DriverDBv3: A database for human cancer driver gene research



The Cancer Summary section provides a Summary network which integrates cancer dysfunction and dysregulation events in a multi-omics level, and a Functional Annotation analysis of these cancer driver genes.


Summary network

The Cancer Summary network presents the relationships between driver genes and miRNA drivers in a specific cancer type. The resources of gene dataset include the Cancer Gene Census (GCG) and the Network of Cancer Genes (NGC6.0). Driver genes possess various features and are denoted by nodes gridded with colors (as shown in the legend figure); miRNA drivers are denoted by yellow nodes. These nodes are connected by lines to show the protein-protein interactions (PPIs) in the STRING database and synergistic effects, defining by which hazard ratio (HR) of two genes is greater than 1.5 fold of each gene. Also, the interactions of miRNAs and genes are recorded in miRTarBase.

Note: The nodes without any connection would be removed.

Node:


Driver summary table


Functional Annotation

The Functional Annotation section provides 3 levels of functional analysis of driver genes: Gene Oncology, Pathways, and Protein/Genetics interactions.

Gene Ontology

The Gene Ontology (GO) describes our knowledge of the biological domain. GO categories and genes are divided into three groups: biological process, cellular component, and molecular function.

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Pathway

The Pathway section includes 12 collections of gene sets sourced from 7 public databases: KEGG, Recatome, PID, Biocarta, MsigDB, miRTar, and miRWalk.

Note: Only the top 30 pathways and their corresponding genes are shown below.

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Protein/Genetics Interaction

The Protein/Genetic interaction section provides a network of how proteins and genes interact with each other, and it lays out from 3 databases: BioGRID, IntAct and STRING.






The Cancer Mutation function provides visualizations to illustrate the mutation drivers identified by bioinformatics tools in a specific cancer type.


mutation driver defined by tools

The plot indicates defined mutation driver numbers by different number of computational tools according to the mutation summary table.

Mutation Summary Table


Visualization of Top 30 genes

This plot illustrates the relations between the top 30 mutation driver genes and cancer patients; the samples of cancer patients are on the x-axis and the top 30 genes are on the y-axis.

This plot shows the top 30 genes on the y-axis and the number of tools by which they are defined on x-axis.


The Cancer CNV function provides visualizations to illustrate the genes with a high copy number variation (CNV) gain or loss in a specific cancer type.


Visualization of:


Visualization of Top 30 genes

This bar chart provides an overview of the CNV percentages for each of the top 30 genes in a specific cancer type.


This plot illustrates the relations between the top 30 genes and their CNV in cancer patients for a specific cancer type; the samples of cancer patients are on the x-axis and the top 30 genes are on the y-axis.


Locus enrichment

The Locus Enrichment graph marks the loci on each chromosome using a red dot. Hover over the dots to see detailed information such as the chromosome number, position, value and gene symbol.



CNV table


The Cancer Methylation function provides visualizations to illustrate the genes with a high methylation in a specific cancer type.


Visualization of:


Visualization of Top 30 genes

This bar chart provides an overview of the methylation percentages for each of the top 30 genes in a certain cancer type.


This plot illustrates the relations between the top 30 genes and their methylation type in cancer patients for a certain cancer type; the samples of cancer patients are on the x-axis and the top 30 genes are on the y-axis.


Locus enrichment

The Locus Enrichment graph marks the loci on each chromosome using a red dot. Hover over the dots to see detailed information such as the chromosome number, position, value and gene symbol.



Methylation table


The Cancer Survival function provides visualizations to illustrate the survival probability under the synergistic effect of gene pairs in a specific cancer type.


Survival network

The Cancer Survival network illustrates the synergistic effect between significant survival-relevant genes. The synergistic effect is defined by the hazard ratio (HR) of two genes, and has 2 levels – 1.5 folds and 2 folds; each with a positive (>1) and negative (<1) direction of HR.


Survival of synergistic effect

This section includes all gene pairs with HR fold change greater than 1.5 in both directions. For each gene pair, the patients are stratified to groups based on their levels of expression for the genes. The groups of patients are then compared on their survival probability over months, as shown in the plots.


The Cancer miRNA function provides visualizations to illustrate the relations between differentially expressed (DE) genes and miRNAs in a specific cancer type.


Cancer miRNA network

The Cancer miRNA network illustrates the relations between differentially expressed (DE) genes and miRNAs as identified by bioinformatics tools. Validated relations are represented by solid lines; whereas predicted relations are represented by dotted lines. Predicted relations can be further filtered by a minimum of 6, 8, or 10 tools.


Visualization by

Visualization of differential expressed gene and miRNA

This heat map illustrates the relations between differentially expressed (DE) genes/miRNAs and cancer patients for a certain cancer type; the samples of cancer patients are on the x-axis and the DE genes/miRNAs are on the y-axis.

Gene-miRNA table



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