Important
After saving your Session ID, you can leave the page and return later. Be aware that some reports may take a long time to generate (e.g.If you select all of the cancer types and all analyses (except GSEA), the process can take approximately 90 minutes.).
If you want to perform additional analysis, always use “Restart Analysis”.
To view sample reports, visit the “Examples” tab.
Each report can be downloaded individually by clicking “Download…” or all at once in a zip file by clicking “Download All Reports.”
Except for the GSEA analysis, p-values are not corrected.
Pancancer overview of TCGA
This report provides an overview of the expression of selected gene in various cancer types, using data from the TCGA and GTEx datasets. It includes figures for TPM and log2(TPM+1).
The TCGA dataset contains three source types (though not for all cancers): Primary Tumor, Metastasis, and Tumor-adjacent Normal Tissue. The GTEx dataset represents healthy normal tissue.
Detailed results are available in the “Normal vs Tumor, Met” report.
An important note: in these plots, every sample is represented. The “Normal vs Tumor, Met” report (available on the main site) offers separate plots—one for when all samples are included and another that shows only paired samples (i.e., tumor and normal tissues from the same patients).
Normal vs Tumor, Met
This report provides of the comparisons expression of selected gene in TCGA comparing Primary Tumor, Metastasis, and Tumor-adjacent Normal Tissue.
The first column of figures contains all the samples for the cancer type, and the 2nd column contains only those samples where Tumor-adjacent Normal Tissue and Primary Tumor RNASeq data is available from the same patients.
Stage
In this report, the expression of selected gene is compared to the cancer stage. Each row of figures represents a different cancer type. The first column of figures uses the AJCC Pathological Stage, the second column uses the Clinical Stage, and the third column represents the “Consensus Stage,” which combines the AJCC stage and, where unavailable, defaults to the Clinical Stage.
Expression vs Proliferartion Index
The expression of many, maybe most of the “cancer genes” is associated with proliferation. There are not many genes that are upregulated in cancer tissue compared to normal, but is not associated with proliferation. This is a nice paper on the topic: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5748209/
The “general level of proliferation” in a sample can be estimated using RNASeq, these estimations usually referred as proliferation indexes.
The proliferation index was determined using two methods:
- Venet D, Dumont JE, Detours V. Most random gene expression signatures are significantly associated with breast cancer outcome. PLoS Comput Biol. 2011; 7. doi: 10.1371/journal.pcbi.1002240.
- Marie Locard-Paulet,Oana Palasca,Lars Juhl Jensen, Identifying the genes impacted by cell proliferation in proteomics and transcriptomics studies
Furthermore, the correlation with cell cycle-related genes is also represented in this report.
Survival, all patients
Survival analysis for patients with the selected cancer type. Methods used: Kaplan-Meier and Cox proportional hazards regression. Patients are divided into two groups based on either median expression or optimal cutoff. Survival times analyzed: Overall Survival (OS), Progression-Free Survival (PFS), Disease-Specific Survival (DSS), Disease-Free Interval (DFI), and Progression-Free Interval (PFI). Refer to the Abbreviations section for detailed definitions.
To generate this analysis for all cancer types, use the “Select All” option.
Survival, treatment included platina
Survival analysis for patients who received documented treatment including platinum-based drugs (e.g., cisplatin, carboplatin). It’s likely that more patients had this treatment than is documented. However, we can be certain that these patients received it.
Survival, treatment included paclitaxel
Survival analysis for patients who received documented treatment including paclitaxel or other taxols. It’s likely that more patients had this treatment than is documented. However, we can be certain that these patients received it.
Expression vs Immune infiltration, all patients
The gene expression is compared to the sample purity and immune composition, which are deconvoluted using bulk RNA-Seq data. The methods used include CIBERSORT, TIMER, the approach presented by Danaher et al., MCP-counter, and xCell.
Expression vs Mutational Signatures (COSMICv2)
The gene expression is compared to the contribution (weight) and number of mutations corresponding to COSMICv2 single nucleotide mutational signatures (https://cancer.sanger.ac.uk/signatures/signatures_v2/), using whole-exome sequencing data.
Expression vs Mutational Signatures (COSMICv3)
The gene expression is compared to the contribution (weight) and number of mutations corresponding to COSMICv3 single nucleotide mutational signatures (https://cancer.sanger.ac.uk/signatures/sbs/), using whole-exome sequencing data.
Expression vs Indel Mutational Signatures
The gene expression is compared to the contribution (weight) and number of mutations corresponding to COSMICv3 indel mutational signatures (https://cancer.sanger.ac.uk/signatures/id/), using whole-exome sequencing (WES) data. These signatures are less reliable using WES data (compared to WGS).
Expression vs Immune infiltration, in breast cancer subtypes
As in the previous analysis, the difference is that the breast cancer cases are classified into PAM50 subtypes using RNA-Seq data, and the analysis is carried out for each of the subtypes.
Survival, breast cc. patients per subtype
As in the previous analysis, the difference is that the breast cancer cases are classified into PAM50 subtypes using RNA-Seq data, and the analysis is carried out for each of the subtypes.
Survival, breast cc. patients per subtype, treatment included paclitaxel
As in the previous analysis, the difference is that the breast cancer cases are classified into PAM50 subtypes using RNA-Seq data, and the analysis is carried out for each of the subtypes.
TCGA, Differential gene expression analysis of high vs low expressing group and GSEA
To view the tables and some figures, please download this report. and open it on your PC.
In the first step, patients are divided into two groups based on the limit set on the scale. These groups are then compared to identify differentially expressed genes. Subsequently, Gene Set Enrichment Analysis (GSEA) is performed. For example, if you choose the gene BRCA1, ovarian cancer (OV), and a limit of 25%, the analysis compares the highest BRCA1-expressing patients (top 25%) to the lowest 25%. The differentially expressed genes between these groups are then analyzed further.