DORGE: Discovery of Oncogenes and tumoR suppressor genes using Genetic and Epigenetic features.
Jie LyuJingyi Jessica LiJianzhong SuFanglue PengYiling Elaine ChenXinzhou GeWeibo XiePublished in: Science advances (2020)
Data-driven discovery of cancer driver genes, including tumor suppressor genes (TSGs) and oncogenes (OGs), is imperative for cancer prevention, diagnosis, and treatment. Although epigenetic alterations are important for tumor initiation and progression, most known driver genes were identified based on genetic alterations alone. Here, we developed an algorithm, DORGE (Discovery of Oncogenes and tumor suppressoR genes using Genetic and Epigenetic features), to identify TSGs and OGs by integrating comprehensive genetic and epigenetic data. DORGE identified histone modifications as strong predictors for TSGs, and it found missense mutations, super enhancers, and methylation differences as strong predictors for OGs. We extensively validated DORGE-predicted cancer driver genes using independent functional genomics data. We also found that DORGE-predicted dual-functional genes (both TSGs and OGs) are enriched at hubs in protein-protein interaction and drug-gene networks. Overall, our study has deepened the understanding of epigenetic mechanisms in tumorigenesis and revealed previously undetected cancer driver genes.
Keyphrases
- genome wide
- dna methylation
- genome wide identification
- papillary thyroid
- gene expression
- bioinformatics analysis
- small molecule
- copy number
- protein protein
- squamous cell
- genome wide analysis
- machine learning
- squamous cell carcinoma
- high throughput
- electronic health record
- emergency department
- big data
- lymph node metastasis
- multidrug resistant
- childhood cancer
- young adults
- data analysis
- drug induced
- neural network