circExp

circExp provides a standardized compendium of human circular RNA (circRNA) expression across multiple cancer types to support identification of differentially expressed circRNAs and their potential clinical relevance.

Key Features:

• Data curation and coverage: Integration of circRNA expression data from 11 technical platforms comprising 48 expression profile datasets across 18 human cancer types totaling 860,751 circRNA expression records.
• Differential expression analysis: Identification of 189,193 differential expression signatures distinguishing normal and cancer samples, including a subset of 52 circRNAs consistently differentially expressed in 20 or more analyses.
• Pre-calculated results: Results are organized into 132 plain text files representing pre-computed analyses and expression summaries.
• Expression visualization: Dataset-level expression heatmaps summarize circRNA expression profiles for individual datasets.
• Gene mapping and clinical association: Differentially expressed circRNAs were mapped to their parental protein-coding genes and assessed for potential impacts on survival in The Cancer Genome Atlas (TCGA) pan-cancer cohort of 10,797 patients.

Scientific Applications:

• Biomarker discovery: Facilitate identification of circRNAs with potential diagnostic or prognostic value in human cancers.
• Cancer circRNA biology: Support investigations into the roles of circRNAs in gene regulation and cancer-related molecular processes.
• Therapeutic target research: Aid discovery of circRNAs and associated parental genes as candidate therapeutic targets.
• Clinical association studies: Enable analysis of circRNA–parent gene mappings for associations with patient survival in TCGA pan-cancer data.

Methodology:

Extensive data curation from multiple technical platforms and integration of 48 expression profile datasets were performed to generate standardized circRNA expression profiles; differential expression analyses were conducted and differentially expressed circRNAs were mapped to parental protein-coding genes to assess potential clinical impacts in TCGA pan-cancer data.