The selection of more datasets would result in fewer circRNAs available for analysis. Loading, integrating, and analyzing multiple datasets can be time-consuming, especially during high traffic periods. We appreciate your patience while using the website.

• Overview
• 1. Home Page
• 2. Repository Page
• 3. Cohort-centred Page
  • 3.1 Overview
  • 3.2 Scatter-Correlation
  • 3.3 Heatmap-Correlation
  • 3.4 Group-Comparison (Simplified)
  • 3.5 Group-Comparison
  • 3.6 KM-Analysis
  • 3.7 Cox-Analysis
  • 3.8 Filter the cohort
• 4. Molecular-centred Page
  • 4.1 Overview
  • 4.2 Distribution of single circRNA
  • 4.3 Distribution of multi-circRNAs
  • 4.4 Cox analysis for single circRNA
  • 4.5 Cox analysis for multiple circRNAs
• 5. Signature Page
• 6. DE circRNAs Page
• 7. Global Setting Page

Overview

TCCIA (The Cancer CircRNA Immunome Atlas) is the first database that combines circRNA profiles and immunotherapy response data for five cancer types, including over 4,000 clinical samples from 25 cohorts treated with immune-checkpoint blockades (ICBs) including PD-1/PD-L1 and CTLA-4 inhibitors, and other treatments.

A video showing how to operate the TCCIA has been given at the YouTube and the bilibili.

1. Home Page

This page gives you a quick overview of this tool, which includes:

• basic information and statistics.
• feature overview.
• gallery of supported modules.

2. Repository Page

The repository page allows users to select cohorts for further analysis.

Repository page shows multiple filters on the left panels and a cohort table (with collapsing dataset details) on the main panel.

1. Cohort filter (optional)

You can quickly filter the datasets by filters on the left panels. The filters include: Cancer Type, Treatment Type, Drug, Treatment information (Pre/on treatment), Cohort Size, Publish year.

2. Cohort selection

The filtered cohort table are not selected, you have to use your mouse to click one or more data rows to select corresponding datasets.

3. Add into Cart (optional)

You can click the ‘Cart’ button to confirm selected cohorts and go to the analysis modules on the other pages.

3. Cohort-centred Page

3.1 Overview

The functional modules presented on this page are designed to help users explore CircRNAs in a single immunotherapy cohort. All selectable datasets are obtained from cohorts that you choose on the ‘Repository’ page. When selecting different datasets in some of the modules on this page, make sure that they are from the same cohort; otherwise, a box will typically pop up reminding you about this.

Note: Reading the papers the cohorts derived from is highly recommended. Some cohorts may have complex designs, which means that the phenotype data could include samples taken both before and after therapy (stored in the ‘Response2’ column) or samples with different treatments. By default, only immunotherapy-related samples are included. However, you can check the ‘Setting’ page for control. When using the data, please ensure that it meets your requirements and filter the data if necessary using the options available on the right panel.

Five analysis modules are provided for solving common analysis tasks:

3.2 Scatter-Correlation

To analyze and visualize correlation for two variables. How to filter cohort please see the filter tutorial.

1. Select dataset for axis X

2. Select circRNA-ID or Host Gene of the circRNA-ID

3. Select dataset for axis Y (Make sure select the same dataset in step 1)

4. Select circRNA-ID or Host Gene of the circRNA-ID

5. Adjust parameters for analysis

6. Submit the analysis

3.3 Heatmap-Correlation

To analyze and visualize correlation for two or more variables. How to filter cohort please see the filter tutorial.

1. Select dataset

2. Select circRNA-ID(s) or Host Gene(s) of the circRNA-ID(s)

3. Adjust parameters for analysis

4. Submit the analysis

3.4 Group-Comparison (Simplified)

To analyze and visualize numeric difference of a continuous variable between multiple groups of a variable. How to filter cohort please see the filter tutorial.

1. Select dataset

2. Select circRNA-ID or Host Gene of the circRNA-ID

3. Adjust parameters for analysis

4. Submit the analysis

3.5 Group-Comparison

To analyze and visualize numeric difference of a continuous variable between multiple groups of a variable in a more comprehensive manner. How to filter cohort please see the filter tutorial.

1. Select grouping dataset

2. Select dataset for axis Y

3. Select circRNA-ID or Host Gene of the circRNA-ID

4. Preprocess the grouping dataset

5. Select grouping column

6. Choose the categories (for categorical variables) or the threshold (for numeric variables, e.g., TMB, expression of a CircRNA) for grouping

7. Select the values to show in axis Y

8. Click to join the data

9. Confirm the process and exit

10. Adjust parameters for analysis

11. Submit the analysis

3.6 KM-Analysis

To analyze and visualize survival curves between multiple groups of a variable. How to filter cohort please see the filter tutorial.

1. Select dataset

2. Select circRNA-ID or Host Gene of the circRNA-ID

3. Adjust parameters for analysis

4. Submit the analysis

3.7 Cox-Analysis

To do Cox-analysis between multiple groups of a variable. How to filter cohort please see the filter tutorial.

1. Select dataset

2. Select circRNA-ID or Host Gene of the circRNA-ID

3. Adjust parameters for analysis

4. Submit the analysis

3.8 Filter the cohort

1. Select the variable in metadata

2. Add variable to the metadata for further filtering

3. Select the value to activate the filter in different variables

4. Submit to filter

5. Confirm filtering and go to the next analysis

4. Molecular-centred Page

4.1 Overview

The functional modules presented on this page are designed to help users explore CircRNAs across multiple immunotherapy cohorts. At default, only one cohort is selected. You may want to choose more cohorts on the ‘Repository’ page before exploring modules of this page. If you want to explore and analyze a specific cohort, please go to the ‘Cohort-centred Analysis’ page (although it may also work here). All selectable datasets are obtained from cohorts that you choose on the ‘Repository’ page.

4.2 Distribution of single circRNA

1. Select datasets

2. Select circRNA-ID or Host Gene of the circRNA-ID

3. Adjust parameters for analysis

4. Submit the analysis

4.3 Distribution of multi-circRNAs

1. Select datasets

2. Select circRNA-ID(s) or Host Gene(s) of the circRNA-ID(s)

3. Adjust parameters for analysis

4. Submit the analysis

4.4 Cox analysis for single circRNA

1. Select datasets

2. Select circRNA-ID or Host Gene of the circRNA-ID

3. Adjust parameters for analysis

4. Submit the analysis

4.5 Cox analysis for multiple circRNAs

1. Select datasets

2. Select circRNA-ID(s) or Host Gene(s) of the circRNA-ID(s)

3. Adjust parameters for analysis

4. Submit the analysis

5. Signature Page

5.1 Overview

This module provides functionality for visualizing the correlation between one or more CircRNAs and tumor microenvironment measures or immune signatures in one or more cohorts. All selectable datasets are obtained from cohorts that you choose on the ‘Repository’ page.

1. Select datasets

2. Select circRNA-ID or Host Gene of the circRNA-ID

3. Adjust parameters for analysis

4. Submit the analysis

6. DE circRNAs Page

6.1 Overview

This module provides functionality for viewing the differential expression CircRNA list for immunotherapy responsive and non-responsive patients, and displaying the data through a volcano plot. All selectable cohorts are obtained from the ‘Repository’ page.

1. Select datasets

2. Adjust parameters for analysis

3. Submit the analysis

7. Global Setting Page

This page provides global options for controlling access to underlying data. By setting default options, we try to minimize the user’s initial learning cost and ensure that the results displayed on the website are biologically meaningful. For example, we only analyze sample data related to immunotherapy by default, in order to match the core goals of our website, and to remove data bias (confusion caused by different treatments in the data) that first-time users may not see. In addition, we have set up default configuration for CircRNA expression using appropriate CPM quantification. Users can adjust these global options according to their own needs after becoming familiar with the website, in order to facilitate exploring more possibilities of this website.

All the processed data can be accessed on the TCCIA website. We collected ICB-related cancer data sets with tumor gene expression profiles from the dbGaP, EGA, EMBL-EBI and GSA databases. Following the accession instruction described in published ICB studies (table on the “Repository” page), we downloaded ICB patients’ RNA-Seq raw sequencing data, clinical information, and response outcome information from ICB studies (if available). Curated circRNA identification results are available at Zenodo (https://zenodo.org/doi/10.5281/zenodo.7969298). For requesting circRNA expression matrix or other types of data, please contact the leader, Jian-Guo Zhou.

A number of host genes of circRNAs play oncogenic or suppressive role in tumorigenesis. circRNAs are derived from these genes and regulate the transcription, translation, parental protein activity and degradation of their host genes.

• Ref

We employed multiple circRNA detection tools, including CIRCexplorer2, CIRIquant, find_circ, and circRNA_finder, to identify, parse, and annotate circRNA junctions within each sample, using the human genome version hg38 as the reference. In our pursuit of biologically robust circRNA identification within each cohort, we undertook an approach that amalgamated results from various circRNA detection tools and implemented a rigorous ensemble strategy (label as ‘Ensemble’ in the selection) characterized by the following stringent criteria. Firstly, we exclusively retained circRNAs located on chromosomes 1 to 22, as well as the X and Y chromosomes. Secondly, circRNAs were mandated to exhibit genomic overlap with gene regions as defined in the reference genome annotation file, specifically “gencode.v34.annotation.gtf.” Lastly, to ensure heightened robustness, circRNAs had to be concurrently identified by a minimum of two out of the four employed detection tools, with at least one of these tools detecting no fewer than two back-splicing junction counts. This meticulous methodological approach was instrumental in ensuring the acquisition of biologically sound and reliable circRNA datasets, thereby fortifying the integrity of our analysis within each cohort. Subsequently, the back-splicing junction counts underwent differential expression analysis using established approaches, namely Limma Voom, edgeR, and DESeq2. This analysis facilitated comparisons between patient groups, such as those who responded to checkpoint immunotherapy versus non-responders, as well as between samples collected before and during ICB treatment.

We gathered and processed patient ‘Response’ data from various cohorts. However, due to inconsistencies among these cohorts, such as the usage of different terms like CR, PR, SD, PD, R, NR, DCB, and NDB, we harmonized the response variable based on the timing of treatment and treatment response. As a result, we introduced a new variable called ‘Response2’ with four distinct categories: Pre-R, Pre-NR, On-R, and On-NR. In cases where response data was not available, we assigned the category NE.

Please cite the following article when you used TCCIA in your study:

Shixiang Wang^#, Yi Xiong^#, Yihao Zhang^#, Haitao Wang, Jianfeng Li, Minjun Chen, Peng Luo, Yung-Hung Luo, Markus Hecht, Benjamin Frey, Udo S Gaipl, Xuejun Li^*, Qi Zhao^*, Hu Ma^*, Jian-Guo Zhou^*. TCCIA: A Comprehensive Resource for Exploring CircRNA in Cancer Immunotherapy. Journal for ImmunoTherapy of Cancer (2024);12:e008040. doi: 10.1136/jitc-2023-008040.

If you find any bug or have any feature to request, please report it by email (Shixiang Wang mailto:shixiang1994wang@gmail.com, Jianguo Zhou mailto:jianguo.zhou@zmu.edu.cn).

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