title: Systematically characterizing and prioritizing chemosensitivity related gene based on Gene Ontology and protein interaction network.
abstract: BACKGROUND
  The identification of genes that predict in vitro cellular chemosensitivity of cancer cells is of great importance. Chemosensitivity related genes (CRGs) have been widely utilized to guide clinical and cancer chemotherapy decisions. In addition, CRGs potentially share functional characteristics and network features in protein interaction networks (PPIN).
  
  
  METHODS
  In this study, we proposed a method to identify CRGs based on Gene Ontology (GO) and PPIN. Firstly, we documented 150 pairs of drug-CCRG (curated chemosensitivity related gene) from 492 published papers. Secondly, we characterized CCRGs from the perspective of GO and PPIN. Thirdly, we prioritized CRGs based on CCRGs' GO and network characteristics. Lastly, we evaluated the performance of the proposed method.
  
  
  RESULTS
  We found that CCRG enriched GO terms were most often related to chemosensitivity and exhibited higher similarity scores compared to randomly selected genes. Moreover, CCRGs played key roles in maintaining the connectivity and controlling the information flow of PPINs. We then prioritized CRGs using CCRG enriched GO terms and CCRG network characteristics in order to obtain a database of predicted drug-CRGs that included 53 CRGs, 32 of which have been reported to affect susceptibility to drugs. Our proposed method identifies a greater number of drug-CCRGs, and drug-CCRGs are much more significantly enriched in predicted drug-CRGs, compared to a method based on the correlation of gene expression and drug activity. The mean area under ROC curve (AUC) for our method is 65.2%, whereas that for the traditional method is 55.2%.
  
  
  CONCLUSIONS
  Our method not only identifies CRGs with expression patterns strongly correlated with drug activity, but also identifies CRGs in which expression is weakly correlated with drug activity. This study provides the framework for the identification of signatures that predict in vitro cellular chemosensitivity and offers a valuable database for pharmacogenomics research.
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Chen
authors.forename: Wei
authors.initials: W
authors.lastname: Jiang
authors.forename: Qianghu
authors.initials: Q
authors.lastname: Wang
authors.forename: Teng
authors.initials: T
authors.lastname: Huang
authors.forename: Peng
authors.initials: P
authors.lastname: Wang
authors.forename: Yan
authors.initials: Y
authors.lastname: Li
authors.forename: Xiaowen
authors.initials: X
authors.lastname: Chen
authors.forename: Yingli
authors.initials: Y
authors.lastname: Lv
authors.forename: Xia
authors.initials: X
authors.lastname: Li
mesh_terms: D000970:Antineoplastic Agents / Q000494:pharmacology / Q000627:therapeutic use*; D045744:Cell Line, Tumor; D004354:Drug Screening Assays, Antitumor; D015972:Gene Expression Regulation, Neoplastic / Q000187:drug effects; D052138:Genes, Neoplasm / Q000235:genetics*; D006801:Humans; D058977:Molecular Sequence Annotation; D009369:Neoplasms / Q000188:drug therapy* / Q000235:genetics*; D060066:Protein Interaction Maps / Q000235:genetics*; D012372:ROC Curve
chemical_list: D000970:Antineoplastic Agents