TCGA, or The Cancer Genome Atlas, is a public multi-omics cancer resource and cohort collection used as a large-scale data source for pan-cancer and disease-specific analyses. It supports integrated studies of gene expression, promoter methylation, survival, immune infiltration, and other molecular features across cancer types, making it useful for biomarker discovery and prognostic modeling. In the literature, TCGA has been used to identify jmjd6 as an oncogene and prognostic biomarker in pan-cancer analysis, and to evaluate sting promoter methylation and expression in renal cell carcinoma. It has also served as an evaluation dataset for multi-task adaptive deep sparse canonical correlation analysis for multi-omics survival prediction, highlighting its role in method development and validation. In colorectal cancer, TCGA multi-omics cohorts enabled immune classification aimed at predicting immunotherapy efficacy and assessing enhancement strategies involving wnt signaling inhibition. Overall, TCGA functions as a foundational multi-omics data source for cancer genomics, with applications spanning prognosis, immune landscape characterization, and translational biomarker discovery.
Cancer genomics and prognosis
- Public multi-omics cancer database used in a pan-cancer analysis that identified jmjd6 as an oncogene and prognostic biomarker. (PMID:41849021)
- Cancer genomics cohort used to analyze sting promoter methylation, expression, survival, and immune infiltration in renal cell carcinoma. (PMID:41942521)
- Cancer genomics resource used for integrated analyses in a study of banf1 as a potential prognostic biomarker in lung adenocarcinoma. (PMID:42000935)
- The Cancer Genome Atlas cohorts were used as evaluation datasets for multi-omics cancer survival prediction. (PMID:41973703)
Immunology and therapy prediction
- Large-scale colorectal cancer multi-omics cohort used to build an immune classification relevant to immunotherapy efficacy prediction. (PMID:41819525)
- The colorectal cancer study also examined enhancement strategies involving wnt signaling inhibition alongside immune classification. (PMID:41819525)
- TCGA-based analyses linked sting expression and promoter methylation with immune correlation in renal cell carcinoma. (PMID:41942521)
- Pan-cancer TCGA analysis supported characterization of tumor-intrinsic programs and a complex immune landscape in lung adenocarcinoma. (PMID:42000935)
Methods and multi-omics modeling
- TCGA cohorts served as benchmark datasets for multi-task adaptive deep sparse canonical correlation analysis in multi-omics survival prediction. (PMID:41973703)
- The resource’s multi-omics structure enabled integrated analyses across genomic, transcriptomic, and epigenetic layers. (PMID:42000935)
- Its scale and breadth make it suitable for evaluating prognostic models across multiple cancer types. (PMID:41849021)
- TCGA was used as a foundational dataset for building classification frameworks from multi-omics colorectal cancer data. (PMID:41819525)