Abstract
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.
Highlights
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
Huang et al BMC Bioinformatics 2016, 17(Suppl 1):2 interaction) community, we established a systematic strategy for identifying potential drugs and target genes for treating NSCLC, which can be extended in several respects that are addressed in the present study
Machine learning algorithms In the previous study [7], we developed a simple and effective machine learning method, based on domaindomain interactions (DDI), weighted domain frequency score (DFS) and cancer linker degree data (CLD) to predict cancer proteins
Summary
Microarray data analysis In this study, multiple microarray source data were used for analysis. The common drugs were submitted to DrugBank [57] and NCBI to search for their corresponding targeted genes Among these targeted genes, we kept only those which are key genes, yielding a total of 8 and 12 targeted genes for early- and late-stage NSCLC respectively, as shown, which are the potential therapeutic targets for future lung cancer clinical trials. MutationTaster [60] is another tool that uses NGS data to elucidate the effect of missense mutations on the expression and function of proteins
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