Prediction of epigenetically regulated genes in breast cancer cell lines

BMC Bioinformatics

Volumn 11, Issue , 2010, Pages

  Loss, Leandro A ^a   Sadanandam, Anguraj ^a   Durinck, Steffen ^a   Nautiyal, Shivani ^b   Flaucher, Diane ^b   Carlton, Victoria E H ^b   Moorhead, Martin ^b   Lu, Yontao ^b   Gray, Joe W ^a   Faham, Malek ^b   Spellman, Paul ^a   Parvin, Bahram ^a  

^a LAWRENCE BERKELEY NATIONAL LABORATORY   (United States)

^b Thermo Fisher Scientific   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL PROTOCOLS; DIMENSIONALITY REDUCTION; EPIGENETIC REGULATION; GENE EXPRESSION DATA; HIGH-THROUGHPUT TECHNOLOGIES; LOGISTIC REGRESSIONS; NEGATIVE CORRELATION; REGIONS OF INTEREST;

ALKYLATION; CELL CULTURE; CELLS; DISEASES; GENE EXPRESSION; METHYLATION; PIPELINES; REDUCTION; REGRESSION ANALYSIS;

DATA REDUCTION;

COLLAGEN TYPE 1; COLLAGEN TYPE I, ALPHA 1 CHAIN; DNA BINDING PROTEIN; DNA TOPOISOMERASE (ATP HYDROLYSING); DNA TOPOISOMERASE II ALPHA; TFF1 PROTEIN, HUMAN; TUMOR ANTIGEN; TUMOR SUPPRESSOR PROTEIN; VAV PROTEIN; VAV3 PROTEIN, HUMAN;

ARTICLE; BREAST TUMOR; CPG ISLAND; DNA METHYLATION; DNA MICROARRAY; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENETIC ASSOCIATION; GENETIC EPIGENESIS; GENETICS; HUMAN; METABOLISM; PATHOLOGY; PROMOTER REGION; TUMOR CELL LINE; TUMOR SUPPRESSOR GENE;

ANTIGENS, NEOPLASM; BREAST NEOPLASMS; CELL LINE, TUMOR; COLLAGEN TYPE I; CPG ISLANDS; DNA METHYLATION; DNA TOPOISOMERASES, TYPE II, EUKARYOTIC; DNA-BINDING PROTEINS; EPIGENESIS, GENETIC; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION, NEOPLASTIC; GENES, TUMOR SUPPRESSOR; GENOME-WIDE ASSOCIATION STUDY; HUMANS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PROMOTER REGIONS, GENETIC; PROTO-ONCOGENE PROTEINS C-VAV; TUMOR SUPPRESSOR PROTEINS;

EID: 77953007415     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-11-305     Document Type: Article

References (33)

1. Russo VEA, Martienssen RA, Riggs AD. Epigenetic mechanism of gene regulation. 1996, Cold Spring Harbor Laboratory Press.
2. Dillon N. Gene regulation and large scale chromatin organization in the nucleus. Chromosome Research 2006, 14:117-126. 10.1007/s10577-006-1027-8, 16506101.
3. Esteller M. Cancer epigenomics: DNA methylomes and histon modification maps. Nature Review Genetics 2007, 286-298.
4. Bock C, Lengauer T. Computational epigenetic. Bioinformatics 2008, 24:1-10. 10.1093/bioinformatics/btm546, 18024971.
5. Jones PA, Baylin SB. The fundamental role of epigenetic events in cancer. Nature Rev Genetics 2002, 3:415-428.
6. Esteller M. CpG island hypermethylation and tumor suppressor genes: a booming present, a brighter future. Oncogene 2002, 21:5427-5440. 10.1038/sj.onc.1205600, 12154405.
7. Widschwendter M, Jiang G, Woods C, Müller HM, Fiegl H, Goebel G, Marth C, Müller-Holzner E, Zeimet AG, Laird PW, Ehrlich M. DNA Hypomethylation and Ovarian Cancer Biology. Cancer Research 2004, 64:4472-4480. 10.1158/0008-5472.CAN-04-0238, 15231656.
8. Laird PW. The power and the promise of DNA methylation markers. Nature Rev Cancer 2003, 3(4):253-266.
9. Nautiyal S, Carlton V, Lu Y, Ireland J, Flaucher D, Moorhead M, Gray J, Spellman P, Mindrinos M, Berg P, Faham M. A High-Throughput Method for Analyzing Methylation of CpGs in Targeted Genomic Regions. PNAS 123138, 12084816.
10. Sjahputera O, Keller JM, Davis JW, Taylor KH, Rahmatpanah F, Shi H, Anderson DT, Blisard SN, Luke RH, Popescu M, Arthur GC, Caldwell CW. Relational Analysis of CpG Islands Methylation and Gene Expression in Human Lymphomas Using Possibilistic C-Means Clustering and Modified Cluster Fuzzy Density. IEEE Transactions on Computational Biology and Bioinformatics 2007, 4(2):176-189. 10.1109/TCBB.2007.070205, 17473312.
11. Chung F. Spectral graph theory. 1997, 92. CBMS Regional Conference Series in Mathematics, Amercan Mathematical Society.
12. Shi J, Malik J. Normalized Cuts and Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 2000, 22:888-905.
13. Ng AY, Jordan MI, Weiss Y. On spectral clustering: Analysis and an algorithm. Advances in Neural Information Processing Systems 2001, 14:849-856.
14. Bellman R. Adaptive Control Processes: A Guided Tour. 1961, Princeton University Press.
15. Hartigan JA, Wong MA. A K-Means Clustering Algorithm. Applied Statistics 1979, 28:100-108.
16. Jain A, Dubes R. Algorithms for Clustering Data. 1988, Englewood Cliffs, NJ: Prentice Hall.
17. Neve RM, Chin K, Fridlyand J, Yeh J, Baehner FL, Fevr T, Clark L, Bayani N, Coppe JP, Tong F, Speed T, Spellman PT, DeVries S, Lapuk A, Wang NJ, Kuo WL, Stilwell JL, Pinkel D, Albertson DG, Waldman FM, McCormick F, Dickson RB, Johnson MD, Lippman M, Ethier S, Gazdar A, Gray JW. A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes. Cancer cell 2006, 10:515-527. 10.1016/j.ccr.2006.10.008, 2730521, 17157791.
18. http://cancergenome.nih.gov/dataportal/data/about/types/sequencing/
19. Sengupta PK, Smith EM, Kim K, Murnane MJ, Smith BD. DNA hypermethylation near the transcription start site of collagen alpha2(I) gene occurs in both cancer cell lines and primary colorectal cancers. Cancer research 2003, 63:1789-1797.
20. Anderton JA, Lindsey JC, Lusher ME, Gilbertson RJ, Bailey S, Ellison DW, Clifford SC. Global analysis of the medulloblastoma epigenome identifies disease-subgroup-specific inactivation of COL1A2. Neuro-oncology 2008, 10:981-994. 10.1215/15228517-2008-048, 2719012, 18664619.
21. Chiba T, Yokosuka O, Fukai K, Hirasawa Y, Tada M, Mikata R, Imazeki F, Taniguchi H, Iwama A, Miyazaki M, Ochiai T, Saisho H. Identification and investigation of methylated genes in hepatoma. Eur J Cancer 2005, 41:1185-1194. 10.1016/j.ejca.2005.02.014, 15911243.
22. Brasel JC, Schmidt M, Fischbach T, Sültmann H, Bojar H, Koelbl H, Hellwig B, Rahnenführer J, Hengstler JG, Gehrmann MC. ERBB2 and TOP2A in Breast Cancer: A Comprehensive Analysis of Gene Amplification, RNA Levels, and Protein Expression and Their Influence on Prognosis and Prediction. Clinical Cancer Research 2010, 16(2391).
23. Rody A, Karn T, Ruckhaberle E, Muller V, Gehrmann M, Solbach C, Ahr A, Gatje R, Holtrich U, Kaufmann M. Gene expression of topoisomerase II alpha (TOP2A) by microarray analysis is highly prognostic in estrogen receptor (ER) positive breast cancre. Breast cancer research and treatment 2009, 113:457-466. 10.1007/s10549-008-9964-x, 18340528.
24. Nguyen A, Rauch TA, Pfeifer GP, Hu VW. Global methylation profiling of lymphoblastoid cell lines reveals epigenetic contributions to autism spectrum disorders and a novel autism candidate gene, RORA, whose protein product is reduced in autistic brain. FJ Express 10.1096/fj.fj.10-154484 2010,
25. Davidson B, Stavnes HT, Holth A, Chen X, Yang Y, Shih IM, L WT. Gene expression signatures differentiate ovarian/peritoneal serous carcinoma from breast carcinoma in effusions. J Cell Mol Med 2010,
26. Dietrich D, Lesche R, Tetzner R, Krispin M, Dietrich J, Haedicke W, Schuster M, Kristiansen G. Analysis of DNA methylation of multiple genes in microdissected cells from formalin-fixed and paraffin-embedded tissues. J Histochem Cytochem 2009, 57(5):477-489. 10.1369/jhc.2009.953026, 2674771, 19153192.
27. Martin V, Ribieras S, Song-Wang XG, Lasne Y, Frappart L, Rio MC, Dante R. Involvement of DNA methylation in the control of the expression of an estrogen-induced breast-cancer-associated protein (pS2) in human breast cancers. Journal of cellular biochemistry 1997, 65:95-106. 10.1002/(SICI)1097-4644(199704)65:1<95::AID-JCB10>3.0.CO;2-G, 9138084.
28. Lee K, Liu Y, Mo JQ, Zhang J, Dong Z, Lu S. Vav3 oncogene activates estrogen receptor and its overexpression may be involved in human breast cancer. BMC cancer 2008, 8:158. 10.1186/1471-2407-8-158, 2430719, 18518979.
29. Borg A, Sandberg T, Nilsson K, Johannsson O, Klinker M, Masback A, Westerdahl J, Olsson H, Ingvar C. High frequency of multiple melanomas and breast and pancreas carcinomas in CDKN2A mutation-positive melanoma families. Journal of the National Cancer Institute 2000, 92:1260-1265. 10.1093/jnci/92.15.1260, 10922411.
30. Johnson N, Speirs V, Curtin NJ, Hall AG. A comparative study of genome-wide SNP, CGH microarray and protein expression analysis to explore genotypic and phenotypic mechanisms of acquired antiestrogen resistance in breast cancer. Breast cancer research and treatment 2008, 111:55-63. 10.1007/s10549-007-9758-6, 17899364.
31. Hui AM, Shi YZ, Li X, Takayama T, Makuuchi M. Loss of p16(INK4) protein, alone and together with loss of retinoblastoma protein, correlate with hepatocellular carcinoma progression. Cancer letters 2000, 154:93-99. 10.1016/S0304-3835(00)00385-2, 10799744.
32. Bardeesy N, Aguirre AJ, Chu GC, Cheng KH, Lopez L, Hezel AF, Feng B, Brennan C, Weissleder R, Mahmood U, Hanahan D, Redston MS, et al. Both p16(Ink4a) and the p19(Arf)-p53 pathway constrain progression of pancreatic adenocarcinoma in the mouse. Proceedings of the National Academy of Sciences of the United States of America 2006, 103:5947-5952. 10.1073/pnas.0601273103, 1458678, 16585505.
33. Wicki R, Franz C, Scholl FA, Heizmann CW, Schafer BW. Repression of the candidate tumor suppressor gene S100A2 in breast cancer is mediated by site-specific hypermethylation. Cell Calcium 1997, 22(4):243-254. 10.1016/S0143-4160(97)90063-4, 9481475.