DriverDBv4: A database for human cancer driver gene research



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.
(OPTIONAL) 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

Gene Source :

Node (Driver) Type :

Interaction Type :


Driver Summary Table


The Cancer Mutation 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.
(OPTIONAL) It includes two main 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.

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 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.

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.



The Cancer CNV section visualizes genes exhibiting significant copy number variation (CNV) gain or loss in a selected cancer type. It provides an overview of CNV driver distributions across pan-cancer datasets and chromosomes, helping users explore CNV–expression relationships.
(OPTIONAL) It includes three main components: the Visualization of Top 30 CNV Driver Genes, Locus Enrichment, and the CNV Driver Gene Summary Table.
Users can select to analyze results defined by either one or two CNV-detection tools for comparison.


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.



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. (OPTIONAL) 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-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.

Locus Enrichment Summary Table

The table lists enriched pathways associated with CNV-affected genes.




CNV Driver Gene Summary Table


The Cancer Methylation section visualizes genes with significant hypermethylation or hypomethylation in a selected cancer type. It illustrates the distribution of methylation driver genes across pan-cancer datasets and chromosomes, helping users explore methylation–expression relationships.
(OPTIONAL) It includes three main components: the Visualization of Top 30 Methylation Driver Genes, Locus Enrichment, and the Methylation Driver Gene Summary Table.
Users can select to analyze results defined by one or two methylation-detection tools.


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.



Methylation Patterns Across Cancer Samples

This heatmap displays the methylation profiles of the top 30 driver genes across patient samples. (OPTIONAL) 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.


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.

Locus Enrichment Summary Table

Lists pathways enriched among methylation-associated genes.




Methylation Driver Gene Summary Table


The Cancer Survival section visualizes the survival relevance and synergistic effects of driver genes in user-selected cancer type. It integrates survival data to help users identify gene pairs whose combined expression levels are significantly associated with patient outcomes.
(OPTIONAL) The section consists of two main components: Survival Synergy Network and Survival of Synergistic Effect.


Survival Synergy Network

The Cancer Survival network illustrates synergistic effects between significant survival-relevant genes in selected cancer type. A synergistic effect is defined by the combined hazard ratio (HR) of two genes and is categorized into two levels: 1.5-fold and 2-fold, each with a positive (>1) or negative (<1) HR direction. Edges connect gene pairs whose joint HR exceeds that of either gene alone.


Survival of Synergistic Effect

This section summarizes synergistic gene pairs and their associated survival statistics in selected cancer type. The table lists each gene pair with corresponding cancer type, hazard ratio (HR) fold change, and log-rank p-values. Selecting a row generates survival plots on the right: the top plot displays survival curves for all four gene expression combinations (All.low, Low.high, High.low, All.high), while the bottom plot compares All.high against all other groups. Lower survival probabilities in the All.high group suggest a synergistic negative effect on prognosis.


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.


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.

Gene-miRNA Correlation Summary Table


The Cancer Multi-omics section visualizes driver genes identified through multi-omics integration tools in the selected cancer type. It integrates data from multiple molecular layers—including mutations, CNV, methylation, mRNA, and miRNA—to prioritize multi-omics drivers and explore their biological functions and distributions.
Users can choose to analyze all genes, or restrict to those included in the Cancer Gene Census (CGC) or the Network of Cancer Genes (NCG6.0).


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.





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.






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)

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