The Gene Overview provides an overview of multi-omics evidence for the selected gene across projects, cohorts, and tissues.
Bar plots and boxplots summarize global cross-cohort results, including RNA, CNV, Methylation, Mutation, and miRNA findings. The heatmap shows tissue-specific project-level results based on the tissue or organ selected from the body diagram.
Color definitions, statistical cutoffs, and asterisk criteria are available in the legend by clicking the ? icon next to the section title, with full details provided in
Help.
Select or click an organ to explore
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The RNA interface visualizes expression patterns and survival associations for the selected gene across cancer datasets. Users can explore RNA expression by sample type or tumor stage. Tissue or organ selection from the body diagram applies only to the sample type view. Survival analyses summarize whether gene expression is associated with patient outcomes across cohorts and analysis methods. Survival results include a cohort-level summary map and detailed survival analyses based on multiple analysis methods.
Select or click a tissue or organ to view relevant cancer projects for the selected gene. Click on the legend entries to toggle sample types on or off. Hover over individual dots to view sample-level details. Hover near box areas to view summary statistics.
Select or click a tissue or organ to view relevant cancer projects for the selected gene. Click on the legend entries to toggle tumor stages on or off.
Hover over individual dots to view sample-level details. Hover near box areas to view summary statistics.
The Survival Map summarizes the survival impact of the selected gene across cancer types, survival endpoints, omics features, and analysis methods. Colored cells indicate significant associations between the selected molecular feature and survival outcome, while uncolored cells indicate non-significant or unavailable results. Red represents higher hazard and blue represents lower hazard relative to the reference group.
Click a colored cell to view the corresponding Kaplan–Meier plot. For machine learning results, the detailed output opens in a new tab.
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Survival Analysis
The Survival Analysis panel evaluates whether the selected gene's molecular features are associated with patient prognosis. Users first select a survival analysis type: Cox Univariate, Cox Multivariate (clinical), Cure Model, Machine Learning, or Synergistic Survival Analysis. After an analysis type is selected, the available filters and result views update accordingly. Users can click the question mark icon to view descriptions for each analysis type. Algorithm descriptions and references are available in FAQ4.
Depending on the selected analysis framework and omics type, users can choose relevant options such as cancer type, survival endpoint, survival time, grouping or stratification method, CNV calling method, methylation grouping method, or machine learning algorithm. The resulting plots and tables help compare survival patterns, estimate risk differences, and identify molecular features or cross-omics interactions associated with patient outcomes.
Univariate Cox regression analysis
Cox Univariate analysis evaluates whether the selected molecular feature is associated with patient survival using univariate Cox proportional hazards regression, without adjusting for any clinical covariates. Results are presented as Kaplan–Meier survival curves and hazard ratios across cancer types and survival endpoints. An additional covariate-adjusted survival curve is displayed alongside the unadjusted result when sufficient clinical data are available.
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Cox Multi (clinical)
Cox Multivariate analysis evaluates whether the selected molecular feature is independently associated with patient survival after adjusting for available clinical covariates such as age, gender, and stage. Results are presented as covariate-adjusted survival curves and forest plots summarizing the hazard ratios and confidence intervals of the molecular feature and each clinical covariate included in the model.
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Cure Model
The Cure Model evaluates the association between the selected molecular feature and overall survival by estimating two distinct effects: a short-term effect reflecting the association with survival time among patients who remain at risk, and a long-term effect reflecting the association with the probability of long-term survival or cure. This approach is particularly informative for cancer types where a subset of patients may be considered functionally cured after treatment.
Median All-time
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Machine Learning
Machine Learning–based survival analysis identifies molecular features associated with patient survival using three algorithms: LASSO, Random Forest, and I-Boost. Each method builds a multi-feature prognostic signature, and patients are stratified into high- and low-risk groups based on their composite signature score. Results are presented as Kaplan–Meier survival curves and ROC curves evaluating the predictive performance of each signature.
The Synergistic Survival Analysis evaluates whether the selected gene shows combined survival effects with molecular features from other omics layers, including RNA expression, mutation, CNV, and methylation. The table summarizes significant cross-omics interactions and their associated survival statistics. Selecting a row displays the corresponding Kaplan–Meier survival plots below.
The Mutation interface visualizes mutation patterns and statistics of the selected gene across multiple cancer types, in correspondence with its protein regions.
Users can explore three mutation-level summaries: Mutation Rate, Mutation Percent, and Exon Distribution. Each summary includes both a Pan-Cancer View and a Cancer-Specific View, allowing users to compare mutation patterns across cancer types or examine mutation details within a selected cancer project.
Below the mutation panels, the Survival section evaluates whether mutation status of the selected gene is associated with patient survival. Survival results include a cohort-level summary map and detailed survival analyses based on multiple analysis methods.
The Survival Map summarizes the survival impact of the selected gene across cancer types, survival endpoints, omics features, and analysis methods. Colored cells indicate significant associations between the selected molecular feature and survival outcome, while uncolored cells indicate non-significant or unavailable results. Red represents higher hazard and blue represents lower hazard relative to the reference group.
Click a colored cell to view the corresponding Kaplan–Meier plot. For machine learning results, the detailed output opens in a new tab.
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Survival Analysis
The Survival Analysis panel evaluates whether the selected gene's molecular features are associated with patient prognosis. Users first select a survival analysis type: Cox Univariate, Cox Multivariate (clinical), Cure Model, Machine Learning, or Synergistic Survival Analysis. After an analysis type is selected, the available filters and result views update accordingly. Users can click the question mark icon to view descriptions for each analysis type. Algorithm descriptions and references are available in FAQ4.
Depending on the selected analysis framework and omics type, users can choose relevant options such as cancer type, survival endpoint, survival time, grouping or stratification method, CNV calling method, methylation grouping method, or machine learning algorithm. The resulting plots and tables help compare survival patterns, estimate risk differences, and identify molecular features or cross-omics interactions associated with patient outcomes.
Univariate Cox regression analysis
Cox Univariate analysis evaluates whether the selected molecular feature is associated with patient survival using univariate Cox proportional hazards regression, without adjusting for any clinical covariates. Results are presented as Kaplan–Meier survival curves and hazard ratios across cancer types and survival endpoints. An additional covariate-adjusted survival curve is displayed alongside the unadjusted result when sufficient clinical data are available.
Cox Multi (clinical)
Cox Multivariate analysis evaluates whether the selected molecular feature is independently associated with patient survival after adjusting for available clinical covariates such as age, gender, and stage. Results are presented as covariate-adjusted survival curves and forest plots summarizing the hazard ratios and confidence intervals of the molecular feature and each clinical covariate included in the model.
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Cure Model
The Cure Model evaluates the association between the selected molecular feature and overall survival by estimating two distinct effects: a short-term effect reflecting the association with survival time among patients who remain at risk, and a long-term effect reflecting the association with the probability of long-term survival or cure. This approach is particularly informative for cancer types where a subset of patients may be considered functionally cured after treatment.
All-time
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Machine Learning
Machine Learning–based survival analysis identifies molecular features associated with patient survival using three algorithms: , Random Forest, and I-Boost. Each method builds a multi-feature prognostic signature, and patients are stratified into high- and low-risk groups based on their composite signature score. Results are presented as Kaplan–Meier survival curves and ROC curves evaluating the predictive performance of each signature.
The Synergistic Survival Analysis evaluates whether the selected gene shows combined survival effects with molecular features from other omics layers, including RNA expression, mutation, CNV, and methylation. The table summarizes significant cross-omics interactions and their associated survival statistics. Selecting a row displays the corresponding Kaplan–Meier survival plots below.
The Copy Number Variation interface visualizes CNV patterns of the selected gene across multiple cancer types.
CNV driver evidence is identified primarily using iGC, with additional support from DIGGIT. Results include both pan-cancer and cancer-specific visualizations, together with a summary table of CNV-related statistics.
Below the CNV panels, the Survival section evaluates whether copy number variation of the selected gene is associated with patient survival. Survival results include a cohort-level summary map and detailed survival analyses based on multiple analysis methods.
Pan-Cancer View: Copy Number Variation Overview
This visualization summarizes copy number variation (CNV) results for the selected gene across cancer projects.
The CNV driver panel indicates the level of CNV driver support for the selected gene. Light grey indicates support by iGC only, whereas dark grey indicates support by both iGC and DIGGIT-related evidence.
Below the driver panel, CNV results are summarized using iGC and DIGGIT. When more than five projects are available, results are displayed as a bar chart. When five or fewer projects are available, results are displayed as pie charts. Colors indicate copy-number gain, loss, or no change. Hover over bars or pie segments to view additional details, including cancer type, CNV status, and sample proportion.
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Cancer-Specific View: CNV Distribution and Correlation
Organ
Cancer Project
Visualization
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CNV Summary Table
Survival map
The Survival Map summarizes the survival impact of the selected gene across cancer types, survival endpoints, omics features, and analysis methods. Colored cells indicate significant associations between the selected molecular feature and survival outcome, while uncolored cells indicate non-significant or unavailable results. Red represents higher hazard and blue represents lower hazard relative to the reference group.
Click a colored cell to view the corresponding Kaplan–Meier plot. For machine learning results, the detailed output opens in a new tab.
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Survival Analysis
The Survival Analysis panel evaluates whether the selected gene's molecular features are associated with patient prognosis. Users first select a survival analysis type: Cox Univariate, Cox Multivariate (clinical), Cure Model, Machine Learning, or Synergistic Survival Analysis. After an analysis type is selected, the available filters and result views update accordingly. Users can click the question mark icon to view descriptions for each analysis type. Algorithm descriptions and references are available in FAQ4.
Depending on the selected analysis framework and omics type, users can choose relevant options such as cancer type, survival endpoint, survival time, grouping or stratification method, CNV calling method, methylation grouping method, or machine learning algorithm. The resulting plots and tables help compare survival patterns, estimate risk differences, and identify molecular features or cross-omics interactions associated with patient outcomes.
Univariate Cox regression analysis
Cox Univariate analysis evaluates whether the selected molecular feature is associated with patient survival using univariate Cox proportional hazards regression, without adjusting for any clinical covariates. Results are presented as Kaplan–Meier survival curves and hazard ratios across cancer types and survival endpoints. An additional covariate-adjusted survival curve is displayed alongside the unadjusted result when sufficient clinical data are available.
Cox Multi (clinical)
Cox Multivariate analysis evaluates whether the selected molecular feature is independently associated with patient survival after adjusting for available clinical covariates such as age, gender, and stage. Results are presented as covariate-adjusted survival curves and forest plots summarizing the hazard ratios and confidence intervals of the molecular feature and each clinical covariate included in the model.
Cure Model
The Cure Model evaluates the association between the selected molecular feature and overall survival by estimating two distinct effects: a short-term effect reflecting the association with survival time among patients who remain at risk, and a long-term effect reflecting the association with the probability of long-term survival or cure. This approach is particularly informative for cancer types where a subset of patients may be considered functionally cured after treatment.
iGC All-time
Gistic All-time
Survival analysis
Machine Learning–based survival analysis identifies molecular features associated with patient survival using three algorithms: , Random Forest, and I-Boost. Each method builds a multi-feature prognostic signature, and patients are stratified into high- and low-risk groups based on their composite signature score. Results are presented as Kaplan–Meier survival curves and ROC curves evaluating the predictive performance of each signature.
The Methylation interface visualizes the DNA methylation status of the selected gene across multiple cancer types and explores how methylation relates to gene expression.
Methylation driver evidence is identified primarily using MethylMix, with additional support from ELMER. This interface provides both pan-cancer and cancer-specific visualizations, along with a summary table of methylation details.
Below the methylation panels, the Survival section evaluates whether methylation status of the selected gene is associated with patient survival. Survival results include a cohort-level summary map and detailed survival analyses based on multiple analysis methods.
Pan-Cancer View: Methylation Status Overview
This visualization summarizes DNA methylation results for the selected gene across available cancer projects. The methylation driver panel indicates the level of methylation driver support for the selected gene. Light grey indicates that the gene is identified as a methylation driver by MethylMix, while dark grey indicates additional support from ELMER. Below the driver panel, sample proportions classified by MethylMix as hypermethylated, hypomethylated, or showing no methylation change are displayed. When more than five projects are available, results are shown as a bar chart; when five or fewer projects are available, results are shown as pie charts. Hover over the visualization to view additional methylation status and sample-proportion information.
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Cancer-Specific View: Methylation Distribution and Correlation
Organ
Cancer Project
Visualization
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Methylation Summary Table
Survival map
The Survival Map summarizes the survival impact of the selected gene across cancer types, survival endpoints, omics features, and analysis methods. Colored cells indicate significant associations between the selected molecular feature and survival outcome, while uncolored cells indicate non-significant or unavailable results. Red represents higher hazard and blue represents lower hazard relative to the reference group.
Click a colored cell to view the corresponding Kaplan–Meier plot. For machine learning results, the detailed output opens in a new tab.
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Survival Analysis
The Survival Analysis panel evaluates whether the selected gene's molecular features are associated with patient prognosis. Users first select a survival analysis type: Cox Univariate, Cox Multivariate (clinical), Cure Model, Machine Learning, or Synergistic Survival Analysis. After an analysis type is selected, the available filters and result views update accordingly. Users can click the question mark icon to view descriptions for each analysis type. Algorithm descriptions and references are available in FAQ4.
Depending on the selected analysis framework and omics type, users can choose relevant options such as cancer type, survival endpoint, survival time, grouping or stratification method, CNV calling method, methylation grouping method, or machine learning algorithm. The resulting plots and tables help compare survival patterns, estimate risk differences, and identify molecular features or cross-omics interactions associated with patient outcomes.
Univariate Cox regression analysis
Cox Univariate analysis evaluates whether the selected molecular feature is associated with patient survival using univariate Cox proportional hazards regression, without adjusting for any clinical covariates. Results are presented as Kaplan–Meier survival curves and hazard ratios across cancer types and survival endpoints. An additional covariate-adjusted survival curve is displayed alongside the unadjusted result when sufficient clinical data are available.
Cox Multi (clinical)
Cox Multivariate analysis evaluates whether the selected molecular feature is independently associated with patient survival after adjusting for available clinical covariates such as age, gender, and stage. Results are presented as covariate-adjusted survival curves and forest plots summarizing the hazard ratios and confidence intervals of the molecular feature and each clinical covariate included in the model.
Cure Model
The Cure Model evaluates the association between the selected molecular feature and overall survival by estimating two distinct effects: a short-term effect reflecting the association with survival time among patients who remain at risk, and a long-term effect reflecting the association with the probability of long-term survival or cure. This approach is particularly informative for cancer types where a subset of patients may be considered functionally cured after treatment.
Beta Median All-time
Methylmix All-time
Survival analysis
Machine Learning–based survival analysis identifies molecular features associated with patient survival using three algorithms: , Random Forest, and I-Boost. Each method builds a multi-feature prognostic signature, and patients are stratified into high- and low-risk groups based on their composite signature score. Results are presented as Kaplan–Meier survival curves and ROC curves evaluating the predictive performance of each signature.
The Synergistic Survival Analysis evaluates whether the selected gene shows combined survival effects with molecular features from other omics layers, including RNA expression, mutation, CNV, and methylation. The table summarizes significant cross-omics interactions and their associated survival statistics. Selecting a row displays the corresponding Kaplan–Meier survival plots below.
The miRNA Interaction interface visualizes regulatory relationships between a user-selected gene and its associated miRNAs, integrating predicted and experimentally validated interactions across cancer types.
Users can explore the interaction network using filters for gene source, interaction type, and prediction support, while the correlation table provides cancer-specific expression-correlation statistics for individual gene–miRNA pairs.
Gene-miRNA Interaction Network
The Gene–miRNA Interaction Network displays predicted and experimentally validated interactions between the selected gene and associated miRNAs. Users can filter the network by Gene Source, Interaction Type, and Prediction Support. Predicted interactions can be filtered by the number of supporting prediction tools, while validated interactions are retained independently of the selected prediction-support threshold.
Note: When both interaction types are selected, interactions meeting either criterion are displayed. Solid lines indicate interactions with validated evidence, while dashed lines indicate predicted interactions without validated evidence.
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Interaction Type:
Selecting both displays both interaction types.
Gene-miRNA Correlation Table
The Protein interface visualizes protein-level variations, their association with mRNA expression, and post-translational modifications (PTMs) across multiple cancer types.
All results are based on the user-selected gene and can be analyzed under three conditions:
1. Across Clinical Stages – protein and mRNA–protein patterns stratified by tumor stage.
2. Across Mutation Classes – grouped by mutation impact severity.
3. Across PTM Sites – focused on specific phosphorylation sites (e.g., pY1068, pY1173).
Protein Expression by Clinical Stages (Pan-Cancer)
Boxplots display protein expression levels of the selected gene across all TCGA cancer types. Samples are grouped by clinical stage, and users can toggle individual stages using the legend. Hover over individual boxes or dots to view additional information and statistical values. Optional filters allow users to choose specific PTM sites (None, pY1068, or pY1173) to view site-specific protein patterns.
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mRNA–Protein Correlation by Clinical Stages (Pan-Cancer)
The bar chart displays Spearman correlation coefficients (ρ) between mRNA (FPKM-UQ) and protein expression (PTM-specific) across cancer types, grouped by clinical stages. Toggle individual stages using the legend to view specific stages. Hover over bars to view Cancer Type, Stage, correlation coefficient, and p-value. Selecting a bar opens a scatter plot showing gene-level correlations.
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Cancer-Specific: Stage-Specific Protein Expression
Protein Expression by Mutation Classes (Pan-Cancer)
Boxplots show protein expression across all TCGA cancer types, grouped by mutation impact class, and users can toggle individual impact class using the legend. Hover over individual boxes or dots to view additional information and statistical values. Optional filters allow users to choose specific PTM sites (None, pY1068, or pY1173) to view site-specific protein patterns.
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mRNA–Protein Correlation by Mutation Classes (Pan-Cancer)
The bar chart displays Spearman correlation coefficients (ρ) between mRNA (FPKM-UQ) and protein expression (PTM-specific) across cancer types, grouped by mutation impact. Toggle individual impact class using the legend to view specific impact class. Hover over bars to view Cancer Type, Stage, correlation coefficient, and p-value. Selecting a bar opens a scatter plot showing gene-level correlations.
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Cancer-Specific: Mutation Impact–Specific Protein Expression
The By PTM Site analysis examines how specific post-translational modifications (PTMs) influence the relationship between mRNA expression (FPKM-UQ) and protein expression across cancers. The bar chart displays Spearman correlation coefficients (ρ) between mRNA and PTM-specific protein expression across cancer types, grouped by phosphorylation site.
Click on the legend entries to toggle individual PTM sites (e.g., None, pY1068, pY1173) for targeted comparison. Hover over bars to view Cancer type, PTM site, Correlation coefficient (ρ), and p-value. Selecting a bar opens a scatter plot showing the gene-level correlation.
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Survival analysis
The Survival condition tests whether the unmodified protein abundance and (when available) site-specific PTM levels (e.g., phosphorylation sites) are associated with patient prognosis in the selected cancer cohort.
After choosing a cancer type and survival endpoint, patients are grouped by a user-defined stratification rule (e.g., High vs Low), and survival models are applied to estimate risk differences and visualize survival patterns.
Analysis frameworks
Cox Univariate (Cox Uni): Evaluates the survival association of the selected protein/PTM grouping alone (no clinical adjustment).
Cox Multivariate (clinical) (Cox Multi): Evaluates the association while adjusting for clinical covariates (e.g., age, gender, stage; depending on availability).
Cure Model: A cancer-specific survival framework to capture long-term survival patterns, including scenarios where a subset of patients may experience sustained survival.
Univariate Cox regression analysis
Cox Univariate analysis evaluates whether the selected molecular feature is associated with patient survival using univariate Cox proportional hazards regression, without adjusting for any clinical covariates. Results are presented as Kaplan–Meier survival curves and hazard ratios across cancer types and survival endpoints. An additional covariate-adjusted survival curve is displayed alongside the unadjusted result when sufficient clinical data are available.
Cox Multi (clinical)
Use the dropdown menus to select a cancer type, survival endpoint, survival time, and stratification method. The results display survival probability and cumulative hazard plots for the selected molecular feature after accounting for available clinical covariates, such as age, gender, stage, or other cohort-specific variables.
Cure Model
The Cure Model evaluates the association between the selected molecular feature and overall survival by estimating two distinct effects: a short-term effect reflecting the association with survival time among patients who remain at risk, and a long-term effect reflecting the association with the probability of long-term survival or cure. This approach is particularly informative for cancer types where a subset of patients may be considered functionally cured after treatment.
The Multi-Omics Integration interface summarizes multi-omics driver evidence for the user-selected gene across cancer projects, integrating results from RNA, mutation, CNV, and methylation analyses. Results are organized by omics type, cancer project, and supporting integration tools to show the distribution and extent of support for the selected gene across different cancer contexts.
This interface includes three result sections:
1. Integrated Multi-Omics Overview
2. Omics Connectivity Network
3. Multi-Omics Driver Event Table
Integrated Multi-Omics Overview
This visualization summarizes multi-omics driver evidence for the selected gene across cancer projects and omics types. Bar charts show the accumulated tool evidence, while the combination matrix indicates which omics types contribute evidence in each cancer project.
For better web viewing, this figure is displayed in a compact layout. Click Download PNG to obtain a higher-resolution version with expanded spacing for easier reading of project labels.
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Omics Connectivity Network
The Omics Connectivity Network illustrates how multi-omics evidence for the selected gene is distributed across omics types and cancer projects. Nodes represent the selected gene, omics types, and cancer projects, while links connect the gene to supported omics types and the corresponding cancer projects. Link width reflects the number of represented gene–omic–project relationships. Cancer projects belonging to the same cancer type are shown using the same node color to facilitate comparison across related datasets.
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Multi-Omics Driver Event Table
The table listed the detailed information and results of multi-omics driver events.