Abstract
Aim:
To investigate the robust gene signature in liver cancer, we applied an integrated approach to perform a joint analysis of a highly diverse collection of liver cancer genome-wide datasets, including genomic alterations and transcription profiles.
Methods:
1-class Significance Analysis of Microarrays coupled with ranking score method were used to identify the robust gene signature in liver tumor tissue.
Results:
In total, 1 625 051 gene expression measurements from 16 public microarrays, 2 pairs of serial analyses of gene expression experiments, and 252 loss of heterozygosity reports obtained from 568 publications were used in this integrated study. The resulting robust gene signatures included 90 genes, which may be of great importance to liver cancer research. A system assessment analysis revealed that our integrative method had an accuracy of 92% and a correlation coefficient value of 0.88.
Conclusion:
The system assessment results indicated that our method had the ability of integrating the datasets from various types of sources, and eliciting more accurate results, as can be very useful in the study of liver cancer.
Similar content being viewed by others
Article PDF
References
Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, et al. Initial sequencing and analysis of the human genome. Nature 2001; 409: 860–921.
Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, Sutton GG, et al. The sequence of the human genome. Science 2001; 291: 1304–51.
Lamb J, Ramaswamy S, Ford HL, Contreras B, Martinez RV, Kittrell FS, et al. A mechanism of cyclin D1 action encoded in the patterns of gene expression in human cancer. Cell 2003; 114: 323–34.
Tai AL, Mak W, Ng PK, Chua DT, Ng MY, Fu L, et al. High-throughput loss-of-heterozygosity study of chromosome 3p in lung cancer using single-nucleotide polymorphism markers. Cancer Res 2006; 66: 4133–8.
Spanakis NE, Gorgoulis V, Mariatos G, Zacharatos P, Kotsinas A, Garinis G, et al. Aberrant p16 expression is correlated with hemizygous deletions at the 9p21-22 chromosome region in non-small cell lung carcinomas. Anticancer Res 1999; 19: 1893–9.
Hanash S . Integrated global profiling of cancer. Nat Rev Cancer 2004; 4: 638–44.
Beissbarth T, Hyde L, Smyth GK, Job C, Boon WM, Tan SS, et al. Statistical modeling of sequencing errors in SAGE libraries. Bioinformatics 2004; 20: 131–9.
Tusher VG, Tibshirani R, Chu G . Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA 2001; 98: 5116–21.
Efron B, Tibshirani R . Empirical bayes methods and false discovery rates for microarrays. Genet Epidemiol 2002; 23: 70–86.
Zeeberg BR, Qin H, Narasimhan S, Sunshine M, Cao H, Kane DW, et al. High-throughput GoMiner, an ‘industrial-strength’ integrative gene ontology tool for interpretation of multiple-microarray experiments, with application to studies of common variable immune deficiency (CVID). BMC Bioinformatics 2005; 6: 168.
Kim JH, Lee J, Oh B, Kimm K, Koh I . Prediction of phosphorylation sites using SVMs. Bioinformatics 2004; 20: 3179–84.
Author information
Authors and Affiliations
Corresponding author
Additional information
This work is supported by the Key Project of Chinese Ministry of Education (No 104232), Trans-Century Training Program Foundation for the Talents by the Ministry of Education, National Natural Science Foundation of China (No 90412018), and Tsinghua-Yue-Yuen Medical Sciences Fund.
Rights and permissions
About this article
Cite this article
Zhang, Xy., Li, Tt. & Liu, Xj. Detecting robust gene signature through integrated analysis of multiple types of high-throughput data in liver cancer. Acta Pharmacol Sin 28, 2005–2010 (2007). https://doi.org/10.1111/j.1745-7254.2007.00665.x
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.1111/j.1745-7254.2007.00665.x