RBPTD

RBPTD: Database for pan-cancer RNA-binding protein dysregulation analysis

RBPTD integrates gene expression profiles, prognosis data, and DNA copy number variation (CNV) data across 28 cancer types to analyze RNA-binding protein (RBP) dysregulation and infer RBP functional associations.

Key Features:

• Multidimensional data integration: Integrates gene expression, prognosis, and DNA CNV datasets across 28 cancer types for pan-cancer RBP analysis.
• Dysregulated RBP identification: Identifies 454 significantly differentially expressed RBPs, 1970 RBPs with significant prognostic value, and 53 RBPs with dysregulation correlated with CNV abnormalities.
• Functional analysis and prediction: Explores functions of 26 cancer-related RBPs using high-throughput RNA sequencing data from crosslinking immunoprecipitation; predicts potential functions of other RBPs by calculating correlation coefficients with other genes.

Scientific Applications:

• Oncology RBP dysregulation studies: Supports analysis of RBP-associated gene expression regulation, prognosis associations, and CNV-linked dysregulation across cancer types.
• Candidate target prioritization: Enables prioritization of RBPs with differential expression, prognostic value, or CNV-associated dysregulation for downstream oncology research.

Methodology:

Systematically integrates high-throughput gene expression, prognostic, and DNA CNV data across cancers; applies bioinformatics analyses to identify dysregulated RBPs and combines empirical analysis with predictive modeling based on correlation coefficients for RBP functional inference.