Discovering clinical biomarkers of chronic hepatitis B by mining mutation hotspots

Chun Pei Cheng, Pei Fen Lee, Chu Yu Chin, Wen Chun Liu, I. Chin Wu, Ting Tsung Chang, Vincent S. Tseng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Hepatitis B virus (HBV) is the most common DNA virus that may cause hepatitis, cirrhosis and hepatocellular carcinoma. Although many people are persistently infected with HBV, the serum viral load and host immune response varies from person to person. Because the high rate of mutations in HBV protein sequences will alter the protein expressions and even their functions, in this paper, we explore to discover clinical biomarkers of chronic hepatitis B by mining mutation hotspots. A one year follow-up study was conducted with a total number of 1,694 clones from 23 patients with HBeAg-positive chronic hepatitis B. Serum alanine aminotransferase, HBV DNA and HBeAg levels were monthly measured and used as the criteria for clustering the patients into different subgroups. Using monthly derived HBV precore/core protein sequences, we analyzed amino acid mutations responsible for serologic and clinical outcome of each patient subgroup. Using an integration of covariance network and point mutation rule methods, we identified several representative covariance networks of each patient subgroup. Validation with literature-curated mutation hotspots showed that the identified mutations were strongly associated with the viral loads, presence of HBeAg-seroconversion in sera, HBV genotypes and amino acid properties. We further used these identified networks containing mutation hotspots to develop a feature tree, which is applicable for clinicians to prescribe patients a suitable treatment at early stage of HBV infection even though the patients exhibit no obvious symptoms.

Original languageEnglish
Title of host publicationProceedings - 2011 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2011
Pages51-56
Number of pages6
DOIs
Publication statusPublished - 2011 Dec 1
Event16th Annual Conference on Technologies and Applications of Artificial Intelligence, TAAI 2011 - Chung-Li, Taiwan
Duration: 2011 Nov 112011 Nov 13

Publication series

NameProceedings - 2011 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2011

Other

Other16th Annual Conference on Technologies and Applications of Artificial Intelligence, TAAI 2011
CountryTaiwan
CityChung-Li
Period11-11-1111-11-13

Fingerprint

Biomarkers
Viruses
Proteins
Amino acids
DNA

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications

Cite this

Cheng, C. P., Lee, P. F., Chin, C. Y., Liu, W. C., Wu, I. C., Chang, T. T., & Tseng, V. S. (2011). Discovering clinical biomarkers of chronic hepatitis B by mining mutation hotspots. In Proceedings - 2011 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2011 (pp. 51-56). [6120719] (Proceedings - 2011 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2011). https://doi.org/10.1109/TAAI.2011.17
Cheng, Chun Pei ; Lee, Pei Fen ; Chin, Chu Yu ; Liu, Wen Chun ; Wu, I. Chin ; Chang, Ting Tsung ; Tseng, Vincent S. / Discovering clinical biomarkers of chronic hepatitis B by mining mutation hotspots. Proceedings - 2011 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2011. 2011. pp. 51-56 (Proceedings - 2011 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2011).
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abstract = "Hepatitis B virus (HBV) is the most common DNA virus that may cause hepatitis, cirrhosis and hepatocellular carcinoma. Although many people are persistently infected with HBV, the serum viral load and host immune response varies from person to person. Because the high rate of mutations in HBV protein sequences will alter the protein expressions and even their functions, in this paper, we explore to discover clinical biomarkers of chronic hepatitis B by mining mutation hotspots. A one year follow-up study was conducted with a total number of 1,694 clones from 23 patients with HBeAg-positive chronic hepatitis B. Serum alanine aminotransferase, HBV DNA and HBeAg levels were monthly measured and used as the criteria for clustering the patients into different subgroups. Using monthly derived HBV precore/core protein sequences, we analyzed amino acid mutations responsible for serologic and clinical outcome of each patient subgroup. Using an integration of covariance network and point mutation rule methods, we identified several representative covariance networks of each patient subgroup. Validation with literature-curated mutation hotspots showed that the identified mutations were strongly associated with the viral loads, presence of HBeAg-seroconversion in sera, HBV genotypes and amino acid properties. We further used these identified networks containing mutation hotspots to develop a feature tree, which is applicable for clinicians to prescribe patients a suitable treatment at early stage of HBV infection even though the patients exhibit no obvious symptoms.",
author = "Cheng, {Chun Pei} and Lee, {Pei Fen} and Chin, {Chu Yu} and Liu, {Wen Chun} and Wu, {I. Chin} and Chang, {Ting Tsung} and Tseng, {Vincent S.}",
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Cheng, CP, Lee, PF, Chin, CY, Liu, WC, Wu, IC, Chang, TT & Tseng, VS 2011, Discovering clinical biomarkers of chronic hepatitis B by mining mutation hotspots. in Proceedings - 2011 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2011., 6120719, Proceedings - 2011 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2011, pp. 51-56, 16th Annual Conference on Technologies and Applications of Artificial Intelligence, TAAI 2011, Chung-Li, Taiwan, 11-11-11. https://doi.org/10.1109/TAAI.2011.17

Discovering clinical biomarkers of chronic hepatitis B by mining mutation hotspots. / Cheng, Chun Pei; Lee, Pei Fen; Chin, Chu Yu; Liu, Wen Chun; Wu, I. Chin; Chang, Ting Tsung; Tseng, Vincent S.

Proceedings - 2011 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2011. 2011. p. 51-56 6120719 (Proceedings - 2011 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2011).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Cheng, Chun Pei

AU - Lee, Pei Fen

AU - Chin, Chu Yu

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AU - Chang, Ting Tsung

AU - Tseng, Vincent S.

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N2 - Hepatitis B virus (HBV) is the most common DNA virus that may cause hepatitis, cirrhosis and hepatocellular carcinoma. Although many people are persistently infected with HBV, the serum viral load and host immune response varies from person to person. Because the high rate of mutations in HBV protein sequences will alter the protein expressions and even their functions, in this paper, we explore to discover clinical biomarkers of chronic hepatitis B by mining mutation hotspots. A one year follow-up study was conducted with a total number of 1,694 clones from 23 patients with HBeAg-positive chronic hepatitis B. Serum alanine aminotransferase, HBV DNA and HBeAg levels were monthly measured and used as the criteria for clustering the patients into different subgroups. Using monthly derived HBV precore/core protein sequences, we analyzed amino acid mutations responsible for serologic and clinical outcome of each patient subgroup. Using an integration of covariance network and point mutation rule methods, we identified several representative covariance networks of each patient subgroup. Validation with literature-curated mutation hotspots showed that the identified mutations were strongly associated with the viral loads, presence of HBeAg-seroconversion in sera, HBV genotypes and amino acid properties. We further used these identified networks containing mutation hotspots to develop a feature tree, which is applicable for clinicians to prescribe patients a suitable treatment at early stage of HBV infection even though the patients exhibit no obvious symptoms.

AB - Hepatitis B virus (HBV) is the most common DNA virus that may cause hepatitis, cirrhosis and hepatocellular carcinoma. Although many people are persistently infected with HBV, the serum viral load and host immune response varies from person to person. Because the high rate of mutations in HBV protein sequences will alter the protein expressions and even their functions, in this paper, we explore to discover clinical biomarkers of chronic hepatitis B by mining mutation hotspots. A one year follow-up study was conducted with a total number of 1,694 clones from 23 patients with HBeAg-positive chronic hepatitis B. Serum alanine aminotransferase, HBV DNA and HBeAg levels were monthly measured and used as the criteria for clustering the patients into different subgroups. Using monthly derived HBV precore/core protein sequences, we analyzed amino acid mutations responsible for serologic and clinical outcome of each patient subgroup. Using an integration of covariance network and point mutation rule methods, we identified several representative covariance networks of each patient subgroup. Validation with literature-curated mutation hotspots showed that the identified mutations were strongly associated with the viral loads, presence of HBeAg-seroconversion in sera, HBV genotypes and amino acid properties. We further used these identified networks containing mutation hotspots to develop a feature tree, which is applicable for clinicians to prescribe patients a suitable treatment at early stage of HBV infection even though the patients exhibit no obvious symptoms.

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U2 - 10.1109/TAAI.2011.17

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M3 - Conference contribution

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BT - Proceedings - 2011 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2011

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Cheng CP, Lee PF, Chin CY, Liu WC, Wu IC, Chang TT et al. Discovering clinical biomarkers of chronic hepatitis B by mining mutation hotspots. In Proceedings - 2011 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2011. 2011. p. 51-56. 6120719. (Proceedings - 2011 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2011). https://doi.org/10.1109/TAAI.2011.17