scholarly journals RWCFusion: identifying phenotype-specific cancer driver gene fusions based on fusion pair random walk scoring method

Oncotarget ◽  
2016 ◽  
Vol 7 (38) ◽  
pp. 61054-61068 ◽  
Author(s):  
Jianmei Zhao ◽  
Xuecang Li ◽  
Qianlan Yao ◽  
Meng Li ◽  
Jian Zhang ◽  
...  
Keyword(s):  
Random Walk ◽  
Driver Gene ◽  
Gene Fusions ◽  
Scoring Method ◽  
Cancer Driver ◽  
Cancer Driver Gene ◽  
Specific Cancer

scholarly journals DriverDBv3: a multi-omics database for cancer driver gene research

2019 ◽  
Author(s):  
Shu-Hsuan Liu ◽  
Pei-Chun Shen ◽  
Chen-Yang Chen ◽  
An-Ni Hsu ◽  
Yi-Chun Cho ◽  
...  
Keyword(s):  
Cancer Biology ◽  
Synergistic Effects ◽  
Driver Gene ◽  
Driver Genes ◽  
Specific Patient ◽  
Mutation Status ◽  
Cancer Driver ◽  
Cancer Driver Gene ◽  
Significant Survival ◽  
Gene Database

Abstract An integrative multi-omics database is needed urgently, because focusing only on analysis of one-dimensional data falls far short of providing an understanding of cancer. Previously, we presented DriverDB, a cancer driver gene database that applies published bioinformatics algorithms to identify driver genes/mutations. The updated DriverDBv3 database (http://ngs.ym.edu.tw/driverdb) is designed to interpret cancer omics’ sophisticated information with concise data visualization. To offer diverse insights into molecular dysregulation/dysfunction events, we incorporated computational tools to define CNV and methylation drivers. Further, four new features, CNV, Methylation, Survival, and miRNA, allow users to explore the relations from two perspectives in the ‘Cancer’ and ‘Gene’ sections. The ‘Survival’ panel offers not only significant survival genes, but gene pairs synergistic effects determine. A fresh function, ‘Survival Analysis’ in ‘Customized-analysis,’ allows users to investigate the co-occurring events in user-defined gene(s) by mutation status or by expression in a specific patient group. Moreover, we redesigned the web interface and provided interactive figures to interpret cancer omics’ sophisticated information, and also constructed a Summary panel in the ‘Cancer’ and ‘Gene’ sections to visualize the features on multi-omics levels concisely. DriverDBv3 seeks to improve the study of integrative cancer omics data by identifying driver genes and contributes to cancer biology.

scholarly journals DriverDB: an exome sequencing database for cancer driver gene identification

2013 ◽  
Vol 42 (D1) ◽  
pp. D1048-D1054 ◽  
Author(s):  
Wei-Chung Cheng ◽  
I-Fang Chung ◽  
Chen-Yang Chen ◽  
Hsing-Jen Sun ◽  
Jun-Jeng Fen ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Gene Identification ◽  
Driver Gene ◽  
Cancer Driver ◽  
Cancer Driver Gene

Combining Mutation and Gene Network Data in a Machine Learning Approach for False-Positive Cancer Driver Gene Discovery

2020 ◽  
pp. 81-92
Author(s):  
Jorge Francisco Cutigi ◽  
Renato Feijo Evangelista ◽  
Rodrigo Henrique Ramos ◽  
Cynthia de Oliveira Lage Ferreira ◽  
Adriane Feijo Evangelista ◽  
...  
Keyword(s):  
Machine Learning ◽  
False Positive ◽  
Gene Network ◽  
Gene Discovery ◽  
Driver Gene ◽  
Network Data ◽  
Learning Approach ◽  
Cancer Driver ◽  
Cancer Driver Gene ◽  
Machine Learning Approach

scholarly journals Comprehensive analysis of the cancer driver genes in breast cancer demonstrates their roles in cancer prognosis and tumor microenvironment

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Xiao-wei Du ◽  
Gao Li ◽  
Juan Liu ◽  
Chun-yan Zhang ◽  
Qiong Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Tumor Microenvironment ◽  
Risk Group ◽  
Cancer Prognosis ◽  
Driver Gene ◽  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Driver Gene ◽  
Cancer Driver Genes ◽  
Patient Prognosis

Abstract Background Breast cancer is the most common malignancy in women. Cancer driver gene-mediated alterations in the tumor microenvironment are critical factors affecting the biological behavior of breast cancer. The purpose of this study was to identify the expression characteristics and prognostic value of cancer driver genes in breast cancer. Methods The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets are used as the training and test sets. Classified according to cancer and paracancerous tissues, we identified differentially expressed cancer driver genes. We further screened prognosis-associated genes, and candidate genes were submitted for the construction of a risk signature. Functional enrichment analysis and transcriptional regulatory networks were performed to search for possible mechanisms by which cancer driver genes affect breast cancer prognosis. Results We identified more than 200 differentially expressed driver genes and 27 prognosis-related genes. High-risk group patients had a lower survival rate compared to the low-risk group (P<0.05), and risk signature showed high specificity and sensitivity in predicting the patient prognosis (AUC 0.790). Multivariate regression analysis suggested that risk scores can independently predict patient prognosis. Further, we found differences in PD-1 expression, immune score, and stromal score among different risk groups. Conclusion Our study confirms the critical prognosis role of cancer driver genes in breast cancer. The cancer driver gene risk signature may provide a novel biomarker for clinical treatment strategy and survival prediction of breast cancer.

scholarly journals Rationale and Roadmap for Developing Panels of Hotspot Cancer Driver Gene Mutations as Biomarkers of Cancer Risk

2019 ◽  
Vol 61 (1) ◽  
pp. 152-175 ◽  
Author(s):  
Kelly L. Harris ◽  
Meagan B. Myers ◽  
Karen L. McKim ◽  
Rosalie K. Elespuru ◽  
Barbara L. Parsons
Keyword(s):  
Cancer Risk ◽  
Gene Mutations ◽  
Driver Gene ◽  
Cancer Driver ◽  
Cancer Driver Gene
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