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Aim:To investigate the robust gene signature in liver cancer,we applied anintegrated approach to perform a joint analysis of a highly diverse collection ofliver cancer genome-wide datasets,including genomic alterations and transcrip-tion profiles.Methods:1-class Significance Analysis of Microarrays coupled withranking score method were used to identify the robust gene signature in livertumor tissue.Results:In total,1 625 051 gene expression measurements from 16public microarrays,2 pairs of serial analyses of gene expression experiments,and252 loss of heterozygosity reports obtained from 568 publications were used inthis integrated study.The resulting robust gene signatures included 90 genes,which may be of great importance to liver cancer research.A system assessmentanalysis revealed that our integrative method had an accuracy of 92% and acorrelation coefficient value of 0.88.Conclusion:The system assessment resultsindicated that our method had the ability of integrating the datasets from varioustypes of sources,and eliciting more accurate results,as can be very useful in thestudy of liver cancer.
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 datasets, including genomic alterations and transcrip- tion profiles. Methods: 1-class Significance Analysis of Microarrays coupled withranking score method were used to identify the robust gene signature in livertumor tissue. Results: In total, 1 625 051 gene expression measurements from 16 public microarrays, 2 pairs of serial analyzes of gene expression experiments, and 252 loss of heterozygosity reports obtained from 568 publications were used inthis integrated study. The resulting robust gene signatures included 90 genes, which may be of great importance to liver cancer research. A system assessment analysis that that integrative method had an accuracy of 92% and acorrelation coefficient value of 0.88. Confclusion: The system assessment resultsindicated that our method had the ability of integrating the datasets f rom varioustypes of sources, and eliciting more accurate results, as can be very useful in the study of liver cancer.