CancerCoCoPUTs

CancerCoCoPUTs analyzes codon and codon pair usage across 32 primary tumor types using The Cancer Genome Atlas (TCGA) RNA-seq data from 6,427 solid tumor and 632 normal samples across 11 tissues to characterize cancer-specific synonymous codon usage changes and their associations with clinical outcomes.

Key Features:

• Dataset composition: Uses TCGA RNA-seq data comprising 6,427 solid tumor samples and 632 normal tissue samples spanning 32 primary tumor types and 11 distinct tissues.
• Codon and codon pair analysis: Quantifies both individual codon usage and codon pair usage across tumor types.
• Cancer- and tissue-specific signatures: Identifies unique patterns of synonymous codon usage changes specific to different cancer types within the same tissue.
• Paired healthy–tumor comparisons: Facilitates comparative analysis between paired healthy and tumor tissues from individual patients.
• Clinical integration for survival analysis: Integrates TCGA clinical data to perform survival analyses based on the magnitude of codon usage change between healthy and tumor samples.
• Translation supply–demand contextualization: Assesses relationships between tRNA supply and codon demand in cancer states.
• Reported synonymous codon signature example: Documents observed increases in GGT (glycine) usage relative to GGC across invasive ductal carcinoma (IDC), invasive lobular carcinoma (ILC), and mixed invasive ductal and lobular carcinoma (IDLC) of the breast.

Scientific Applications:

• Prognostic biomarker discovery: Enables investigation of codon usage change magnitude as a predictor correlated with mortality in cancer patients.
• Translation biology in cancer: Supports studies of tRNA availability and codon demand dynamics in tumor versus normal states.
• Cancer-type molecular signature identification: Facilitates identification of cancer- and tissue-specific synonymous codon and codon pair signatures.
• Therapeutic and personalized medicine research: Informs research aimed at designing therapeutics and personalized approaches that consider tumor-specific codon usage patterns.

Methodology:

Computational analysis of codon and codon pair usage was performed on TCGA RNA-seq datasets (6,427 tumors, 632 normals) across 32 tumor types and 11 tissues, including paired healthy–tumor comparisons and integration of TCGA clinical data for survival analyses.