CenhANCER: a comprehensive cancer enhancer database for primary tissues and cell lines.
Zhi-Hui LuoMeng-Wei ShiYuan ZhangDan-Yang WangYi-Bo TongXue-Ling PanShanShan ChengPublished in: Database : the journal of biological databases and curation (2023)
Enhancers, which are key tumorigenic factors with wide applications for subtyping, diagnosis and treatment of cancer, are attracting increasing attention in the cancer research. However, systematic analysis of cancer enhancers poses a challenge due to the lack of integrative data resources, especially those from tumor primary tissues. To provide a comprehensive enhancer profile across cancer types, we developed a cancer enhancer database CenhANCER by curating public resources including all the public H3K27ac ChIP-Seq data from 805 primary tissue samples and 671 cell line samples across 41 cancer types. In total, 57 029 408 typical enhancers, 978 411 super-enhancers and 226 726 enriched transcription factors were identified. We annotated the super-enhancers with chromatin accessibility regions, cancer expression quantitative trait loci (eQTLs), genotype-tissue expression eQTLs and genome-wide association study risk single nucleotide polymorphisms (SNPs) for further functional analysis. The identified enhancers were highly consistent with accessible chromatin regions in the corresponding cancer types, and all the 10 super-enhancer regions identified from one colorectal cancer study were recapitulated in our CenhANCER, both of which testified the high quality of our data. CenhANCER with high-quality cancer enhancer candidates and transcription factors that are potential therapeutic targets across multiple cancer types provides a credible resource for single cancer analysis and for comparative studies of various cancer types. Database URL http://cenhancer.chenzxlab.cn/.
Keyphrases
- papillary thyroid
- squamous cell
- transcription factor
- healthcare
- gene expression
- emergency department
- lymph node metastasis
- machine learning
- squamous cell carcinoma
- young adults
- mass spectrometry
- genome wide association study
- mental health
- oxidative stress
- single cell
- childhood cancer
- deep learning
- circulating tumor cells