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 language | English |
|---|---|
| Pages (from-to) | 50-61 |
| Number of pages | 12 |
| Journal | Biochimica et Biophysica Acta - Molecular Basis of Disease |
| Volume | 1739 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2004 Dec 24 |
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All Science Journal Classification (ASJC) codes
- Molecular Medicine
- Molecular Biology
Cite this
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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 journal › Article
TY - JOUR
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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=10644291862&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=10644291862&partnerID=8YFLogxK
U2 - 10.1016/j.bbadis.2004.07.004
DO - 10.1016/j.bbadis.2004.07.004
M3 - Article
VL - 1739
SP - 50
EP - 61
JO - Biochimica et Biophysica Acta - Molecular Basis of Disease
JF - Biochimica et Biophysica Acta - Molecular Basis of Disease
SN - 0925-4439
IS - 1
ER -