b900177h. View

20.

Kim Y, Kim V . Processing of intronic microRNAs. EMBO J. 2007; 26(3):775-83. PMC: 1794378. DOI: 10.1038/sj.emboj.7601512. View