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Feature genes of hepatitis B virus-positive hepatocellular carcinoma, established by its molecular discrimination approach using prediction analysis of microarray

Cited 24 time in Web of Science Cited 22 time in Scopus
Issue Date
2004-12
Citation
Biochimica et Biophysica Acta - Molecular Basis of Disease, Vol.1739 No.1, pp.50-61
Keywords
hepatocellular carcinomamicroarraylearningprediction
Abstract
Recent introduction of a learning algorithm for cDNA microarray analysis has permitted to select feature set to accurately distinguis: human cancers according to their pathological judgments. Here, we demonstrate that hepatitis B virus-positive hepatocellular carcinom (HCC) could successfully be identified from non-tumor liver tissues by supervised learning analysis of gene expression profiling. Throng learning and cross-validating HCC sample set, we could identify an optimized set of 44 genes to discriminate the status of HCC from nor tumor liver tissues. In an analysis of other blind-tested HCC sample sets, this feature set was found to be statistically significant, indicatin the reproducibility of our molecular discrimination approach with the defined genes. One prominent finding was an asymmetrical distributio pattern of expression profiling in HCC, in which the number of down-regulated genes was greater than that of up-regulated genes. I conclusion, the present findings indicate that application of learning algorithm to HCC may establish a reliable feature set of genes to b useful for therapeutic target of HCC, and that the asymmetric expression pattern may emphasize the importance of suppressed genes in HCC (C) 2004 Elsevier B.V. All rights reserved.
ISSN
0925-4439
URI
https://hdl.handle.net/10371/172326
DOI
https://doi.org/10.1016/j.bbadis.2004.07.004
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College of Veterinary Medicine (수의과대학)Dept. of Veterinary Medicine (수의학과)Journal Papers (저널논문_수의학과)
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