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

Bu Yeo Kim, Je Geun Lee, Sunhoo Park, Jae Yeon Ahn, Yeun Jin Ju, Jin Haeng Chung, Chul Ju Han, Sook Hyang Jeong, Young Il Yeom, Sangsoo Kim, Yong Sung Lee, Chang Min Kim, Eun Mi Eom, Dong Hee Lee, Kang-Yell Choi, Myung Haing Cho, Kyung Suk Suh, Dong Wook Choi, Kee Ho Lee

Research output: Contribution to journalArticle

22 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)50-61
Number of pages12
JournalBiochimica et Biophysica Acta - Molecular Basis of Disease
Volume1739
Issue number1
DOIs
Publication statusPublished - 2004 Dec 24

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Microarray Analysis
Hepatitis B virus
Hepatocellular Carcinoma
Genes
Learning
Discrimination (Psychology)
Liver
Gene Expression Profiling
Oligonucleotide Array Sequence Analysis
Neoplasms

All Science Journal Classification (ASJC) codes

  • Molecular Medicine
  • Molecular Biology

Cite this

Kim, Bu Yeo ; Lee, Je Geun ; Park, Sunhoo ; Ahn, Jae Yeon ; Ju, Yeun Jin ; Chung, Jin Haeng ; Han, Chul Ju ; Jeong, Sook Hyang ; Yeom, Young Il ; Kim, Sangsoo ; Lee, Yong Sung ; Kim, Chang Min ; Eom, Eun Mi ; Lee, Dong Hee ; Choi, Kang-Yell ; Cho, Myung Haing ; Suh, Kyung Suk ; Choi, Dong Wook ; Lee, Kee Ho. / Feature genes of hepatitis B virus-positive hepatocellular carcinoma, established by its molecular discrimination approach using prediction analysis of microarray. In: Biochimica et Biophysica Acta - Molecular Basis of Disease. 2004 ; Vol. 1739, No. 1. pp. 50-61.
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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.",
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Kim, BY, Lee, JG, Park, S, Ahn, JY, Ju, YJ, Chung, JH, Han, CJ, Jeong, SH, Yeom, YI, Kim, S, Lee, YS, Kim, CM, Eom, EM, Lee, DH, Choi, K-Y, Cho, MH, Suh, KS, Choi, DW & Lee, KH 2004, 'Feature genes of hepatitis B virus-positive hepatocellular carcinoma, established by its molecular discrimination approach using prediction analysis of microarray', Biochimica et Biophysica Acta - Molecular Basis of Disease, vol. 1739, no. 1, pp. 50-61. https://doi.org/10.1016/j.bbadis.2004.07.004

Feature genes of hepatitis B virus-positive hepatocellular carcinoma, established by its molecular discrimination approach using prediction analysis of microarray. / Kim, Bu Yeo; Lee, Je Geun; Park, Sunhoo; Ahn, Jae Yeon; Ju, Yeun Jin; Chung, Jin Haeng; Han, Chul Ju; Jeong, Sook Hyang; Yeom, Young Il; Kim, Sangsoo; Lee, Yong Sung; Kim, Chang Min; Eom, Eun Mi; Lee, Dong Hee; Choi, Kang-Yell; Cho, Myung Haing; Suh, Kyung Suk; Choi, Dong Wook; Lee, Kee Ho.

In: Biochimica et Biophysica Acta - Molecular Basis of Disease, Vol. 1739, No. 1, 24.12.2004, p. 50-61.

Research output: Contribution to journalArticle

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T1 - Feature genes of hepatitis B virus-positive hepatocellular carcinoma, established by its molecular discrimination approach using prediction analysis of microarray

AU - Kim, Bu Yeo

AU - Lee, Je Geun

AU - Park, Sunhoo

AU - Ahn, Jae Yeon

AU - Ju, Yeun Jin

AU - Chung, Jin Haeng

AU - Han, Chul Ju

AU - Jeong, Sook Hyang

AU - Yeom, Young Il

AU - Kim, Sangsoo

AU - Lee, Yong Sung

AU - Kim, Chang Min

AU - Eom, Eun Mi

AU - Lee, Dong Hee

AU - Choi, Kang-Yell

AU - Cho, Myung Haing

AU - Suh, Kyung Suk

AU - Choi, Dong Wook

AU - Lee, Kee Ho

PY - 2004/12/24

Y1 - 2004/12/24

N2 - 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.

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