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

Title
Feature genes of hepatitis B virus-positive hepatocellular carcinoma, established by its molecular discrimination approach using prediction analysis of microarray
Authors
Kim B.-Y.Lee J.-G.Park S.Ahn J.-Y.Ju Y.-J.Chung J.-H.Han C.J.Jeong S.-H.Yeom Y.I.Kim S.Lee Y.-S.Kim C.-M.Eom E.-M.Lee D.-H.Choi K.-Y.Cho M.-H.Suh K.-S.Choi D.-W.Lee K.-H.
Ewha Authors
이동희
SCOPUS Author ID
이동희scopus
Issue Date
2004
Journal Title
Biochimica et Biophysica Acta - Molecular Basis of Disease
ISSN
0925-4439JCR Link
Citation
Biochimica et Biophysica Acta - Molecular Basis of Disease vol. 1739, no. 1, pp. 50 - 61
Indexed
SCI; SCIE; SCOPUS WOS scopus
Document Type
Article
Abstract
Recent introduction of a learning algorithm for cDNA microarray analysis has permitted to select feature set to accurately distinguish human cancers according to their pathological judgments. Here, we demonstrate that hepatitis B virus-positive hepatocellular carcinoma (HCC) could successfully be identified from non-tumor liver tissues by supervised learning analysis of gene expression profiling. Through learning and cross-validating HCC sample set, we could identify an optimized set of 44 genes to discriminate the status of HCC from non-tumor liver tissues. In an analysis of other blind-tested HCC sample sets, this feature set was found to be statistically significant, indicating the reproducibility of our molecular discrimination approach with the defined genes. One prominent finding was an asymmetrical distribution pattern of expression profiling in HCC, in which the number of down-regulated genes was greater than that of up-regulated genes. In conclusion, the present findings indicate that application of learning algorithm to HCC may establish a reliable feature set of genes to be useful for therapeutic target of HCC, and that the asymmetric expression pattern may emphasize the importance of suppressed genes in HCC. © 2004 Elsevier B.V. All rights reserved.
DOI
10.1016/j.bbadis.2004.07.004
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일반대학원 > 바이오융합과학과 > Journal papers
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