Introduction
This database provides the list of non-coding cancer drivers in gene promoters, enhancers, ncRNAs and CTCF-cohesin insulators published in over 25 studies. Most mutations from whole genome sequencing data occur in the non-coding regions with unknown impact to the tumor development. The advances in the study of regulatory regions have been uncovering a large number of putative non-coding cancer drivers. We applied text mining and manual curation to the PubMed articles and then developed CNCDatabase, Cornell Non-Coding Cancer driver Database. CNCDatabase provides a helpful resource for researchers to explore the pathological role of non-coding alterations and their associations with gene expression in human cancers.
Data Summary
Summary of non-coding drivers. (A) Number of non-coding drivers in each cancer type by computational prediction. (B) Number of non-coding drivers in each cancer type that show differential gene expression in samples with mutations vs. without using RNA-seq data. (C) Number of non-coding drivers in each cancer type with support from other functional validation, such as CRISPR/Cas9 or luciferase reporter assay.
Features
Query non-coding cancer drivers by gene name, cancer types , element types or evidence types that include computational prediction method, gene expression associations from RNA-seq and other experimental methods.
Illustrate the query results in summary plots.
Prioritize non-coding cancer driver candidates for follow-up experiments.
Users can submit curated non-coding cancer driver list to be included in the new database release.
