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Inferring novel genes related to oral cancer with a network embedding method and one-class learning algorithms

Gene Therapy volume 26, pages 465–478 (2019)Cite this article

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Abstract

Oral cancer (OC) is one of the most common cancers threatening human lives. However, OC pathogenesis has yet to be fully uncovered, and thus designing effective treatments remains difficult. Identifying genes related to OC is an important way for achieving this purpose. In this study, we proposed three computational models for inferring novel OC-related genes. In contrast to previously proposed computational methods, which lacked the learning procedures, each proposed model adopted a one-class learning algorithm, which can provide a deep insight into features of validated OC-related genes. A network embedding algorithm (i.e., node2vec) was applied to the protein–protein interaction network to produce the representation of genes. The features of the OC-related genes were used in the training of the one-class algorithm, and the performance of the final inferring model was improved through a feature selection procedure. Then, candidate genes were produced by applying the trained inferring model to other genes. Three tests were performed to screen out the important candidate genes. Accordingly, we obtained three inferred gene sets, any two of which were different. The inferred genes were also different from previous reported genes and some of them have been included in the public Oral Cancer Gene Database. Finally, we analyzed several inferred genes to confirm whether they are novel OC-related genes.

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Funding

This study was supported by the Shanghai Municipal Science and Technology Major Project (2017SHZDZX01), National Key R&D Program of China (2018YFC0910403), National Natural Science Foundation of China (31701151, 61672356), Natural Science Foundation of Shanghai (17ZR1412500), Shanghai Sailing Program (16YF1413800), the Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS) (2016245), the fund of the key Laboratory of Stem Cell Biology of Chinese Academy of Sciences (201703), Science and Technology Commission of Shanghai Municipality (STCSM) (18dz2271000), Scientific Research Fund of Hunan Provincial Education Department (18A394), and aid program for Science and Technology Innovative Research Team in Shaoyang University.

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  1. These authors contributed equally: Lei Chen, Yu-Hang Zhang

Authors and Affiliations

  1. School of Life Sciences, Shanghai University, 200444, Shanghai, People’s Republic of China

    Lei Chen & Yu-Dong Cai

  2. College of Information Engineering, Shanghai Maritime University, 201306, Shanghai, People’s Republic of China

    Lei Chen

  3. Shanghai Key Laboratory of PMMP, East China Normal University, 200241, Shanghai, People’s Republic of China

    Lei Chen

  4. Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 200031, Shanghai, People’s Republic of China

    Yu-Hang Zhang & Tao Huang

  5. College of Information Engineering, Shaoyang University, Shaoyang, 422000, Hunan, People’s Republic of China

    Guohua Huang

  6. Department of Medical Informatics, Erasmus MC, Rotterdam, Netherlands

    Xiaoyong Pan

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Chen, L., Zhang, YH., Huang, G. et al. Inferring novel genes related to oral cancer with a network embedding method and one-class learning algorithms. Gene Ther 26, 465–478 (2019). https://doi.org/10.1038/s41434-019-0099-y

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