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

BMC Bioinformatics
Chien-Hung HuangKa-Lok Ng

Abstract

Non-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. This 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. With 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 ...Continue Reading

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Citations

Apr 6, 2019·The Journal of International Medical Research·Qiuli YuZhiyong Yang
Apr 6, 2018·Journal of the Royal Society, Interface·Travers ChingCasey S Greene
Nov 8, 2019·Seminars in Cancer Biology·Ramanathan KaruppasamyDaisuke Kihara
May 6, 2019·Journal of Medicinal Chemistry·Gaochan WuPeng Zhan

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Datasets Mentioned

BETA
GSE19804
GSE7670

Methods Mentioned

BETA
ELISA

Clinical Trials Mentioned

NCT01839955
NCT02437136
NCT00667082

Software Mentioned

DAVID
Weka
Bioconductor
HeatS
REACTOME
MutationTaster
DAVID REACTOME REACTOME
ConsensusPathDB
GEO
cMap

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