TC-VGC: A Tumor Classification System using Variations in Genes' Correlation

Computer Methods and Programs in Biomedicine

Volumn 104, Issue 3, 2011, Pages

  Shin, Eunji ^a   Yoon, Youngmi ^b   Ahn, Jaegyoon ^a   Park, Sanghyun ^a  

^a Yonsei University   (South Korea)

^b Gachon University   (South Korea)

Author keywords

Cancer specific genetic network; Gene gene correlation; Microarray data analysis; Tumor classification

Indexed keywords

BIOLOGICAL FEATURES; CANCER CLASSIFICATION; CLASSIFICATION ANALYSIS; GENE-GENE CORRELATION; GENETIC NETWORKS; LEARNING MODELS; MARKER GENES; MICROARRAY DATA; MICROARRAY DATA ANALYSIS; PROSTATE CANCERS; TUMOR CLASSIFICATION; TUMOR TISSUES;

CLASSIFICATION (OF INFORMATION); DATA REDUCTION; DISEASES; TUMORS;

GENES;

ALGORITHM; ARTICLE; CANCER CLASSIFICATION; CORRELATIONAL STUDY; MICROARRAY ANALYSIS; PROSTATE CANCER; TUMOR CLASSIFICATION; TUMOR GENE;

ALGORITHMS; HUMANS; NEOPLASMS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS;

EID: 80655124719     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2011.03.002     Document Type: Article

References (68)

1. Duggan D.J., Bittner M., Chen Y., Meltzer P., Trent J.M. Expression profiling using cDNA microarray. Nature Genetics Supplement 1999, 21:p.10-14.
2. Brazma A., Hingamp P., Quackenbush J., Sherlock G., Spellman P., et al. Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nature Genetics 2001, 29:365-371.
3. Bernal A., Crammer K., Hatzigeorgiou A., Pereira F. Global discriminative learning for higher-accuracy computational gene prediction. PLoS Computational Biology 2007, 3:488-497.
4. Furey T.S., Cristianini N., Duffy N., Bednarski D.W., Schummer M., Haussler D. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 2000, 16:906-914.
5. Nanni L., Lumini A. An ensemble of K-local hyperplanes for predicting protein-protein interactions. Bioinformatics 2006, 22:1207-1210.
6. Kamber M., Han J. Data Mining: Concepts and Techniques 2006, Morgan Kaufmann.
7. Pirooznia M., Yang J.Y., Yang M.Q., Deng Y. A comparative study of different machine learning methods on microarray gene expression data. BMC Genomics 2008.
8. Wang Y., Tetko L.V., Hall M.A., Frankb E., Facius A., Mayer K.F.X., Mewes F.W. Gene selection from microarray data for cancer classification-a machine learning approach. Computational Biology and Chemistry 2005, 37-45.
9. Guyon I., Weston J., Barnhill S., Vapnik V. Gene selection for cancer classification using support vector machines. Machine Learning 2001, 389-422.
10. Duan K.B., Rajapakse J.C., Wang H., Azuaje F. Multiple SVM-RFE for gene selection in cancer classification with expression data. IEEE Transactions on NanoBioscience 2005, 4:228-234.
11. Pan F., Wang B., Perrizzo W. Comprehensive vertical sample-based k-NN/LSVM classification for gene expression analysis. Journal of Biomedical Informatics 2004, 37:241-249.
12. Diaz-Uriarte R., Alvarez de Andres S. Gene selection and classification of microarray data using random forest. BMC Bioinformatics 2006, 7.
13. Albrecht A.A. Stochastic local search for the feature set problem, with applications to microarray data. Applied Mathematics and Computation 2006, 183(2):1148-1164.
14. Gheyas I.A., Smith L.S. Feature subset selection in large dimensionality domains. Pattern Recognition 2010, 43(1):5-13.
15. Jaeger J., Sengupta R., Ruzzo W.L. Improved gene selection for classification of microarrays. Pacific Symposium on Biocomputing 2003, 8:53-64.
16. Berretta R., Costa W., Moscato P. Combinatorial optimization models for finding genetic signatures from gene expression datasets. Bioinformatics 2008, 453:363-377.
17. Dougherty E.R., Brun M. On the number of close-to-optimal feature sets. Cancer Informatics 2006, 189-196.
18. Jolliffe I.T. Principal Component Analysis, Springer Series in Statistics 2002, p. 28.
19. Hori G., Inoue M., Nishimura S.I., Nakahara H. Blind gene classification-an application of a signal separation method. Genome Informatics 2001, 12:255-256.
20. Tan A., Naiman D., Xu L., Winslow R., Geman D. Simple decision rules for classifying human cancers from gene expression profiles. Bioinformatics 2005, 21:3896-3904.
21. Wei X., Li K.C. Exploring the within- and between-class correlation distributions for tumor classification. PNAS 2010, 107(15):6737-6742.
22. Ravetti M.G., Rosso O.A., Berretta R., Moscato P. Uncovering molecular biomarkers that correlate cognitive decline with the changes of hippocampus'gene expression profiles in Alzheimer's disease. PLoS 2010, 5(4):e10153.
23. Taylor I.W., Linding R., Warde-Farley D., Liu Y., Pesquita C., et al. Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nature Biotechnology 2009, 27:199-204.
24. Lehmann E.L., D'Abrera H.J.M. Nonparametrics: Statistical Methods Based on Ranks 1998, Prentice-Hall, Englewood Cliffs, NJ, pp. 292, 300, and 323.
25. He X., Zhang J. Why do hubs tend to be essential in protein networks?. PLoS Genetics 2006, 2(6).
26. S. Horvath, J. Dong and Y. Yip, Connectivity, Module-Conformity, and Significance: Understanding Gene Co-Expression Network Methods, UCLA Technical Report, 2006.
27. Yonsei University, Cancer Metastasis Research Center, Yonsei University College of Medicine, South Korea.
28. Singh D., Febbo P.G., Ross K., Jackson D.G., Manola J., Ladd C. Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 2002, 1:203-209.
29. Welsh J.B., Sapinoso L.M., Su A.I., Kern S.G., Wang-Rodriguez J., Moskaluk C.A. Analysis of gene expression identifies candidate markers and pharmacological targets in prostate cancer. Cancer Research 2001, 61:5974-5978.
30. LaTulippe E., Satagopan J., Smith A., Scher H., Scardino P., Reuter V. Comprehensive gene expression analysis of prostate cancer reveals distinct transcriptional programs associated with metastatic disease. Cancer Research 2002, 62:4499-4506.
31. Stuart R., Wachsman W., Berry C.C., Wang-Rodriguez J., Wasserman L., et al. In silico dissection of cell-type-associated patterns of gene expression in prostate cancer. Proceedings of National Academy of Sciences of the United States of America 2004, 101:615-620.
32. Landi M.T., Dracheva T., Rotunno M., Figueroa J.D., Liu H., et al. Gene expression signature of cigarette smoking and its role in lung adenocarcinoma development and survival. PLoS One 2008, 3(2):e1651.
33. Hou J., Aerts J., Hamer B.D., Ijcken W.V., Bakker M.D., et al. Gene expression-based classification of non-small cell lung carcinomas and survival prediction. PLoS One 2010, 5(4):e10312.
34. David F.N. The moments of the z and F distributions. Biometrika 1949, 36:394-403.
35. Witten I.H., Frank E. Data Mining: Practical Machine Learning Tools and Techniques 2005, Morgan Kaufmann, San Francisco. 2nd ed.
36. Kononenko I. Estimating attributes: analysis and extensions of relief. Proceedings of ECML 1994, 784:171-182.
37. Press W.H., Flannery B.P., Teukolsky S.A., Vetterling W.T. Numerical Recipes in C 1988, Cambridge University Press, Cambridge.
38. Shannon P., Markiel A., Ozier O., Baliga N.S., Wang J.T., et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Research 2003, 13:2498-2504.
39. Maglott D., Ostell J., Pruitt K.D., Tatusova T., et al. Entrez Gene: gene-centered information at NCBI. Nucleic Acids Research 2005, 33:54-58.
40. Wang H., Sun D., Ji P., Mohler J., Zhu L. An AR-Skp2 pathway for proliferation of androgen-dependent prostate-cancer cells. Journal of Cell Science 2008, 121:2578-2587.
41. Kitagawa M., Lee S.H., McCormick F. Skp2 suppresses p53-dependent apoptosis by inhibiting p300. Molecular Cell 2008, 29(2):217-231.
42. Jalava S.E., Porkka K.P., Rauhala H.E., Isotalo J., Tammela T.L., et al. TCEB1 promotes invasion of prostate cancer cells. International Journal of Cancer 2009, 124(1):95-102.
43. Kremer C.L., Klein R.R., Mendelson J., Browne W., Samadzedeh L.K., et al. Expression of mTOR signaling pathway markers in prostate cancer progression. The Prostate 2006, 66(11):1203-1212.
44. Bau D.T., Wu H.C., Chiu C.F., Lin C.C., Hsu C.M., et al. Association of XPD polymorphisms with prostate cancer in Taiwanese patients. Anticancer Research 2007, 27(4C):2893-2896.
45. F. Wang, D. Chang, F.L. Hu, H. Sui, B. Han, et al., DNA repair gene XPD polymorphisms and cancer risk: a meta-analysis based on 56 case-control studies, Cancer Epidemiology, Biomarkers & Prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 17 (3) (2008) 507-517.
46. Gudmundsson J., Sulem P., Rafnar1 T., Bergthorsson J.T., Manolescu A., et al. Common sequence variants on 2p15 and Xp1122 confer susceptibility to prostate cancer. Nature Genetics 2008, 40:281-283.
47. Ranganathan S., Salazar H., Benetatos C.A., Hudes G.R. Immunohistochemical analysis of β-tubulin isotypes in human prostate carcinoma and benign prostate hypertrophy. Prostate 1997, 30:263-268.
48. Walss-Bass C., Xu K., David S., Fellous A., Luduena R.F. Occurrence of nuclear beta(II)-tubulin in cultured cells. Cell and Tissue Research 2002, 308(2):p.215-223.
49. Yamashita S., Takahashi S., McDonell N., Watanabe N., Niwa T., et al. Methylation silencing of transforming growth factor-β receptor type II in rat prostate cancers. Cancer Research 2008, 68:2112-2121.
50. Orso F., Penna E., Cimino D., Astanina E., Maione F., et al. AP-2alpha and AP-2gamma regulate tumor progression via specific genetic programs. The FASEB Journal 2008, 22(8):2702-2714.
51. Kadaja L., Laos S., Maimets T. Overexpression of leukocyte marker CD43 causes activation of the tumor suppressor proteins p53 and ARF. Oncogene 2004, 23(14):2523-2530.
52. Sila A., Chan H., Loh A.X., Phang H.Q., Wong E.T., et al. The POPX2 phosphatase regulates cancer cell motility and invasiveness. Cell Cycle 2010, 9(1):179-187.
53. Flonta S.E., Arena S., Pisacane A., Michieli P., Bardelli A. Expression and functional regulation of myoglobin in epithelial cancers. The American Journal of Pathology 2009, 175(1):201-206.
54. Renier C., Vogel H., Offor O., Yao C., Wapnir I. Breast cancer brain metastases express the sodium iodide symporter. Journal of Neuro-Oncology 2010, 96(3):331-336.
55. Micalizzi D.S., Christensen K.L., Jedlicka P., Coletta R.D., Baron A.E., et al. The Six1 homeoprotein induces human mammary carcinoma cells to undergo epithelial-mesenchymal transition and metastasis in mice through increasing TGF-beta signaling. The Journal of Clinical Investigation 2009, 119(9):2678-2690.
56. McCoy E.L., Iwanaga R., Jedlicka P., Abbey N.S., Chodosh L.A., et al. Six1 expands the mouse mammary epithelial stem/progenitor cell pool and induces mammary tumors that undergo epithelial-mesenchymal transition. The Journal of Clinical Investigation 2009, 119(9):2663-2677.
57. Ohashi Y., Kaneko S.J., Cupples T.E., Young S.R. Ubiquinol cytochrome c reductase (UQCRFS1) gene amplification in primary breast cancer core biopsy samples. Gynecology Oncology 2004, 93(1):54-58.
58. Kendall J., Liu Q., Bakleh A., Krasnitz A., Nguyen K.C.Q., et al. Oncogenic cooperation and coamplification of developmental transcription factor genes in lung cancer. Proceedings of the National Academy of Sciences of the United States of America 2007.
59. Lee M.K., Kim J.H., Lee C.H., Kim J.M., Kang C.D., et al. Clinicopathological significance of BGP expression in non-small-cell lung carcinoma: relationship with histological type, microvessel density and patients' survival. Pathology 2006, 38(6):555-560.
60. Graflund M., Sorbe B., Karlsson M. MIB-1, p53, bcl-2, and WAF-1 expression in pelvic lymph nodes and primary tumors in early stage cervical carcinomas: correlation with clinical outcome. International Journal of Oncology 2002, 20(5):1041-1047.
61. Ryschich E., Khamidjanov A., Kerkadze V., Vachtang, Buchler M.W., et al. Promotion of tumor cell migration by extracellular matrix proteins in human pancreatic cancer. Pancreas 2009, 38(7):804-810.
62. Shema E., Tirosh I., Aylon Y., Huang J., Ye C., et al. The histone H2B-specific ubiquitin ligase RNF20/hBRE1 acts as a putative tumor suppressor through selective regulation of gene expression. Genes & Development 2008, 22:2664-2676.
63. Wang P., Ma Q., Luo J.D., Liu B., Tan F.Q., et al. Nkx31 and p27KIP1 cooperate in proliferation inhibition and apoptosis induction in human androgen-independent prostate cancer cells. Cancer Investigation 2009, 27:369-375.
64. Chenette E.J. Cell cycle: Akt Skps through. Nature Reviews Cancer 2009, 9:316-317.
65. Wagner E.F., Nebreda A.R. Signal integration by JNK and p38 MAPK pathways in cancer development. Nature Reviews Cancer 2009, 9:537-549.
66. Coppe J.P., Itahana Y., Moore D.H., Bennington J.L., Desprez P.Y. Id-1 and Id-2 proteins as molecular markers for human prostate cancer progression. Clinical Cancer Research 2004, 10:2044-2051.
67. Cavarretta I.T., Neuwirt H., Zaki M.H., Steiner H., Hobisch A., et al. Mcl-1 is Regulated by IL-6 and Mediates the Survival Activity of the Cytokine in a Model of Late Stage Prostate Carcinoma 2008, Springer, pp. 547-555.
68. Kim amd K.U., Cho S.B. Ensemble classifiers based on correlation analysis for DNA microarray classification. Neurocomputing 2006, 70:187-199.