Computational Biology and Bioinformatics

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In-Silico Screening of Biomarker Genes of Hepatocellular Carcinoma Using R/Bioconductor

Received: 26 July 2017    Accepted: 07 August 2017    Published: 25 August 2017
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Abstract

Hepatocellular Carcinoma is a primary malignancy of the liver. It is the fifth most common cancer around the world and is a leading cause of cancer related deaths. For about 40 years HCC has been predominantly linked with Hepatitis B and Hepatitis C infection. This work aims to find out potential biomarkers for HBV and HCV infected HCC through rigorous computational analyses. This was achieved by collecting gene expression microarray data from GEO (Gene Expression Omnibus) database as GSE series (GSE38941, GSE26495, GSE51489, GSE41804, GSE49954, GSE16593) and pre-processing it using Bioconductor repository for R. Following a robust mechanism including the use of statistical testing techniques and tools, the data was screened for DEGs (Differentially Expressed Genes). 3354 down regulated genes and 785 up regulated genes for HBV and 3462 down regulated and 251 up regulated genes for HCV were obtained. For a comparative study of DEGs from HBV and HCV, they were merged to look for potential biomarkers whose differential expression may result in carcinoma. A total of 17 biomarkers (1 up-regulated and 16 downregulated), was obtained which were further subjected to Cytoscape to generate a GRN using STRING app. Furthermore, module level analysis was performed as it offers robustness and a better understanding of complex GRNs. The work also focuses on the topological properties of the network. The results point out to the presence of a hierarchical framework in the network. They also shed a light on the interactions of biomarkers whose down regulation may result in HCC. These results can be used for future research and in exploring drug targets for this disease.

DOI 10.11648/j.cbb.20170503.12
Published in Computational Biology and Bioinformatics (Volume 5, Issue 3, June 2017)
Page(s) 36-42
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

HCC, HBV, HCV, DEGs, Hamiltonian Energy, Network Modelling

References
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[4] V. W. Keng, D. A. Largaespada, and A. Villanueva, “Why men are at higher risk for hepatocellular carcinoma?,” J. Hepatol., vol. 57, no. 2, pp. 453–454, Aug. 2012.
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[7] S. A. Jones, D. N. Clark, F. Cao, J. E. Tavis, and J. Hu, “Comparative Analysis of Hepatitis B Virus Polymerase Sequences Required for Viral RNA Binding, RNA Packaging, and Protein Priming,” J. Virol., vol. 88, no. 3, pp. 1564–1572, Feb. 2014.
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Author Information
  • Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India

  • Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India

  • Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India

  • Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India

  • Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India

  • Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India

  • Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India

  • Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India

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    Afza Akbar, Mohd Murshad Ahmed, Safia Tazyeen, Aftab Alam, Anam Farooqui, et al. (2017). In-Silico Screening of Biomarker Genes of Hepatocellular Carcinoma Using R/Bioconductor. Computational Biology and Bioinformatics, 5(3), 36-42. https://doi.org/10.11648/j.cbb.20170503.12

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    ACS Style

    Afza Akbar; Mohd Murshad Ahmed; Safia Tazyeen; Aftab Alam; Anam Farooqui, et al. In-Silico Screening of Biomarker Genes of Hepatocellular Carcinoma Using R/Bioconductor. Comput. Biol. Bioinform. 2017, 5(3), 36-42. doi: 10.11648/j.cbb.20170503.12

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    AMA Style

    Afza Akbar, Mohd Murshad Ahmed, Safia Tazyeen, Aftab Alam, Anam Farooqui, et al. In-Silico Screening of Biomarker Genes of Hepatocellular Carcinoma Using R/Bioconductor. Comput Biol Bioinform. 2017;5(3):36-42. doi: 10.11648/j.cbb.20170503.12

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  • @article{10.11648/j.cbb.20170503.12,
      author = {Afza Akbar and Mohd Murshad Ahmed and Safia Tazyeen and Aftab Alam and Anam Farooqui and Shahnawaz Ali and Md. Zubbair Malik and Romana Ishrat},
      title = {In-Silico Screening of Biomarker Genes of Hepatocellular Carcinoma Using R/Bioconductor},
      journal = {Computational Biology and Bioinformatics},
      volume = {5},
      number = {3},
      pages = {36-42},
      doi = {10.11648/j.cbb.20170503.12},
      url = {https://doi.org/10.11648/j.cbb.20170503.12},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.cbb.20170503.12},
      abstract = {Hepatocellular Carcinoma is a primary malignancy of the liver. It is the fifth most common cancer around the world and is a leading cause of cancer related deaths. For about 40 years HCC has been predominantly linked with Hepatitis B and Hepatitis C infection. This work aims to find out potential biomarkers for HBV and HCV infected HCC through rigorous computational analyses. This was achieved by collecting gene expression microarray data from GEO (Gene Expression Omnibus) database as GSE series (GSE38941, GSE26495, GSE51489, GSE41804, GSE49954, GSE16593) and pre-processing it using Bioconductor repository for R. Following a robust mechanism including the use of statistical testing techniques and tools, the data was screened for DEGs (Differentially Expressed Genes). 3354 down regulated genes and 785 up regulated genes for HBV and 3462 down regulated and 251 up regulated genes for HCV were obtained. For a comparative study of DEGs from HBV and HCV, they were merged to look for potential biomarkers whose differential expression may result in carcinoma. A total of 17 biomarkers (1 up-regulated and 16 downregulated), was obtained which were further subjected to Cytoscape to generate a GRN using STRING app. Furthermore, module level analysis was performed as it offers robustness and a better understanding of complex GRNs. The work also focuses on the topological properties of the network. The results point out to the presence of a hierarchical framework in the network. They also shed a light on the interactions of biomarkers whose down regulation may result in HCC. These results can be used for future research and in exploring drug targets for this disease.},
     year = {2017}
    }
    

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  • TY  - JOUR
    T1  - In-Silico Screening of Biomarker Genes of Hepatocellular Carcinoma Using R/Bioconductor
    AU  - Afza Akbar
    AU  - Mohd Murshad Ahmed
    AU  - Safia Tazyeen
    AU  - Aftab Alam
    AU  - Anam Farooqui
    AU  - Shahnawaz Ali
    AU  - Md. Zubbair Malik
    AU  - Romana Ishrat
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    T2  - Computational Biology and Bioinformatics
    JF  - Computational Biology and Bioinformatics
    JO  - Computational Biology and Bioinformatics
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    EP  - 42
    PB  - Science Publishing Group
    SN  - 2330-8281
    UR  - https://doi.org/10.11648/j.cbb.20170503.12
    AB  - Hepatocellular Carcinoma is a primary malignancy of the liver. It is the fifth most common cancer around the world and is a leading cause of cancer related deaths. For about 40 years HCC has been predominantly linked with Hepatitis B and Hepatitis C infection. This work aims to find out potential biomarkers for HBV and HCV infected HCC through rigorous computational analyses. This was achieved by collecting gene expression microarray data from GEO (Gene Expression Omnibus) database as GSE series (GSE38941, GSE26495, GSE51489, GSE41804, GSE49954, GSE16593) and pre-processing it using Bioconductor repository for R. Following a robust mechanism including the use of statistical testing techniques and tools, the data was screened for DEGs (Differentially Expressed Genes). 3354 down regulated genes and 785 up regulated genes for HBV and 3462 down regulated and 251 up regulated genes for HCV were obtained. For a comparative study of DEGs from HBV and HCV, they were merged to look for potential biomarkers whose differential expression may result in carcinoma. A total of 17 biomarkers (1 up-regulated and 16 downregulated), was obtained which were further subjected to Cytoscape to generate a GRN using STRING app. Furthermore, module level analysis was performed as it offers robustness and a better understanding of complex GRNs. The work also focuses on the topological properties of the network. The results point out to the presence of a hierarchical framework in the network. They also shed a light on the interactions of biomarkers whose down regulation may result in HCC. These results can be used for future research and in exploring drug targets for this disease.
    VL  - 5
    IS  - 3
    ER  - 

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