ArticleBMC medical genomics2012
Systematically characterizing and prioritizing chemosensitivity related gene based on Gene Ontology and protein interaction network.
Article in BMC medical genomics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Article
- Identification of a prognostic chemoresistance-related gene signature associated with immune microenvironment in breast cancer.Bioengineered · 2021Article
- Principled multi-omic analysis reveals gene regulatory mechanisms of phenotype variation.Genome research · 2018Article
- Knowledge-guided gene prioritization reveals new insights into the mechanisms of chemoresistance.Genome biology · 2017Article
- OvMark: a user-friendly system for the identification of prognostic biomarkers in publically available ovarian cancer gene expression datasets.Molecular cancer · 2014Article
- Surveillance for the prevention of chronic diseases through information association.BMC medical genomics · 2014Article
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Authors and funding
9 authors.
Funding
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Abstract
backgroundThe 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).
methodsIn 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.
resultsWe 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%.
conclusionsOur 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.
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