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

All Science Journal Classification (ASJC) codes

  • Molecular Medicine
  • Molecular Biology

Fingerprint Dive into the research topics of 'Feature genes of hepatitis B virus-positive hepatocellular carcinoma, established by its molecular discrimination approach using prediction analysis of microarray'. Together they form a unique fingerprint.

  • Cite this

    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. (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, 1739(1), 50-61. https://doi.org/10.1016/j.bbadis.2004.07.004