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Characterization of Top Hub Genes in Breast and Lung Cancer Using Functional Association

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Book cover Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1057))

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Abstract

Identification of disease gene in cancer is a complex problem, not due to the lack of methods, but because of the lack of standard validation procedure for identified genes. This issue is addressed in this paper, where we first select disease markers from both microarray and RNA-seq gene expression data of breast and lung cancer, and then validate the obtained gene set by the method of functional similarity with the known cancer genes. The classification procedure using kernel support vector machine (KSVM) is applied to find functional similarity of the hub genes from the protein–protein-interaction (PPI) network of identified genes, and a set of top genes of interest is extracted. We then construct a common cancer network and observe its functional characteristics in cancer. The results highlight the enrichment of this network in important cancer pathways, and thus validate the association of the identified gene set with the disease. It is anticipated that including more cancer types in the experimentation may help validate the drug targets, particularly common to these cancer types.

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Acknowledgements

We acknowledge Dr. Hemant Purohit, Engineering Genomics Division, CSIR-National Environmental Engineering Research Institute [Nagpur (MS), India] for his incessant support and motivation during the course of this research. We also thank Dr Dhananjay V. Raje for valuable guidance.

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Authors and Affiliations

  1. Visvesvaraya National Institute of Technology, Nagpur, India

    Richa K. Makhijani & Shital A. Raut

Authors
  1. Richa K. Makhijani
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  2. Shital A. Raut
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Corresponding author

Correspondence to Richa K. Makhijani .

Editor information

Editors and Affiliations

  1. Department of Mathematics, National Institute of Technology Silchar, Silchar, Assam, India

    Kedar Nath Das

  2. Department of Mathematics, South Asian University, New Delhi, Delhi, India

    Jagdish Chand Bansal

  3. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  4. Department of Mathematics, Faculty of Science, Liverpool Hope University, Liverpool, UK

    Atulya K. Nagar

  5. School of Electrical Engineering, VIT University, Vellore, Tamil Nadu, India

    Ponnambalam Pathipooranam

  6. School of Electrical Engineering, VIT University, Vellore, Tamil Nadu, India

    Rani Chinnappa Naidu

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Makhijani, R.K., Raut, S.A. (2020). Characterization of Top Hub Genes in Breast and Lung Cancer Using Functional Association. In: Das, K., Bansal, J., Deep, K., Nagar, A., Pathipooranam, P., Naidu, R. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1057. Springer, Singapore. https://doi.org/10.1007/978-981-15-0184-5_42

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