Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2012, Article ID 989637, 9 pages
http://dx.doi.org/10.1100/2012/989637
Research Article

Robust Microarray Meta-Analysis Identifies Differentially Expressed Genes for Clinical Prediction

1Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 313 Ferst Drive, Atlanta, GA 30332, USA
2Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Grady Health System, Grady Memorial Hospital, Atlanta, GA 30303, USA

Received 2 November 2012; Accepted 28 November 2012

Academic Editors: N. S. T. Hirata, M. A. Kon, and K. Najarian

Copyright © 2012 John H. Phan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. K. M. Lin, J. Kang, H. Shin, and J. Lee, “A cube framework for incorporating inter-gene information into biological data mining,” International Journal of Data Mining and Bioinformatics, vol. 3, no. 1, pp. 3–22, 2009. View at Publisher · View at Google Scholar · View at Scopus
  2. J. H. Phan, R. A. Moffitt, T. H. Stokes et al., “Convergence of biomarkers, bioinformatics and nanotechnology for individualized cancer treatment,” Trends in Biotechnology, vol. 27, no. 6, pp. 350–358, 2009. View at Publisher · View at Google Scholar · View at Scopus
  3. D. B. Allison, X. Cui, G. P. Page, and M. Sabripour, “Microarray data analysis: from disarray to consolidation and consensus,” Nature Reviews Genetics, vol. 7, no. 1, pp. 55–65, 2006. View at Publisher · View at Google Scholar · View at Scopus
  4. L. Shi, W. Tong, H. Fang et al., “Cross-platform comparability of microarray technology: intra-platform consistency and appropriate data analysis procedures are essential,” BMC Bioinformatics, vol. 6, no. 2, article S12, 2005. View at Publisher · View at Google Scholar · View at Scopus
  5. B. M. Bolstad, R. A. Irizarry, M. Åstrand, and T. P. Speed, “A comparison of normalization methods for high density oligonucleotide array data based on variance and bias,” Bioinformatics, vol. 19, no. 2, pp. 185–193, 2003. View at Publisher · View at Google Scholar · View at Scopus
  6. P. Stafford and M. Brun, “Three methods for optimization of cross-laboratory and cross-platform microarray expression data,” Nucleic Acids Research, vol. 35, no. 10, article e72, 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. W. E. Johnson, C. Li, and A. Rabinovic, “Adjusting batch effects in microarray expression data using empirical Bayes methods,” Biostatistics, vol. 8, no. 1, pp. 118–127, 2007. View at Publisher · View at Google Scholar · View at Scopus
  8. L. Lusa, L. M. McShane, J. F. Reid et al., “Challenges in projecting clustering results across gene expression-profiling datasets,” Journal of the National Cancer Institute, vol. 99, no. 22, pp. 1715–1723, 2007. View at Publisher · View at Google Scholar · View at Scopus
  9. T. Park, S. G. Yi, Y. K. Shin, and S. Y. Lee, “Combining multiple microarrays in the presence of controlling variables,” Bioinformatics, vol. 22, no. 14, pp. 1682–1689, 2006. View at Publisher · View at Google Scholar · View at Scopus
  10. J. K. Choi, U. Yu, S. Kim, and O. J. Yoo, “Combining multiple microarray studies and modeling interstudy variation,” Bioinformatics, vol. 19, no. 1, pp. i84–i90, 2003. View at Publisher · View at Google Scholar · View at Scopus
  11. J. Wang, K. R. Coombes, W. E. Highsmith, M. J. Keating, and L. V. Abruzzo, “Differences in gene expression between B-cell chronic lymphocytic leukemia and normal B cells: a meta-analysis of three microarray studies,” Bioinformatics, vol. 20, no. 17, pp. 3166–3178, 2004. View at Publisher · View at Google Scholar · View at Scopus
  12. S. Yoon, Y. Yang, J. Choi, and J. Seong, “Large scale data mining approach for gene-specific standardization of microarray gene expression data,” Bioinformatics, vol. 22, no. 23, pp. 2898–2904, 2006. View at Publisher · View at Google Scholar · View at Scopus
  13. R. Breitling and P. Herzyk, “Rank-based methods as a non-parametric alternative of the T-statistic for the analysis of biological microarray data,” Journal of Bioinformatics and Computational Biology, vol. 3, no. 5, pp. 1171–1189, 2005. View at Publisher · View at Google Scholar · View at Scopus
  14. A. Campain and Y. H. Yang, “Comparison study of microarray meta-analysis methods,” BMC Bioinformatics, vol. 11, article 408, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. J. H. Phan, Q. Yin-Goen, A. N. Young, and M. D. Wang, “Improving the efficiency of biomarker identification using biological knowledge,” Pacific Symposium on Biocomputing, pp. 427–438, 2009. View at Google Scholar · View at Scopus
  16. A. N. Schuetz, Q. Yin-Goen, M. B. Amin et al., “Molecular classification of renal tumors by gene expession profiling,” Journal of Molecular Diagnostics, vol. 7, no. 2, pp. 206–218, 2005. View at Google Scholar · View at Scopus
  17. J. Jones, H. Otu, D. Spentzos et al., “Gene signatures of progression and metastasis in renal cell cancer,” Clinical Cancer Research, vol. 11, no. 16, pp. 5730–5739, 2005. View at Publisher · View at Google Scholar · View at Scopus
  18. E. J. Kort, L. Farber, M. Tretiakova et al., “The E2F3-oncomir-1 axis is activated in Wilms' tumor,” Cancer Research, vol. 68, no. 11, pp. 4034–4038, 2008. View at Publisher · View at Google Scholar · View at Scopus
  19. M. V. Yusenko, R. P. Kuiper, T. Boethe et al., “High-resolution DNA copy number and gene expression analyses distinguish chromophobe renal cell carcinomas and renal oncocytomas,” BMC Cancer, vol. 9, article 152, 2009. View at Publisher · View at Google Scholar · View at Scopus
  20. M. V. Yusenko, D. Zubakov, and G. Kovacs, “Gene expression profiling of chromophobe renal cell carcinomas and renal oncocytomas by Affymetrix GeneChip using pooled and individual tumours,” International Journal of Biological Sciences, vol. 5, no. 6, pp. 517–527, 2009. View at Google Scholar · View at Scopus
  21. J. P. T. Higgins, R. Shinghal, H. Gill et al., “Gene expression patterns in renal cell carcinoma assessed by complementary DNA microarray,” American Journal of Pathology, vol. 162, no. 3, pp. 925–932, 2003. View at Google Scholar · View at Scopus
  22. L. Shi, G. Campbell, W. D. Jones et al., “The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models,” Nature Biotechnology, vol. 28, no. 8, pp. 827–838, 2010. View at Google Scholar
  23. L. D. Miller, J. Smeds, J. George et al., “An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 38, pp. 13550–13555, 2005. View at Publisher · View at Google Scholar · View at Scopus
  24. C. Sotiriou, P. Wirapati, S. Loi et al., “Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis,” Journal of the National Cancer Institute, vol. 98, no. 4, pp. 262–272, 2006. View at Publisher · View at Google Scholar · View at Scopus
  25. A. J. Minn, G. P. Gupta, P. M. Siegel et al., “Genes that mediate breast cancer metastasis to lung,” Nature, vol. 436, no. 7050, pp. 518–524, 2005. View at Publisher · View at Google Scholar · View at Scopus
  26. M. J. van de Vijver, Y. D. He, L. J. van 't Veer et al., “A gene-expression signature as a predictor of survival in breast cancer,” The New England Journal of Medicine, vol. 347, no. 25, pp. 1999–2009, 2002. View at Publisher · View at Google Scholar · View at Scopus
  27. L. Badea, V. Herlea, S. O. Dima, T. Dumitrascu, and I. Popescu, “Combined gene expression analysis of whole-tissue and microdissected pancreatic ductal adenocarcinoma identifies genes specifically overexpressed in tumor epithelia,” Hepato-Gastroenterology, vol. 55, no. 88, pp. 2016–2027, 2008. View at Google Scholar · View at Scopus
  28. H. Pei, L. Li, B. L. Fridley et al., “FKBP51 affects cancer cell response to chemotherapy by negatively regulating Akt,” Cancer Cell, vol. 16, no. 3, pp. 259–266, 2009. View at Publisher · View at Google Scholar · View at Scopus
  29. M. Ishikawa, K. Yoshida, Y. Yamashita et al., “Experimental trial for diagnosis of pancreatic ductal carcinoma based on gene expression profiles of pancreatic ductal cells,” Cancer Science, vol. 96, no. 7, pp. 387–393, 2005. View at Publisher · View at Google Scholar · View at Scopus
  30. C. Pilarsky, O. Ammerpohl, B. Sipos et al., “Activation of Wnt signalling in stroma from pancreatic cancer identified by gene expression profiling,” Journal of Cellular and Molecular Medicine, vol. 12, no. 6B, pp. 2823–2835, 2008. View at Publisher · View at Google Scholar · View at Scopus
  31. C. A. Iacobuzio-Donahue, A. Maitra, M. Olsen et al., “Exploration of global gene expression patterns in pancreatic adenocarcinoma using cDNA microarrays,” American Journal of Pathology, vol. 162, no. 4, pp. 1151–1162, 2003. View at Google Scholar · View at Scopus
  32. V. G. Tusher, R. Tibshirani, and G. Chu, “Significance analysis of microarrays applied to the ionizing radiation response,” Proceedings of the National Academy of Sciences of the United States of America, vol. 98, no. 9, pp. 5116–5121, 2001. View at Publisher · View at Google Scholar · View at Scopus
  33. C. Ding and H. Peng, “Minimum redundancy feature selection from microarray gene expression data,” Journal of Bioinformatics and Computational Biology, vol. 3, no. 2, pp. 185–205, 2005. View at Publisher · View at Google Scholar · View at Scopus
  34. I. Bièche, I. Girault, E. Urbain, S. Tozlu, and R. Lidereau, “Relationship between intratumoral expression of genes coding for xenobiotic-metabolizing enzymes and benefit from adjuvant tamoxifen in estrogen receptor alpha-positive postmenopausal breast carcinoma,” Breast Cancer Research, vol. 6, no. 3, pp. R252–R263, 2004. View at Google Scholar · View at Scopus
  35. T. Z. Parris, A. Danielsson, S. Nemes et al., “Clinical implications of gene dosage and gene expression patterns in diploid breast carcinoma,” Clinical Cancer Research, vol. 16, no. 15, pp. 3860–3874, 2010. View at Publisher · View at Google Scholar · View at Scopus
  36. S. J. Prest, F. E. May, and B. R. Westley, “The estrogen-regulated protein, TFF1, stimulates migration of human breast cancer cells,” The FASEB Journal, vol. 16, no. 6, pp. 592–594, 2002. View at Google Scholar · View at Scopus
  37. H. Liu, A. R. Brannon, A. R. Reddy et al., “Identifying mRNA targets of microRNA dysregulated in cancer: with application to clear cell Renal Cell Carcinoma,” BMC Systems Biology, vol. 4, article 51, 2010. View at Publisher · View at Google Scholar · View at Scopus
  38. J. J. Morrissey, A. N. London, J. Luo, and E. D. Kharasch, “Urinary biomarkers for the early diagnosis of kidney cancer,” Mayo Clinic Proceedings, vol. 85, no. 5, pp. 413–421, 2010. View at Publisher · View at Google Scholar · View at Scopus
  39. T. Arumugam, D. M. Simeone, K. Van Golen, and C. D. Logsdon, “S100P promotes pancreatic cancer growth, survival, and invasion,” Clinical Cancer Research, vol. 11, no. 15, pp. 5356–5364, 2005. View at Publisher · View at Google Scholar · View at Scopus
  40. H. J. Whiteman, M. E. Weeks, S. E. Dowen et al., “The role of S100P in the invasion of pancreatic cancer cells is mediated through cytoskeletal changes and regulation of cathepsin D,” Cancer Research, vol. 67, no. 18, pp. 8633–8642, 2007. View at Publisher · View at Google Scholar · View at Scopus
  41. M. Katayama, A. Funakoshi, T. Sumii, N. Sanzen, and K. Sekiguchi, “Laminin γ2-chain fragment circulating level increases in patients with metastatic pancreatic ductal cell adenocarcinomas,” Cancer Letters, vol. 225, no. 1, pp. 167–176, 2005. View at Publisher · View at Google Scholar · View at Scopus