DriverDBv5

DriverDBv5: A database for human cancer driver gene research

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Cancer

Search by Tissue Type

Related Cancer Dataset

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.

Example : Lung adenocarcinoma

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.

Driver Genes
Survival Relevance

This sub-section identifies and visualizes mutation driver genes in a selected cancer type. Mutation driver genes are defined as genes supported by at least three bioinformatics tools, with greater tool support indicating higher 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 the distribution of mutation driver genes by the number of supporting tools, based on the Mutation Summary Table on the right. Genes supported by more tools are considered higher-confidence drivers.

Mutation Summary Table

Mutation Profiles of Top 30 Driver Genes

Mutation Impact Distribution of Top 30 Driver Genes

Each cell represents a mutation event for a sample–gene pair. Colors indicate mutation impact (High, Moderate, Low, Modifier). The bar on the left shows the mutation percentage for each gene, while the bars on the top and right summarize mutation counts by sample and by gene, respectively.

Tool Support for Top 30 Driver Genes

The bar plot shows the number of bioinformatics tools supporting each of the top 30 mutation driver genes. Greater tool support indicates higher confidence in driver gene identification.

This sub-section evaluates the prognostic relevance of mutation driver genes in a selected cancer type.
Using multiple survival analysis approaches, including Cox regression, cure models, and machine learning, this module examines the association between mutation driver genes and clinical outcomes across multiple survival endpoints.

Overall summary

The bar charts and Venn diagrams summarize survival-related driver genes from the selected omics data type. 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.

Note: Significant gene percentages less than 0.01% are marked as 0.0%.

Survival gene summary table

The Survival Gene Summary table lists survival-related driver 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 for detailed descriptions of the table columns and result interpretation.

Synergistic survival analysis

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.

Gene set

All CGC NCG

HR direction

All HR > 1 HR < 1

The Cancer CNV section provides a comprehensive analysis of copy number alterations in a selected cancer type. It integrates CNV driver identification with survival relevance analysis 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.

Driver Genes
Survival Relevance

This sub-section identifies and visualizes potential CNV driver genes in a selected cancer type.
Genes with significant copy number gain or loss are identified based on statistical significance, sample proportion, and copy number fold change. This module includes visualization of the top CNV driver genes, locus enrichment analysis, and a summary table of CNV driver genes.

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 alteration for the top 30 genes.
Hovering over each bar reveals detailed CNV proportions per gene.

CNV Patterns of Top 30 Genes Across Cancer Samples

This heatmap shows CNV gain, CNV loss, and no CNV alteration for the top 30 driver genes across cancer 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.

Locus Enrichment

Chromosomal Locus Enrichment of CNV Driver 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.

Locus Enrichment Summary Table

The table lists significantly enriched chromosomal loci among the CNV driver genes.

CNV Driver Gene Summary Table

This sub-section evaluates the prognostic relevance of CNV driver genes in a selected cancer type.
Using multiple survival analysis approaches, including Cox regression, cure models, and machine learning, this module examines the association between CNV driver genes and clinical outcomes across multiple survival endpoints.

Overall summary

The bar charts and Venn diagrams summarize survival-related driver genes from the selected omics data type. 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.

Note: Significant gene percentages less than 0.01% are marked as 0.0%.

Survival gene summary table

The Survival Gene Summary table lists survival-related driver 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 for detailed descriptions of the table columns and result interpretation.

Synergistic survival analysis

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.

Gene set

All CGC NCG

HR direction

All HR > 1 HR < 1

The Cancer Methylation section provides a comprehensive analysis of DNA methylation alterations in a selected cancer type. It integrates methylation driver identification with downstream survival and clinical relevance analyses to help users interpret hypermethylation and hypomethylation events from both biological and prognostic perspectives. This section contains two subtabs: Driver Genes and Survival Relevance.

Driver Genes
Survival Relevance

This sub-section identifies and visualizes potential methylation driver genes in a selected cancer type. Genes identified as methylation drivers by MethylMix are used for downstream visualization and analysis. This module includes visualization of the top methylation driver genes, locus enrichment analysis, and a summary table of methylation driver genes.
This module includes the visualization of top methylation driver genes, locus enrichment analysis, and a summary table of methylation driver genes.

Visualization of Top 30 Methylation Driver Genes

Methylation Status of Top 30 Genes

This bar chart shows the proportion of hypermethylation, hypomethylation, and no methylation alteration for each of the top 30 driver genes. Hover over a bar to view precise methylation percentages per gene.

Methylation Patterns Across Cancer Samples

This heatmap displays the methylation profiles of the top 30 driver genes across cancer samples. Each cell indicates hypermethylation, hypomethylation, or no methylation alteration. 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.

Locus Enrichment

Chromosomal Locus Enrichment of Methylation Driver 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.

Locus Enrichment Summary Table

The table lists significantly enriched chromosomal loci among the methylation driver genes.

Methylation Driver Gene Summary Table

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 driver genes from the selected omics data type. 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.

Note: Significant gene percentages less than 0.01% are marked as 0.0%.

Survival gene summary table

The Survival Gene Summary table lists survival-related driver 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 for detailed descriptions of the table columns and result interpretation.

Synergistic survival analysis

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.

Gene set

All CGC NCG

HR direction

All HR > 1 HR < 1

The Cancer RNA section provides a comprehensive analysis of gene expression alterations in a selected cancer type. It identifies RNA driver genes based on differential expression and integrates evidence from other omics data types to characterize their potential roles in cancer. The section also evaluates the prognostic relevance of RNA driver genes through downstream survival analyses. This section contains two subtabs: Driver Genes and Survival Relevance.

Driver Genes
Survival Relevance

This sub-section identifies and visualizes RNA driver genes in a selected cancer type. Genes with significant differential expression are identified based on expression level, fold change, and statistical significance. Evidence from CNV, methylation, and miRNA analyses is integrated to indicate whether each RNA driver gene is also supported by other omics data types. This module includes MA and volcano plots and a summary table of RNA driver genes.

Visualization of RNA Driver Genes

RNA Driver Gene Summary Table

This sub-section evaluates the survival relevance and synergistic effects of RNA driver genes in a selected cancer type.
By integrating survival modeling and gene–gene interaction analyses, it identifies gene pairs whose combined expression patterns exhibit enhanced prognostic significance compared to individual genes across multiple survival endpoints.

Overall summary

The bar charts and Venn diagrams summarize survival-related driver genes from the selected omics data type. 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.

Note: Significant gene percentages less than 0.01% are marked as 0.0%.

Survival gene summary table

The Survival Gene Summary table lists survival-related driver 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 for detailed descriptions of the table columns and result interpretation.

Synergistic survival analysis

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.

Gene set

All CGC NCG

HR direction

All HR > 1 HR < 1

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:

All CGC NCG

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.

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.

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.

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. "

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.

Genes in signature

Kaplan-Meier analysis

ROC curve

Genes in signature

Kaplan-Meier analysis

ROC curve

Genes in signature

Kaplan-Meier analysis

ROC curve

Genes in signature

Kaplan-Meier analysis

ROC curve

Genes in signature

Kaplan-Meier analysis

ROC curve

Genes in signature

Kaplan-Meier analysis

ROC curve

Genes in signature

Kaplan-Meier analysis

ROC curve

Genes in signature

Kaplan-Meier analysis

ROC curve

Genes in signature

Kaplan-Meier analysis

Cumulative hazard ratio

Genes in signature

Kaplan-Meier analysis

Cumulative hazard ratio

Genes in signature

Kaplan-Meier analysis

Cumulative hazard ratio

Genes in signature

Kaplan-Meier analysis

Cumulative hazard ratio

Multi-Omics Survival Gene Summary

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.

Citation

To support the continued development of this resource, please cite the following paper if you use DriverDBv4 in your work.

Chia-Hsin Liu , Yo-Liang Lai , Pei-Chun Shen , Hsiu-Cheng Liu , Meng-Hsin Tsai , Yu-De Wang , Wen-Jen Lin , Fang-Hsin Chen , Chia-Yang Li , Shu-Chi Wang , Mien-Chie Hung , Wei-Chung Cheng (2024).

DriverDBv4: a multi-omics integration database for cancer driver gene research. Nucleic Acids Research, 52(D1), D1246–D1252.

DOI Download citation (.nbib)

Contact

Tel: +886-4-2205-3366 #7929

Email: wccheng@mail.cmu.edu.tw

Address: 5F., No. 350, Sec. 1, Jingmao Rd., Beitun Dist., Taichung City 406040, Taiwan

BioinfOMICS LAB: http://www.bioinfomics.org/

DriverDBv5: A database for human cancer driver gene research

Loading...

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.

Example : Lung adenocarcinoma

Gene Source :

Gene Driver Evidence :

matching any selected option

Regulatory Layer :

Gene–Gene Interaction Type :

interactions matching any selected option

Distribution of Mutation Driver Genes by Tool Support

Displays the distribution of mutation driver genes by the number of supporting tools, based on the Mutation Summary Table on the right. Genes supported by more tools are considered higher-confidence drivers.

Mutation Summary Table

Mutation Impact Distribution of Top 30 Driver Genes

Each cell represents a mutation event for a sample–gene pair. Colors indicate mutation impact (High, Moderate, Low, Modifier). The bar on the left shows the mutation percentage for each gene, while the bars on the top and right summarize mutation counts by sample and by gene, respectively.

Tool Support for Top 30 Driver Genes

The bar plot shows the number of bioinformatics tools supporting each of the top 30 mutation driver genes. Greater tool support indicates higher confidence in driver gene identification.

Select the radio button to show KM plot.

CNV Gain and Loss Distribution of Top 30 Genes

This bar chart presents the percentage of CNV gain, CNV loss, and no CNV alteration for the top 30 genes. Hovering over each bar reveals detailed CNV proportions per gene.

CNV Patterns of Top 30 Genes Across Cancer Samples

This heatmap shows CNV gain, CNV loss, and no CNV alteration for the top 30 driver genes across cancer 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.

Chromosomal Locus Enrichment of CNV Driver 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.

Locus Enrichment Summary Table

The table lists significantly enriched chromosomal loci among the CNV driver genes.

Select the radio button to show KM plot.

Methylation Status of Top 30 Genes

This bar chart shows the proportion of hypermethylation, hypomethylation, and no methylation alteration for each of the top 30 driver genes. Hover over a bar to view precise methylation percentages per gene.

Methylation Patterns Across Cancer Samples

Chromosomal Locus Enrichment of Methylation Driver 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.

Locus Enrichment Summary Table

The table lists significantly enriched chromosomal loci among the methylation driver genes.

Select the radio button to show KM plot.

Select the radio button to show KM plot.

Analysis Filters

Gene Source :

Interaction Type:

Select candidate by:

Displays expression profiles of DE genes and miRNAs across tumor (TP) and normal (NT) samples. The displayed genes and miRNAs are based on the interaction set defined by the Analysis Filters above. Users can switch between viewing DE genes, DE miRNAs, or both via the Visualization by panel.

Genes in signature

Kaplan-Meier analysis

ROC curve

Genes in signature

Kaplan-Meier analysis

ROC curve

Genes in signature

Kaplan-Meier analysis

ROC curve

Genes in signature

Kaplan-Meier analysis

ROC curve

Genes in signature

Kaplan-Meier analysis

ROC curve

Genes in signature

Kaplan-Meier analysis

ROC curve

Genes in signature

Kaplan-Meier analysis

ROC curve

Genes in signature

Kaplan-Meier analysis

ROC curve

Genes in signature

Kaplan-Meier analysis

Cumulative hazard ratio

Genes in signature

Kaplan-Meier analysis

Cumulative hazard ratio

Genes in signature

Kaplan-Meier analysis

Cumulative hazard ratio

Genes in signature

Kaplan-Meier analysis

This bar chart presents the percentage of CNV gain, CNV loss, and no CNV alteration for the top 30 genes.
Hovering over each bar reveals detailed CNV proportions per gene.

Displays expression profiles of DE genes and miRNAs across tumor (TP) and normal (NT) samples.
The displayed genes and miRNAs are based on the interaction set defined by the Analysis Filters above. Users can switch between viewing DE genes, DE miRNAs, or both via the Visualization by panel.