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.
| Published in | Computational Biology and Bioinformatics (Volume 5, Issue 3) |
| DOI | 10.11648/j.cbb.20170503.12 |
| 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), 2017. Published by Science Publishing Group |
HCC, HBV, HCV, DEGs, Hamiltonian Energy, Network Modelling
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APA Style
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
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
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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Download@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://article.sciencepublishinggroup.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}
}
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 Y1 - 2017/08/25 PY - 2017 N1 - https://doi.org/10.11648/j.cbb.20170503.12 DO - 10.11648/j.cbb.20170503.12 T2 - Computational Biology and Bioinformatics JF - Computational Biology and Bioinformatics JO - Computational Biology and Bioinformatics SP - 36 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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