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International Conference on Intelligent Computing

ICIC 2014: Intelligent Computing in Bioinformatics pp 445–455Cite as

Integrative Analysis of Gene Expression and Promoter Methylation during Reprogramming of a Non-Small-Cell Lung Cancer Cell Line Using Principal Component Analysis-Based Unsupervised Feature Extraction

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 8590))

Abstract

Cancer cells are to some extent regarded as similar to undifferentiated cells, such as embryonic stem cells and induced pluripotent cells. However, cancer cells can be reprogrammed using standard reprogramming procedures. Thus, it would be interesting to observe the result of cancer cell reprogramming. In this paper, we reanalyzed publically available mRNA expression and promoter methylation profiles during reprogramming of non-small-cell lung cancer cell lines, using the recently proposed principal component analysis-based unsupervised feature extraction. Six genes, TGFBI, S100A6, CSRP1, CLDN11, PRKCDBP, and CRIP1, were commonly found (P = 0.003) in the 100 top-ranked genes with aberrant expression or aberrant promoter methylation. Because all six genes were related to cancer in the literature, they might be new therapeutic targets for treatment of non-small-cell lung cancer.

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Author information

Authors and Affiliations

  1. Department of Physics, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo, 112-8551, Japan

    Y. -h. Taguchi

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  1. Y. -h. Taguchi
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Editors and Affiliations

  1. School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    De-Shuang Huang

  2. School of Computer Science and Engineering Inha University, Incheon, South Korea

    Kyungsook Han

  3. Department of Biotechnology, Indian Institute of Technology Madras, 600 036, Chennai, Tamilnadu, India

    Michael Gromiha

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© 2014 Springer International Publishing Switzerland

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Taguchi, Y.h. (2014). Integrative Analysis of Gene Expression and Promoter Methylation during Reprogramming of a Non-Small-Cell Lung Cancer Cell Line Using Principal Component Analysis-Based Unsupervised Feature Extraction. In: Huang, DS., Han, K., Gromiha, M. (eds) Intelligent Computing in Bioinformatics. ICIC 2014. Lecture Notes in Computer Science(), vol 8590. Springer, Cham. https://doi.org/10.1007/978-3-319-09330-7_52

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  • DOI: https://doi.org/10.1007/978-3-319-09330-7_52

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09329-1

  • Online ISBN: 978-3-319-09330-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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