MicroRNA and gene networks in human Hodgkin's lymphoma

Zhu,Minghui; Xu,Zhiwen; Wang,Kunhao; Wang,Ning; Zhu,Mingyang; Wang,Shang

doi:10.3892/mmr.2013.1741

MicroRNA and gene networks in human Hodgkin's lymphoma

Authors:
- Minghui Zhu
- Zhiwen Xu
- Kunhao Wang
- Ning Wang
- Mingyang Zhu
- Shang Wang
View Affiliations

Affiliations: College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, P.R. China
Published online on: October 18, 2013 https://doi.org/10.3892/mmr.2013.1741
Pages: 1747-1754

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )

Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )

Cited By (CrossRef): 0 citations Loading Articles...

Abstract

There has been significant progress in gene and microRNA (miRNA) research with regard to the morbidity of Hodgkin's lymphoma (HL). However, the regulatory mechanisms of genes and miRNAs have yet to be determined. In the current study, the regulatory association between genes, miRNAs and transcription factors (TFs) was investigated to gain an understanding of the mechanisms and key pathways of HL. The association between TFs and miRNAs, miRNAs and target genes and miRNA and its host gene was examined. To show the regulatory correlation clearly, three regulatory networks were hierarchically constructed: Differentially expressed, associated and global networks. Following comparison and analysis of the similarities and differences among the three networks, a number of key pathways, which showed self‑adaptation associations were identified. This included NFκB1 and hsa‑miR‑9, hsa‑miR‑196a‑1 and its host gene HOXB7, which separately forms a self-adaptation association. The differentially expressed network illuminated the pathogenesis of HL. In addition, the associated network further described the regulatory mechanism associated with HL, including prevention, diagnosis, development and therapy. The current study systematically explains the regulatory mechanisms of HL and supplies comprehensive data associated with HL for further studies. With increasing knowledge of the occurrence, mechanism, improvement, metastasis and treatment, an increased understanding of HL may be achieved.

View Figures

View References

1	Gibcus JH, Tan LP, Harms G, et al: Hodgkin lymphoma cell lines are characterized by a specific miRNA expression profile. Neoplasia. 11:167–176. 2009.PubMed/NCBI
2	Xie L, Ushmorov A, Leithäuser F, et al: FOXO1 is a tumor suppressor in classical Hodgkin lymphoma. Blood. 119:3503–3511. 2012. View Article : Google Scholar : PubMed/NCBI
3	Hobert O: Gene regulation by transcription factors and microRNAs. Science. 319:1785–1786. 2008. View Article : Google Scholar : PubMed/NCBI
4	Tran DH, Satou K, Ho TB and Pham TH: Computational discovery of miR-TF regulatory modules in human genome. Bioinformation. 4:371–377. 2010. View Article : Google Scholar : PubMed/NCBI
5	Li M, Li J, Ding X, He M and Cheng SY: microRNA and cancer. AAPS J. 12:309–317. 2010. View Article : Google Scholar
6	Naeem H, Küffner R and Zimmer R: MIRTFnet: analysis of miRNA regulated transcription factors. PLoS One. 6:e225192011. View Article : Google Scholar : PubMed/NCBI
7	Xiao F, Zuo Z, Cai G, et al: miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res. 37(Database issue): D105–D110. 2009.PubMed/NCBI
8	Betel D, Wilson M, Gabow A, Marks DS and Sander C: The microRNA. org resource: targets and expression. Nucleic Acids Res. 36:D149–D153. 2008.PubMed/NCBI
9	Papadopoulos GL, Reczko M, Simossis VA, Sethupathy P and Hatzigeorgiou AG: The database of experimentally supported targets: a functional update of TarBase. Nucleic Acids Res. 37:D155–D158. 2009. View Article : Google Scholar : PubMed/NCBI
10	Hsu SD, Lin FM, Wu WY, et al: miRTarBase: a database curates experimentally validated microRNA-target interactions. Nucleic Acids Res. 39:D163–D169. 2011. View Article : Google Scholar : PubMed/NCBI
11	Rodriguez A, Griffiths-Jones S, Ashurst JL and Bradley A: Identification of mammalian microRNA host genes and transcription units. Genome Res. 14:1902–1910. 2004. View Article : Google Scholar : PubMed/NCBI
12	Baskerville S and Bartel DP: Microarray profiling of microRNAs reveals frequent coexpression with neighboring miRNAs and host genes. RNA. 11:241–247. 2005. View Article : Google Scholar : PubMed/NCBI
13	Cao G, Huang B, Liu Z, et al: Intronic miR-301 feedback regulates its host gene, ska2, in A549 cells by targeting MEOX2 to affect ERK/CREB pathways. Biochem Biophys Res Commun. 396:978–982. 2010. View Article : Google Scholar : PubMed/NCBI
14	Gibcus JH, Tan LP, Harms G, et al: Hodgkin lymphoma cell lines are characterized by a specific miRNA expression profile. Neoplasia. 11:167–176. 2009.PubMed/NCBI
15	Wang J, Lu M, Qiu C and Cui Q: TransmiR: a transcription factor-microRNA regulation database. Nucleic Acids Res. 38:D119–D122. 2010. View Article : Google Scholar : PubMed/NCBI
16	Kozomara A and Griffiths-Jones S: miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res. 39:D152–D157. 2011. View Article : Google Scholar : PubMed/NCBI
17	Safran M, Dalah I, Alexander J, et al: GeneCards Version 3: the human gene integrator. Database (Oxford). 2010:baq0202010. View Article : Google Scholar : PubMed/NCBI
18	Huang da W, Sherman BT and Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protoc. 4:44–57. 2009.PubMed/NCBI
19	Chekmenev DS, Haid C and Kel AE: P-Match: transcription factor binding site search by combining patterns and weight matrices. Nucleic Acids Res. 33:W432–W437. 2005. View Article : Google Scholar : PubMed/NCBI
20	Fujita PA, Rhead B, Zweig AS, et al: The UCSC Genome Browser database: update 2011. Nucleic Acids Res. 39:D876–D882. 2011. View Article : Google Scholar : PubMed/NCBI
21	Jiang Q, Wang Y, Hao Y, et al: miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res. 37:D98–D104. 2009. View Article : Google Scholar : PubMed/NCBI
22	Nie K, Gomez M, Landgraf P, et al: MicroRNA-mediated down-regulation of PRDM1/Blimp-1 in Hodgkin/Reed-Sternberg cells: a potential pathogenetic lesion in Hodgkin lymphomas. Am J Pathol. 173:242–252. 2008. View Article : Google Scholar : PubMed/NCBI
23	Gibcus JH, Kroesen BJ, Koster R, et al: MiR-17/106b seed family regulates p21 in Hodgkin’s lymphoma. J Pathol. 225:609–617. 2011.PubMed/NCBI
24	Smolewski P, Niewiadomska H, Błonski JZ, Robak T and Krykowski E: Expression of proliferating cell nuclear antigen (PCNA) and p53, bcl-2 or C-erb B-2 proteins on Reed-Sternberg cells: prognostic significance in Hodgkin’s disease. Neoplasma. 45:140–147. 1998.
25	Massip A, Arcondéguy T, Touriol C, et al: E2F1 activates p53 transcription through its distal site and participates in apoptosis induction in HPV-positive cells. FEBS Lett. 587:3188–3194. 2013. View Article : Google Scholar : PubMed/NCBI
26	Tanzer A and Stadler PF: Molecular evolution of a microRNA cluster. J Mol Biol. 339:327–335. 2004. View Article : Google Scholar : PubMed/NCBI
27	Feuerborn A, Möritz C, Von Bonin F, Dobbelstein M, Trümper L, Stürzenhofecker B and Kube D: Dysfunctional p53 deletion mutants in cell lines derived from Hodgkin’s lymphoma. Leuk Lymphoma. 47:1932–1940. 2006.PubMed/NCBI
28	Cimmino A, Calin GA, Fabbri M, et al: miR-15 and miR-16 induce apoptosis by targeting BCL2. Proc Natl Acad Sci USA. 102:13944–13949. 2005. View Article : Google Scholar : PubMed/NCBI
29	Ye H, Liu X, Lv M, et al: MicroRNA and transcription factor co-regulatory network analysis reveals miR-19 inhibits CYLD in T-cell acute lymphoblastic leukemia. Nucleic Acids Res. 40:5201–5214. 2012. View Article : Google Scholar : PubMed/NCBI
30	Nagel S, Venturini L, Przybylski GK, et al: Activation of miR-17-92 by NK-like homeodomain proteins suppresses apoptosis via reduction of E2F1 in T-cell acute lymphoblastic leukemia. Leuk Lymphoma. 50:101–108. 2009. View Article : Google Scholar : PubMed/NCBI