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

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Title
Feature genes of hepatitis B virus-positive hepatocellular carcinoma, established by its molecular discrimination approach using prediction analysis of microarray
Author(s)
B Y Kim; J G Lee; S Park; J Y Ahn; Y J Ju; J H Chung; C J Han; S H Jeong; Young Il Yeom; Sang Soo Kim; Y S Lee; C M Kim; E M Eom; D H Lee; K Y Choi; M H Cho; K S Suh; D W Choi; K H Lee
Bibliographic Citation
Biochimica et Biophysica Acta-Molecular Basis of Disease, vol. 1739, no. 1, pp. 50-61
Publication Year
2004
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.
Keyword
Hepatocellular carcinomaLearningMicroarrayPrediction
ISSN
0925-4439
Publisher
Elsevier
DOI
http://dx.doi.org/10.1016/j.bbadis.2004.07.004
Type
Article
Appears in Collections:
Division of Biomedical Research > Personalized Genomic Medicine Research Center > 1. Journal Articles
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