DriverDBv4 - a multi-omics integration database for cancer driver gene research

Sep 21, 2026

Uploaded by Jiven

Advances in high-throughput technologies provide researchers with extensive multi-omics datasets for understanding cancer biology, yet conventional statistical models and databases struggle with high-dimensional, multi-omics interpretation. DriverDBv4 addresses this gap by updating the DriverDB cancer driver gene resource with expanded cohorts, added proteomics data, multiple multi-omics driver-identification algorithms, new visualizations, and enhanced customized analysis functions. The database enables comprehensive cross-cancer interpretation, supports study of cancer heterogeneity, and helps guide personalized clinical approaches.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages7

Reading Time18 min

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Abstract

Graphical abstract

Introduction

DriverDBv4: a multi-omics integration database for cancer driver gene research Chia-Hsin Liu 1 ,†, Yo-Liang Lai2 ,†, Pei-Chun Shen 1 , Hsiu-Cheng Liu 1 , Meng-Hsin Tsai 1 , Yu-De Wang3 ,4 , Wen-Jen Lin 1 ,5 , Fang-Hsin Chen6 , Chia-Yang Li7 , Shu-Chi Wang8 , Mien-Chie Hung 1 ,3 ,9 , 10 , 11 and Wei-Chung Cheng 1 ,3 , 12 , * 1 Cancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan 2 Department of Radiation Oncology, China Medical University, Taichung 404328, Taiwan. 3 Graduate Institute of Biomedical Sciences, China Medical University, Taichung 404328, Taiwan 4 Department of Urology, China Medical University, Taichung 404328, Taiwan 5 School of Medicine, China Medical University, Taichung 404328, Taiwan 6 Institute of Nuclear Engineering and Science, National Tsing Hua University, Hsinchu 300044, Taiwan 7 Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan 8 Department of Medical Laboratory Science and Biotechnology, Kaohsiung Medical University, Kaohsiung 80708, Taiwan 9 Institute of Biochemistry and Molecular Biology, China Medical University, Taichung 404328, Taiwan 10 Molecular Medicine Center, China Medical University Hospital, China Medical University, Taichung 404328, Taiwan 11 Department of Biotechnology, Asia University, Taichung 413305, Taiwan 12The Ph. D. program for Cancer Biology and Drug Discovery, China Medical University and Academia Sinica, Taichung 404328, Taiwan * To whom correspondence should be addressed. Tel: +886 4 2205 2121 (Ext 7820); Fax: +886 4 2233 3496; Email: [cwc0702@gmail.com](cwc0702@gmail.com)[ ](cwc0702@gmail.com)†The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors. Abstract Advancements in high-throughput technology offer researchers an extensive range of multi-omics data that provide deep insights into the complex landscape of cancer biology. However, traditional statistical models and databases are inadequate to interpret these high-dimensional data within a multi-omics framework. To address this limitation, we introduce DriverDBv4, an updated iteration of the DriverDB cancer driver gene database ([http://driverdb.bioinfomics.org/](http://driverdb.bioinfomics.org/)) . This updated version offers several significant enhancements: (i) an increase in the number of cohorts from 33 to 70, encompassing approximately 24 000 samples; (ii) inclusion of proteomics data, augmenting the existing types of omics data and thus expanding the analytical scope; (iii) implementation of multiple multi-omics algorithms for identification of cancer drivers; (iv) new visualization features designed to succinctly summarize high-context data and redesigned existing sections to accommodate the increased volume of datasets and (v) two new functions in Customized Analysis, specifically designed for multi-omics driver identification and subgroup expression analysis. DriverDBv4 facilitates comprehensive interpretation of multi-omics data across diverse cancer types, thereby enriching the understanding of cancer heterogeneity and aiding in the development of personalized clinical approaches. The database is designed to foster amore nuanced understanding of the multi-faceted nature of cancer. Received: September 14, 2023. Revised: October 12, 2023. Editorial Decision: October 21, 2023. Accepted: October 25, 2023 © The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ([http:](http://creativecommons.org/licenses/by-nc/4.0/)[//](http://creativecommons.org/licenses/by-nc/4.0/)[creativecommons.org](http://creativecommons.org/licenses/by-nc/4.0/)[/](http://creativecommons.org/licenses/by-nc/4.0/)[licenses](http://creativecommons.org/licenses/by-nc/4.0/)[/](http://creat

FAQs

What problem does DriverDBv4 aim to solve in multi-omics cancer research?

DriverDBv4 targets the limitation of traditional statistical models and databases that cannot adequately interpret high-dimensional multi-omics data within a unified framework.

What key improvements distinguish DriverDBv4 from the earlier DriverDB?

DriverDBv4 increases the number of cohorts, adds proteomics as a new data type, introduces multiple multi-omics algorithms for driver identification, and expands visualization and customized analysis capabilities.

How does DriverDBv4 support analysis across different cancer types?

It facilitates comprehensive interpretation of multi-omics data across diverse cancer types, helping researchers better understand cancer heterogeneity and supporting personalized clinical development.

Sep 21, 2026

Uploaded by Jiven