DriverDBv5: A database for human cancer driver gene research
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Cancer
Browse by Cancer Type
DriverDBv5 provides analysis across 78 cancer projects. Use the two dropdown menus on the left to select your cancer type and data source:
1. Tissue Type - Narrow down cancer datasets by tissue type. This helps you quickly find related cancer datasets if you are exploring a specific tissue.
2. Related dataset – Select your specific cancer type along with its data source (e,g., Lung Adenocarcinoma (TCGA)). After making your selections, click "Submit" to view driver gene information and molecular features for your chosen cancer dataset.
The Cancer Summary section provides an overview of potential driver genes and miRNA drivers for a selected cancer type, integrating cancer dysfunction and dysregulation events across multiple omics levels.
It includes two main components: the Summary Network and the Driver Summary Table, which together offer both visual and analytical insights into cancer driver mechanisms.
Summary Network
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Gene Source :
Node Type :
Driver Summary Table
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The Cancer Mutation section provides a comprehensive analysis of mutation-related features in the selected cancer type. It integrates mutation driver identification with downstream survival and clinical relevance analyses to help users interpret mutation events from both biological and prognostic perspectives.
This section contains two subtabs: Driver Genes and Survival Relevance.
This sub-section identifies and visualizes potential mutation driver genes in a selected cancer type.
Mutation drivers are detected and prioritized by multiple bioinformatics tools, and their consistency across tools provides a measure of confidence.
It includes two components: the Mutation Driver Summary by Tools and the Mutation Profiles of Top 30 Driver Genes.
Mutation Driver Summary by Tools
Distribution of Mutation Driver Genes by Tool Support
Displays how many genes were identified by different numbers of tools, derived from the Mutation Summary Table on the right. Genes supported by more tools are considered higher-confidence drivers.
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Mutation Summary Table
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Mutation Profiles of Top 30 Driver Genes
Mutation Impact Distribution of Top 30 Driver Genes
Each cell represents a mutation event for a patient–gene pair. Colors indicate mutation impact (High, Moderate, Low, Modifier). The bar on the left shows the total mutation percentage for each gene, while the bars on the top and right summarize total mutation counts by sample and by gene, respectively.
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Tool Support for Top 30 Driver Genes
The bar plot shows how many bioinformatics tools identified each of the top 30 genes as mutation drivers. Genes supported by more tools indicate higher confidence.
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This sub-section evaluates the prognostic relevance of mutation-associated genes in a selected cancer type.
Using multiple survival analysis frameworks, including Cox regression, machine learning–based models, and cure models, this module examines the association between gene-level mutation status and clinical outcomes across distinct survival endpoints.
Overall summary
The bar charts and Venn diagrams summarize survival-related genes identified from the selected omics data type, including mutation, copy number variation (CNV), or methylation. Results are shown across four survival endpoints: overall survival (OS), progression-free interval (PFI), disease-specific survival (DSS), and disease-free interval (DFI), and four analysis methods: Cox univariate regression, Cox multivariate regression adjusted for clinical variables, cure model analysis, and machine learning.
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Note: Significant gene percentages less than 0.01% are marked as 0.0%.
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Note: Significant gene percentages less than 0.01% are marked as 0.0%.
Overall summary
The Survival Gene Summary table lists survival-related genes identified in the selected cancer type across four survival endpoints: overall survival (OS), progression-free interval (PFI), disease-specific survival (DSS), and disease-free interval (DFI). Each tab summarizes results from Cox Univariate, Cox Multivariate (Clinical), Cure Model, and Machine Learning analyses. Users can click the question mark icon to view detailed descriptions of the table columns and how to interpret the results.
The Synergistic Survival Analysis section evaluates whether the selected gene’s mutation status, CNV status, or methylation status has combined survival effects with related genes or features from other omics layers. The table lists synergistic survival interactions, including cancer type, interaction type, gene symbols, omics levels, hazard ratio, and p-value. Selecting a row generates the corresponding Kaplan–Meier survival plots below.
Select the radio button to show KM plot.
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The Cancer CNV section provides a comprehensive analysis copy number alterations in a selected cancer type. It integrates CNV driver identification with downstream survival and clinical relevance analyses to help users interpret copy number gain and loss events from both biological and prognostic perspectives.
This section contains two subtabs: Driver Genes and Survival Relevance.
This sub-section identifies and visualizes potential CNV driver genes in a selected cancer type.
Genes exhibiting significant copy number gain or loss are prioritized based on statistical significance and sample-level distributions.
This module includes the visualization of top CNV driver genes, locus enrichment analysis, and a summary table of CNV driver genes.
Visualization of:
Visualization of Top 30 CNV Driver Genes
CNV Gain and Loss Distribution of Top 30 Genes
This bar chart presents the percentage of CNV gain, CNV loss, and no CNV for the top 30 genes. Hovering over each bar reveals detailed CNV proportions per gene.
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CNV Patterns of Top 30 Genes Across Cancer Samples
This heatmap shows CNV gain, CNV loss, and no CNV events for the top 30 driver genes across patient samples. The side panel on the left displays the total CNV percentage for each gene, while the top and right bar charts summarize total CNV occurrences by sample and by gene, respectively.
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Locus Enrichment
Chromosomal Locus Enrichment of CNV-Associated Genes
Each red dot represents a gene mapped to its chromosomal position. Hover over a dot to view details such as chromosome, position, correlation value, and gene name.
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Locus Enrichment Summary Table
The table lists enriched pathways associated with CNV-affected genes.
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CNV Driver Gene Summary Table
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This sub-section evaluates the prognostic relevance of CNV-associated genes in a selected cancer type.
By integrating multiple survival analysis frameworks, including Cox regression, machine learning–based models, and cure models, it examines the association between gene-level copy number alterations and patient outcomes across distinct survival endpoints.
Overall summary
The bar charts and Venn diagrams summarize survival-related genes identified from the selected omics data type, including mutation, copy number variation (CNV), or methylation. Results are shown across four survival endpoints: overall survival (OS), progression-free interval (PFI), disease-specific survival (DSS), and disease-free interval (DFI), and four analysis methods: Cox univariate regression, Cox multivariate regression adjusted for clinical variables, cure model analysis, and machine learning.
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Note: Significant gene percentages less than 0.01% are marked as 0.0%.
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Note: Significant gene percentages less than 0.01% are marked as 0.0%.
Overall summary
The Survival Gene Summary table lists survival-related genes identified in the selected cancer type across four survival endpoints: overall survival (OS), progression-free interval (PFI), disease-specific survival (DSS), and disease-free interval (DFI). Each tab summarizes results from Cox Univariate, Cox Multivariate (Clinical), Cure Model, and Machine Learning analyses. Users can click the question mark icon to view detailed descriptions of the table columns and how to interpret the results.
The Synergistic Survival Analysis section evaluates whether the selected gene’s mutation status, CNV status, or methylation status has combined survival effects with related genes or features from other omics layers. The table lists synergistic survival interactions, including cancer type, interaction type, gene symbols, omics levels, hazard ratio, and p-value. Selecting a row generates the corresponding Kaplan–Meier survival plots below.
Select the radio button to show KM plot.
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The Cancer Methylation section provides a comprehensive analysis DNA methylation alterations in a selected cancer type. It integrates methylation driver identification with downstream survival and clinical relevance analyses to help users interpret copy number gain and loss events from both biological and prognostic perspectives.
This section contains two subtabs: Driver Genes and Survival Relevance.
This sub-section identifies and visualizes potential methylation driver genes in a selected cancer type.
Genes exhibiting significant hypermethylation or hypomethylation are prioritized based on their methylation patterns across patient samples and chromosomal loci.
This module includes the visualization of top methylation driver genes, locus enrichment analysis, and a summary table of methylation driver genes.
Visualization of:
Visualization of Top 30 Methylation Driver Genes
Methylation Status of Top 30 Genes
This bar chart shows the proportion of hypermethylation, hypomethylation, and non-methylation for each of the top 30 driver genes. Hover over a bar to view precise methylation percentages per gene.
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Methylation Patterns Across Cancer Samples
This heatmap displays the methylation profiles of the top 30 driver genes across patient samples. Each cell represents a methylation event, with color indicating hypermethylation or hypomethylation. The side panel on the left shows the total methylation percentage for each gene, while the top and right bars summarize total methylation events by sample and by gene.
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Locus Enrichment
Chromosomal Locus Enrichment of Methylation-Associated Genes
Each red dot marks a gene mapped to its chromosomal location. Hover to view details such as chromosome, position, correlation value, and gene symbol. Positive correlations indicate methylation-driven expression changes.
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Locus Enrichment Summary Table
Lists pathways enriched among methylation-associated genes.
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Methylation Driver Gene Summary Table
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This sub-section evaluates the prognostic relevance of methylation-associated genes in a selected cancer type.
By integrating multiple survival analysis frameworks, including Cox regression, machine learning–based models, and cure models, it examines the association between gene-level methylation status and patient outcomes across distinct survival endpoints.
Overall summary
The bar charts and Venn diagrams summarize survival-related genes identified from the selected omics data type, including mutation, copy number variation (CNV), or methylation. Results are shown across four survival endpoints: overall survival (OS), progression-free interval (PFI), disease-specific survival (DSS), and disease-free interval (DFI), and four analysis methods: Cox univariate regression, Cox multivariate regression adjusted for clinical variables, cure model analysis, and machine learning.
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Note: Significant gene percentages less than 0.01% are marked as 0.0%.
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Note: Significant gene percentages less than 0.01% are marked as 0.0%.
Overall summary
The Survival Gene Summary table lists survival-related genes identified in the selected cancer type across four survival endpoints: overall survival (OS), progression-free interval (PFI), disease-specific survival (DSS), and disease-free interval (DFI). Each tab summarizes results from Cox Univariate, Cox Multivariate (Clinical), Cure Model, and Machine Learning analyses. Users can click the question mark icon to view detailed descriptions of the table columns and how to interpret the results.
The Synergistic Survival Analysis section evaluates whether the selected gene’s mutation status, CNV status, or methylation status has combined survival effects with related genes or features from other omics layers. The table lists synergistic survival interactions, including cancer type, interaction type, gene symbols, omics levels, hazard ratio, and p-value. Selecting a row generates the corresponding Kaplan–Meier survival plots below.
Select the radio button to show KM plot.
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The Cancer miRNA section visualizes the relationships between differentially expressed (DE) genes and miRNAs in the user-selected cancer type. This section helps identify gene–miRNA interactions that may play regulatory roles in cancer, including both experimentally validated and computationally predicted associations.
The section consists of three main components: miRNA–Gene Interaction Network, Visualization of Differentially Expressed Genes and miRNAs, and the Gene–miRNA Correlation Table.
miRNA-Gene Interaction Network
This network visualizes validated (solid lines) and predicted (dotted lines) interactions between miRNAs and genes. Green nodes represent genes; yellow nodes represent miRNAs. Users can click on a node to highlight connected partners and click on empty space to reset the view. Filters allow refinement by gene source, prediction tool threshold, and validation status.
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Visualization by
Heatmap of Differentially Expressed Genes and miRNAs
Displays expression levels of DE genes and miRNAs across tumor (TP) and normal (NT) samples. Color intensity represents expression magnitude (red: high, blue: low). Users can switch between viewing DE genes, DE miRNAs, or both via the “Visualization by” panel.
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Gene-miRNA Correlation Summary Table
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The Cancer Multi-omics section summarizes multi-omics driver and machine learning–based survival signature results for the user-selected cancer type. It integrates multiple molecular layers, including mutation, CNV, methylation, mRNA, and miRNA, to help users explore driver genes, survival-related signatures, and gene distributions across omics layers.
This section also includes a machine learning summary table, signature results across four survival endpoints, and gene distribution results from multi-omics analysis. Signature results are computed using Lasso, Random Forest, and I-Boost, and include survival gene tables, Kaplan–Meier plots, and ROC curves or cumulative hazard plots. Users can analyze all genes or focus on genes from CGC or NCG 6.0.
Visualization of:
Multi-Layer Relationship Diagram of Multi-Omics Drivers and Biological Functions
This diagram visualizes the hierarchical relationships among the selected cancer type, omics layers (mutation, CNV, methylation, mRNA, and miRNA), genes, and Gene Ontology (GO) terms. The flow from left to right illustrates how multi-omics drivers link molecular alterations to biological functions.
Nodes with more connections represent genes or GO terms with broader influence across multiple omics. Users can switch between All, CGC, or NCG gene sets using the radio buttons above. Detailed results are listed in the table below.
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Distribution of Multi-Omics Drivers Across Omics Layers
The two complementary visualizations summarizes the distribution of multi-omics drivers across omics types and identification tools. The left plot visualizes the number of identification tools supporting each gene across omics layers and the right plot shows top genes ranked by the number of tools identifying them as multi-omics drivers.
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Cross-Tool Comparison of Multi-Omics Driver Detection
The two visualizations compares the coverage and consistency of
multi-omics identification tools.
across omics levels. The left plot illustrates the proportion of genes identified by each tool across omics levels. Hover over a cell to view detailed values. The right plot displays how many genes were identified by a given number of tools. Hover over bars for specific counts of genes per tool level.
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Machine Learning Results
The Machine Learning Results panel summarizes significant prognostic signatures identified by Lasso, Random Forest, and I-Boost for the selected cancer type. Results are available across four omics data types — RNA expression, mutation, CNV, and methylation — each displayed in a separate tab. Each entry represents a significant survival association identified by a specific algorithm and endpoint combination, with patients stratified into high- and low-risk groups based on their composite signature score.
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Signature results
The Signature Results panel displays the prognostic signature identified by the selected machine learning algorithm and survival endpoint for the selected cancer type. Users can select an algorithm and endpoint from the left menu to view the corresponding signature gene table, Kaplan–Meier survival plot, and predictive performance plot. Results may include features from one or more omics data types — RNA expression, mutation, CNV, and methylation — with the omics source of each feature indicated in the gene table.
The Multi-Omics Survival Gene Summary panel provides an overview of survival-related genes identified by machine learning algorithms across omics data types, survival endpoints, and algorithms for the selected cancer type. The bar charts summarize the distribution of significant genes, and the table below lists each identified gene with its survival associations across all endpoint and algorithm combinations.