New network topology approaches reveal differential correlation patterns in breast cancer

BMC Systems Biology

Volumn 7, Issue , 2013, Pages

  Bockmayr, Michael ^a   Klauschen, Frederick ^a   Györffy, Balazs ^b   Denkert, Carsten ^a   Budczies, Jan ^a  

^a CHARITÉ UNIVERSITÄTSMEDIZIN BERLIN   (Germany)

^b INSTITUTE OF EXPERIMENTAL MEDICINE   (Hungary)

Author keywords

Breast cancer; Differential co expression; Differential correlation; Microarray data; Molecular subtypes

Indexed keywords

TRANSCRIPTOME;

ALGORITHM; BREAST TUMOR; GENE REGULATORY NETWORK; GENETICS; HUMAN; PROCEDURES; REPRODUCIBILITY; SYSTEMS BIOLOGY; TIME; TUMOR GENE; ARTICLE; METHODOLOGY;

ALGORITHMS; BREAST NEOPLASMS; GENE REGULATORY NETWORKS; GENES, NEOPLASM; HUMANS; REPRODUCIBILITY OF RESULTS; SYSTEMS BIOLOGY; TIME FACTORS; TRANSCRIPTOME;

EID: 84884541854     PISSN: None     EISSN: 17520509     Source Type: Journal    
DOI: 10.1186/1752-0509-7-78     Document Type: Article

References (43)

1. Allison D, Cui X, Page G, Sabripour M. Microarray data analysis: from disarray to consolidation and consensus. Nat Rev Genet 2006, 7:55-65. 10.1038/nrg1749, 16369572.
2. Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Nat Acad Sci 1998, 95(25):14863-14868. 10.1073/pnas.95.25.14863, 24541, 9843981.
3. Sørlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M, Jeffrey SS, Thorsen T, Quist H, Matese JC, Brown PO, Botstein D, Lønning PE, Børresen-Dale AL. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Nat Acad Sci 2001, 98(19):10869-10874. 10.1073/pnas.191367098, 58566, 11553815.
4. Li KC. Genome-wide coexpression dynamics: Theory and application. Proc Nat Acad Sci 2002, 99(26):16875-16880. 10.1073/pnas.252466999, 139237, 12486219.
5. Lai Y, Wu B, Chen L, Zhao H. A statistical method for identifying differential gene-gene co-expression patterns. Bioinformatics 2004, 20(17):3146-3155. 10.1093/bioinformatics/bth379, 15231528.
6. Kostka D, Spang R. Finding disease specific alterations in the co-expression of genes. Bioinformatics 2004, 20(Suppl 1):i194-i199. 10.1093/bioinformatics/bth909, 15262799.
7. de la Fuente A. From 'differential expression' to 'differential networking' - identification of dysfunctional regulatory networks in diseases. Trends Genet 2010, 26(7):326-333. 10.1016/j.tig.2010.05.001, 20570387.
8. Tesson B, Breitling R, Jansen R. DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules. BMC Bioinformatics 2010, 11:497. 10.1186/1471-2105-11-497, 2976757, 20925918.
9. Choi Y, Kendziorski C. Statistical methods for gene set co-expression analysis. Bioinformatics 2009, 25(21):2780-2786. 10.1093/bioinformatics/btp502, 2781749, 19689953.
10. Southworth L, Owen A, Kim S. Aging mice show a decreasing correlation of gene expression within genetic modules. PLoS Genet 2009, 5(12):e1000776. 10.1371/journal.pgen.1000776, 2788246, 20019809.
11. Taylor I, Linding R, Warde-Farley D, Liu Y, Pesquita C. Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat Biotechnol 2009, 27:199-204. 10.1038/nbt.1522, 19182785.
12. Teschendorff A, Severini S. Increased entropy of signal transduction in the cancer metastasis phenotype. BMC Syst Biol 2010, 4:104. 10.1186/1752-0509-4-104, 2925356, 20673354.
13. Ihmels J, Bergmann S, Berman J, Barkai N. Comparative gene expression analysis by differential clustering approach: application to the Candida albicans transcription program. PLoS Genet 2005, 1:e39. 10.1371/journal.pgen.0010039, 1239936, 16470937.
14. Watson M. CoXpress: differential co-expression in gene expression data. BMC Bioinformatics 2006, 7:509-509. 10.1186/1471-2105-7-509, 1660556, 17116249.
15. Choi J, Yu U, Yoo O, Kim S. Differential coexpression analysis using microarray data and its application to human cancer. Bioinformatics (Oxford, England) 2005, 21(24):4348-4355.
16. Voy B, Schar J, Perkins A, Saxton A, Borate B, Chesler E, Branstetter L, Langston M. Extracting gene networks for low-dose radiation using graph theoretical algorithms. PloS Comput Biol 2006, 2(7):e89. 10.1371/journal.pcbi.0020089, 1513268, 16854212.
17. Zhang B, Li H, Riggins RB, Zhan M, Xuan J, Zhang Z, Hoffman EP, Clarke R, Wang Y. Differential dependency network analysis to identify condition-specific topological changes in biological networks. Bioinformatics 2009, 25(4):526-532. 10.1093/bioinformatics/btn660, 2642641, 19112081.
18. Altay G, Asim M, Markowetz F, Neal D. Differential C3NET reveals disease networks of direct physical interactions. BMC Bioinformatics 2011, 12:296. 10.1186/1471-2105-12-296, 3156794, 21777411.
19. Amar D, Safer H, Shamir R. Dissection of regulatory networks that are altered in disease via differential co-expression. PLoS Comput Biol 2013, 9(3):e1002955. 10.1371/journal.pcbi.1002955, 3591264, 23505361.
20. Gruvberger S, Ringnér M, Chen Y, Panavally S, Saal LH, Borg A, Fernö M, Peterson C, Meltzer PS. Estrogen receptor status in breast cancer is associated with remarkably distinct gene expression patterns. Cancer Res 2001, 61(16):5979-5984.
21. van't Veer L, Dai H, van de Vijver M, He Y, Hart A, Mao M, Peterse H, van der Kooy K, Marton M, Witteveen A, Schreiber G, Kerkhoven R, Roberts C, Linsley P, Bernards R, Friend S. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002, 415(6871):530-536. 10.1038/415530a, 11823860.
22. Budczies J, Weichert W, Noske A, Müller B, Weller C, Wittenberger T, Hofmann H, Dietel M, Denkert C, Gekeler V. Genome-wide gene expression profiling of formalin-fixed paraffin-embedded breast cancer core biopsies using microarrays. J Histochem Cytochem 2011, 59(2):146-157. 10.1369/jhc.2010.956607, 3201135, 21339180.
23. Freudenberg J, Sivaganesan S, Wagner M, Medvedovic M. A semi-parametric Bayesian model for unsupervised differential co-expression analysis. BMC Bioinformatics 2010, 11:234. [http://www.biomedcentral.com/1471-2105/11/234], 10.1186/1471-2105-11-234, 2876132, 20459663.
24. Tegge AN, Caldwell CW, Xu D. Pathway correlation profile of gene-gene co-expression for identifying pathway perturbation. PLoS ONE 2012, 7(12):e52127. 10.1371/journal.pone.0052127, 3527387, 23284898.
25. R Development Core Team R: A Language and Environment for Statistical Computing 2011, Vienna, Austria: R Foundation for Statistical Computing, [http://www.R-project.org/] [ISBN 3-900051-07-0], R Development Core Team.
26. Steiger J. Test for comparing elements of a correlation matrix. Psychol Bull 1980, 87:245-251.
27. Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proc Nat Acad Sci 2003, 100(16):9440-9445. 10.1073/pnas.1530509100, 170937, 12883005.
28. Edgar R, Domrachev M, Lash AE. Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 2002, 30:207-210. 10.1093/nar/30.1.207, 99122, 11752295.
29. Györffy B, Lanczky A, Eklund AC, Denkert C, Budczies J, Li Q, Szallasi Z. An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients. Breast Cancer Res Treat 2010, 123(3):725-731. 10.1007/s10549-009-0674-9, 20020197.
30. Gautier L, Cope L, Bolstad BM, Irizarry RA. Affy-analysis of affymetrix GeneChip data at the probe level. Bioinformatics 2004, 20(3):307-315. 10.1093/bioinformatics/btg405, 14960456.
31. Müller BM, Kronenwett R, Hennig G, Euting H, Weber K, Bohmann K, Weichert W, Altmann G, Roth C, Winzer KJ, Kristiansen G, Petry C, Dietel M, Denkert C. Quantitative determination of estrogen receptor, progesterone receptor, and HER2 mRNA in formalin-fixed paraffin-embedded tissue-a new option for predictive biomarker assessment in breast cancer. Diagn Mol Pathol 2011, 20:1-10. 10.1097/PDM.0b013e3181e3630c, 21326033.
32. Csardi G, Nepusz T. The igraph software package for complex network research. InterJournal Complex Systems 2006,, 1695.
33. Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 2011, 27(3):431-432. 10.1093/bioinformatics/btq675, 3031041, 21149340.
34. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID Bioinformatics resources. Nat Protoc 2009, 4(1):44-57.
35. Huang DW, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 2009, 37(1):1-13. 10.1093/nar/gkn923, 2615629, 19033363.
36. Bauer K, Parise C, Caggiano V. Use of ER/PR/HER2 subtypes in conjunction with the 2007 St Gallen consensus statement for early breast cancer. BMC Cancer 2010, 10:228. 10.1186/1471-2407-10-228, 2886044, 20492696.
37. Celis JE, Gromov P, Cabezon T, Moreira JMA, Friis E, Jirstrom K, Llombart-Bosch A, Timmermans-Wielenga V, Rank F, Gromova I. 15-Prostaglandin dehydrogenase expression alone or in combination with ACSM1 defines a subgroup of the apocrine molecular subtype of breast carcinoma. Mol Cell Proteomics 2008, 7(10):1795-1809. 10.1074/mcp.R800011-MCP200, 18632593.
38. Celis JE, Cabezon T, Moreira JM, Gromov P, Gromova I, Timmermans-Wielenga V, Iwase T, Akiyama F, Honma N, Rank F. Molecular characterization of apocrine carcinoma of the breast: Validation of an apocrine protein signature in a well-defined cohort. Mol Oncol 2009, 3(3):220-237. 10.1016/j.molonc.2009.01.005, 19393583.
39. Staaf J, Jonsson G, Ringner M, Vallon-Christersson J, Grabau D, Arason A, Gunnarsson H, et al. High-resolution genomic and expression analyses of copy number alterations in HER2-amplified breast cancer. Breast Cancer Res 2010, 12(3):R25. 10.1186/bcr2568, 2917012, 20459607.
40. Doane A, Danso M, Lal P, Donaton M, Zhang L, Hudis C, Gerald W. An estrogen receptor-negative breast cancer subset characterized by a hormonally regulated transcriptional program and response to androgen. Oncogene 2006, 25(28):3994-4008. 10.1038/sj.onc.1209415, 16491124.
41. Ray PS, Wang J, Qu Y, Sim MS, Shamonki J, Bagaria SP, Ye X, Liu B, Elashoff D, Hoon DS, Walter MA, Martens JW, Richardson AL, Giuliano AE, Cui X. FOXC1 is a potential prognostic biomarker with functional significance in basal-like breast cancer. Cancer Res 2010, 70(10):3870-3876. 10.1158/0008-5472.CAN-09-4120, 20406990.
42. Loibl S, Müller B, von Minckwitz G, Schwabe M, Roller M, Darb-Esfahani S, Ataseven B, du Bois A, Fissler-Eckhoff A, Gerber B, Kulmer U, Alles J, Mehta K, Denkert C. Androgen receptor expression in primary breast cancer and its predictive and prognostic value in patients treated with neoadjuvant chemotherapy. Breast Cancer Res Treat 2011, 130(2):477-87. 10.1007/s10549-011-1715-8, 21837479.
43. Smith I, Procter M, Gelber R, Guillaume S, Feyereislova A, Dowsett M, Goldhirsch A, Untch M, Mariani G, Baselga J. 2-year follow-up of trastuzumab after adjuvant chemotherapy in HER2-positive breast cancer: a randomised controlled trial. Lancet 2007, 369(9555):29-36. 10.1016/S0140-6736(07)60028-2, 17208639.