CNCDatabase

CNCDatabase catalogs predicted non-coding cancer driver alterations and their associated evidence across promoters, 5' and 3' untranslated regions (UTRs), enhancers, CTCF insulators, and non-coding RNAs to support research on their roles in tumorigenesis.

Key Features:

• Scope: Data compiled from over 32 cancer types.
• Catalog contents: Documents 1,111 protein-coding genes and 90 non-coding RNAs with reported non-coding drivers.
• Annotated genomic elements: Includes predicted drivers located at gene promoters, 5' and 3' UTRs, enhancers, CTCF insulators, and non-coding RNAs.
• Computational inference: Predictions derived from analysis of positive selection in whole-genome sequences.
• Expression association analysis: Uses differential gene expression between samples with and without mutations to support candidate drivers.
• Experimental evidence: Incorporates luciferase reporter assays and genome editing validations when available.
• Scalability: Designed to be updated with data from larger whole-genome sequencing studies, CRISPR screens, and additional experimental validations.

Scientific Applications:

• Pathological role characterization: Enables studies of how non-coding alterations contribute to tumorigenesis.
• Expression association studies: Supports linking non-coding mutations to differential gene expression in cancer samples.
• Cross-cancer comparison: Facilitates comparison of non-coding driver occurrences across multiple cancer types.

Methodology:

Predictions are based on analysis of positive selection in whole-genome sequences and on differential gene expression comparisons between samples with and without mutations.