Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory

BMC Bioinformatics

Proceedings

Ka-Lok Ng¹  Chien-Hung Huang²  Chia-Wei Hsu²  Peter Mu-Hsin Chang³  Chi-Ying F. Huang⁴ 

[1] Department of Bioinformatics and Medical Engineering, Asia University, 41354, Taichung, Taiwan;Department of Medical Research, China Medical University Hospital, China Medical University, 40402, Taichung, Taiwan;Department of Computer Science and Information Engineering, National Formosa University, 63205, Hu-Wei, Taiwan;Division of Hematology and Oncology, Department of Medicine, Taipei Veterans General Hospital; Faculty of Medicine, National Yang Ming University, 112, Taipei, Taiwan;Institute of Biopharmaceutical Sciences, National Yang-Ming University, 112, Taipei, Taiwan;

关键词: Non-small cell lung cancer;    Drug repositioning;    Microarray data analysis;    Machine learning algorithm;    Topological parameters;    Protein-protein interactions;    Enrichment analysis;    Connectivity Map;   

DOI  :  10.1186/s12859-015-0845-0

来源: Springer

【 摘 要 】

BackgroundNon-small cell lung cancer (NSCLC) is one of the leading causes of death globally, and research into NSCLC has been accumulating steadily over several years. Drug repositioning is the current trend in the pharmaceutical industry for identifying potential new uses for existing drugs and accelerating the development process of drugs, as well as reducing side effects.ResultsThis work integrates two approaches - machine learning algorithms and topological parameter-based classification - to develop a novel pipeline of drug repositioning to analyze four lung cancer microarray datasets, enriched biological processes, potential therapeutic drugs and targeted genes for NSCLC treatments. A total of 7 (8) and 11 (12) promising drugs (targeted genes) were discovered for treating early- and late-stage NSCLC, respectively. The effectiveness of these drugs is supported by the literature, experimentally determined in-vitro IC50 and clinical trials. This work provides better drug prediction accuracy than competitive research according to IC50 measurements.ConclusionsWith the novel pipeline of drug repositioning, the discovery of enriched pathways and potential drugs related to NSCLC can provide insight into the key regulators of tumorigenesis and the treatment of NSCLC. Based on the verified effectiveness of the targeted drugs predicted by this pipeline, we suggest that our drug-finding pipeline is effective for repositioning drugs.

【 授权许可】

Unknown   
© Huang et al. 2016. This article is published under license to BioMed Central Ltd. Open AccessThis article is 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, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.