Discovering potential cancer driver genes by an integrated network-based approach

2016 ◽  
Vol 12 (9) ◽  
pp. 2921-2931 ◽  
Author(s):  
Kai Shi ◽  
Lin Gao ◽  
Bingbo Wang
Keyword(s):  
Gene Expression ◽  
Gene Mutations ◽  
Driver Genes ◽  
Integrated Network ◽  
Cancer Driver ◽  
Cancer Driver Genes ◽  
The Relationship ◽  
Potential Cancer ◽  
Patient Mutation ◽  
Network Diffusion

An integrated network-based approach is proposed to nominate driver genes. It is composed of two steps including a network diffusion step and an aggregated ranking step, which fuses the correlation between the gene mutations and gene expression, the relationship between the mutated genes and the heterogeneous characteristic of the patient mutation.

Comparative Oncogenomics Identifies PSMB4 and SHMT2 as Potential Cancer Driver Genes

2014 ◽  
Vol 74 (11) ◽  
pp. 3114-3126 ◽  
Author(s):  
Genee Y. Lee ◽  
Peter M. Haverty ◽  
Li Li ◽  
Noelyn M. Kljavin ◽  
Richard Bourgon ◽  
...  
Keyword(s):  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Driver Genes ◽  
Potential Cancer

scholarly journals DriverSubNet: A Novel Algorithm for Identifying Cancer Driver Genes by Subnetwork Enrichment Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Di Zhang ◽  
Yannan Bin
Keyword(s):  
Gene Expression ◽  
Cancer Patients ◽  
Therapeutic Targets ◽  
Enrichment Analysis ◽  
Prognostic Biomarkers ◽  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Driver Genes ◽  
Critical Challenge ◽  
Novel Algorithm

Identification of driver genes from mass non-functional passenger genes in cancers is still a critical challenge. Here, an effective and no parameter algorithm, named DriverSubNet, is presented for detecting driver genes by effectively mining the mutation and gene expression information based on subnetwork enrichment analysis. Compared with the existing classic methods, DriverSubNet can rank driver genes and filter out passenger genes more efficiently in terms of precision, recall, and F1 score, as indicated by the analysis of four cancer datasets. The method recovered about 50% more known cancer driver genes in the top 100 detected genes than those found in other algorithms. Intriguingly, DriverSubNet was able to find these unknown cancer driver genes which could act as potential therapeutic targets and useful prognostic biomarkers for cancer patients. Therefore, DriverSubNet may act as a useful tool for the identification of driver genes by subnetwork enrichment analysis.

scholarly journals Evaluating the evaluation of cancer driver genes

2016 ◽  
Vol 113 (50) ◽  
pp. 14330-14335 ◽  
Author(s):  
Collin J. Tokheim ◽  
Nickolas Papadopoulos ◽  
Kenneth W. Kinzler ◽  
Bert Vogelstein ◽  
Rachel Karchin
Keyword(s):  
Somatic Mutations ◽  
Gene Prediction ◽  
Gene Mutations ◽  
Evaluation Framework ◽  
Mutation Rates ◽  
Driver Gene ◽  
Driver Genes ◽  
Cancer Driver ◽  
Bona Fide ◽  
Cancer Driver Genes

Sequencing has identified millions of somatic mutations in human cancers, but distinguishing cancer driver genes remains a major challenge. Numerous methods have been developed to identify driver genes, but evaluation of the performance of these methods is hindered by the lack of a gold standard, that is, bona fide driver gene mutations. Here, we establish an evaluation framework that can be applied to driver gene prediction methods. We used this framework to compare the performance of eight such methods. One of these methods, described here, incorporated a machine-learning–based ratiometric approach. We show that the driver genes predicted by each of the eight methods vary widely. Moreover, the P values reported by several of the methods were inconsistent with the uniform values expected, thus calling into question the assumptions that were used to generate them. Finally, we evaluated the potential effects of unexplained variability in mutation rates on false-positive driver gene predictions. Our analysis points to the strengths and weaknesses of each of the currently available methods and offers guidance for improving them in the future.

scholarly journals Identifying potential cancer driver genes by genomic data integration

2013 ◽  
Vol 3 (1) ◽  
Author(s):  
Yong Chen ◽  
Jingjing Hao ◽  
Wei Jiang ◽  
Tong He ◽  
Xuegong Zhang ◽  
...  
Keyword(s):  
Data Integration ◽  
Genomic Data ◽  
Driver Genes ◽  
Cancer Driver ◽  
Genomic Data Integration ◽  
Cancer Driver Genes ◽  
Potential Cancer

scholarly journals driveR: a novel method for prioritizing cancer driver genes using somatic genomics data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ege Ülgen ◽  
O. Uğur Sezerman
Keyword(s):  
Biological Knowledge ◽  
Driver Gene ◽  
Driver Genes ◽  
Cancer Driver ◽  
Prior Biological Knowledge ◽  
Wilcoxon Rank Sum Test ◽  
Cancer Genomes ◽  
Novel Method ◽  
Cancer Driver Genes ◽  
Batch Analysis

Abstract Background Cancer develops due to “driver” alterations. Numerous approaches exist for predicting cancer drivers from cohort-scale genomics data. However, methods for personalized analysis of driver genes are underdeveloped. In this study, we developed a novel personalized/batch analysis approach for driver gene prioritization utilizing somatic genomics data, called driveR. Results Combining genomics information and prior biological knowledge, driveR accurately prioritizes cancer driver genes via a multi-task learning model. Testing on 28 different datasets, this study demonstrates that driveR performs adequately, achieving a median AUC of 0.684 (range 0.651–0.861) on the 28 batch analysis test datasets, and a median AUC of 0.773 (range 0–1) on the 5157 personalized analysis test samples. Moreover, it outperforms existing approaches, achieving a significantly higher median AUC than all of MutSigCV (Wilcoxon rank-sum test p < 0.001), DriverNet (p < 0.001), OncodriveFML (p < 0.001) and MutPanning (p < 0.001) on batch analysis test datasets, and a significantly higher median AUC than DawnRank (p < 0.001) and PRODIGY (p < 0.001) on personalized analysis datasets. Conclusions This study demonstrates that the proposed method is an accurate and easy-to-utilize approach for prioritizing driver genes in cancer genomes in personalized or batch analyses. driveR is available on CRAN: https://cran.r-project.org/package=driveR.

scholarly journals Module Analysis Captures Pancancer Genetically and Epigenetically Deregulated Cancer Driver Genes for Smoking and Antiviral Response

EBioMedicine ◽  
2018 ◽  
Vol 27 ◽  
pp. 156-166 ◽  
Author(s):  
Magali Champion ◽  
Kevin Brennan ◽  
Tom Croonenborghs ◽  
Andrew J. Gentles ◽  
Nathalie Pochet ◽  
...  
Keyword(s):  
Antiviral Response ◽  
Driver Genes ◽  
Cancer Driver ◽  
Module Analysis ◽  
Cancer Driver Genes
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