Click the radio button next to the interested gene to access its corresponding page, where you will find information about the Ensembl ID, official symbol, and aliases displayed at the top.
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 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, mutation class, 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.
Boxplots display TPM expression of the selected gene across all cancer types (x-axis), grouped by mutation class.
Click on the legend entries to toggle mutation impact groups (High, Moderate, Low, Modifier, Normal Tissue, Tumors without Mutation).
Hover over dots for Cancer Type, Mutation Class, and TPM, or near box areas to view summary statistics.
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Cancer-Specific View: Expression by Mutation Class
Boxplots show TPM expression of the selected gene across all cancer types, grouped by tumor stage (Stage I–IV).
Click on the legend entries to toggle stages. Hover over dots for Cancer Type, Stage, and TPM, or 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.
Synergistic Survival Analysis evaluates whether the selected gene shows combined survival effects with related genes or molecular features from other omics layers, including RNA expression, mutation, CNV, and methylation. Results are presented as a table of significant cross-omics interactions with hazard ratios and p-values, along with Kaplan–Meier survival plots showing how combined omics states stratify patient survival.
Select the radio button to show KM plot.
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: 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.
Synergistic Survival Analysis evaluates whether the selected gene shows combined survival effects with related genes or molecular features from other omics layers, including RNA expression, mutation, CNV, and methylation. Results are presented as a table of significant cross-omics interactions with hazard ratios and p-values, along with Kaplan–Meier survival plots showing how combined omics states stratify patient survival.
Select the radio button to show KM plot.
The Copy Number Variation interface visualizes CNV patterns of the selected gene across multiple cancer types.
Each analysis integrates results from two CNV-identification tools, iGC and DIGGIT, and provides both pan-cancer and cancer-specific visualizations, along with a summary table of CNV details.
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 how many CNV-identification tools detected the selected gene as significant. Darker gray indicates that both tools detected significance, while lighter gray indicates that one tool detected significance.
Below the driver panel, CNV results are summarized using iGC and DIGGIT. When five or more projects are available, results are displayed as a bar chart. When fewer than five projects are available, results are displayed as a pie chart. 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: 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 Methylation interface visualizes the DNA methylation status of the selected gene across multiple cancer types and explores how methylation relates to gene expression.
Analyses are performed using two complementary methylation tools, MethylMix and ELMER, to identify hypermethylated and hypomethylated genes. 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 cancer projects.
The Methylation driver panel indicates how many methylation tools detected the selected gene as significant. Darker gray indicates that both tools detected significance, while lighter gray indicates that one tool detected significance.
Below the driver panel, methylation results are summarized using MethylMix and ELMER. When five or more projects are available, results are displayed as a bar chart. When fewer than five projects are available, results are displayed as a pie chart. Colors indicate hypermethylation, hypomethylation, or no methylation change. Hover over bars or pie segments to view additional details, including cancer type, methylation status, tool-specific results, and sample proportion.
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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: 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.
Synergistic Survival Analysis evaluates whether the selected gene shows combined survival effects with related genes or molecular features from other omics layers, including RNA expression, mutation, CNV, and methylation. Results are presented as a table of significant cross-omics interactions with hazard ratios and p-values, along with Kaplan–Meier survival plots showing how combined omics states stratify patient survival.
Select the radio button to show KM plot.
The miRNA Interaction interface visualizes and quantifies the regulatory relationships between a user-selected gene and its associated miRNAs across multiple cancer types.
Interactions are derived from 12 prediction tools and experimentally validated datasets (miRTarBase), with optional filtering by gene source and prediction support level.
Gene-miRNA Interaction Network
The Gene–miRNA Interaction Network illustrates predicted and validated connections between the selected gene and miRNAs. Interactions are derived from 12 prediction tools or experimentally validated data in miRTarBase. Users can filter the network by Gene Source, Minimum Prediction Support (≥6, ≥8, or ≥10 tools), and whether to display only validated interactions.
Note: When Validated is selected, interactions are shown if they are either experimentally validated (solid lines) or meet the selected minimum prediction support (dotted lines).
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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 how multiple omics layers—including gene expression, mutation, CNV, methylation, and protein—converge to identify potential driver genes across cancer types.
All results are based on multi-omics prediction tools and reflect the degree of support for the user-selected gene as a driver event.
This interface includes three main result sections:
1. Integrated Multi-Omics Overview
2. Omics Connectivity Network
3. List of Multi-Omics Driver Events
Integrated Multi-Omics Overview
This visualization integrates results from multiple omics analyses and prediction tools across cancer types and reveals the importance of the selected gene.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 relationships between the selected gene, omics layers, and cancer types.
Nodes represent entities such as the gene, omics type, or cancer project, and edges denote associations identified by integration tools. Cancer projects belonging to the same cancer type are shown using the same node color to help users recognize related datasets across projects. Nodes with more connections reflect a higher degree of multi-omics influence or cross-layer integration.
Nodes with more connections reflect a higher degree of multi-omics influence or cross-layer integration.
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Multi-Omics Driver Event Table
The table listed the detailed information and results of multi-omics driver events.