DriverDBv4: A database for human cancer driver gene research
Customized Analysis
Subgroup Comparison Analyses
Compare gene-level molecular features (expression, mutation, CNV, or methylation) across clinically defined patient subgroups to uncover group-specific differences.
Compare the expression levels of a selected gene across clinically defined patient subgroups within a chosen cancer dataset.
Evaluate whether mutation frequencies differ between two clinically defined patient groups using Fisher’s Exact Test.
Assess differences in gene-level copy number variation between two patient groups, with multiple contingency tests for gain, loss, and neutral states.
Visualize and compare methylation (β-values) of a selected gene across clinical subgroups in a cancer cohort.
Survival Analyses
Assess how gene mutations or expression levels influence user-defined patient subpopulation survival and generate survival curves and statistical summaries.
Assess how mutations in selected genes impact survival in a specific patient subpopulation, with mutation oncoprints and Kaplan–Meier analyses based on mutation-defined groups.
Investigate how gene expression levels are associated with specific patient subpopulation survival outcomes, using customizable expression cutoffs and KM survival curves.
Identify multi-omics driver events differentiating two patient groups and explore cross-omics functional relationships through integrative visualizations. Choose this method if the goal is to understand which genomic and epigenomic events drive differences between clinically defined patient subpopulations (e.g., responders vs non-responders, early vs late stage).
Upload or enter a gene list to build a prognostic signature using LASSO and Random Forest, generating performance metrics, ROC curves, heatmaps, and survival plots. Use this analysis if you have a gene list and want to build a prognostic signature that predicts patient outcomes.
Construct multivariable Cox models combining gene features with clinical factors to evaluate independent prognostic contributions. Use this analysis if you want to evaluate whether a gene, gene set, or signature is a prognostic factor independent of clinical variables.
Access Results
Completed analyses produce a Result ID linked to your selected parameters. After computation, an email containing the Result ID and a direct results link is sent to the user.
Enter the Result ID here to load the full analysis output and access all downloadable files.
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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.