DriverFinder: A Gene Length-Based Network Method to Identify Cancer Driver Genes

Wei, Pi-Jing; Zhang, Di; Li, Haitao; Xia, Junfeng; Zheng, Chun-Hou

doi:10.1155/2017/4826206

Cited by 11 publications

(8 citation statements)

References 51 publications

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“…The feature importance based on the mean decrease in accuracy shows that the most important feature on a gene-level is the gene length. The current findings appear to be well supported by the DriverFinder study, which states that variants tend to occur more in longer genes [49], yet this study is concentrated on driver genes and not driver mutations. Therefore, there is a high chance of including false positive driver mutations during the extraction phase based on driver genes, which are regarded as being a driver for the reason that they are inside a driver gene [8].…”

Section: Discussionsupporting

confidence: 80%

Identifying Cancer Drivers Using DRIVE: A Feature-Based Machine Learning Model for a Pan-Cancer Assessment of Somatic Missense Mutations

Dragomir

Akbar

Cassidy

et al. 2021

Cancers

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Sporadic cancer develops from the accrual of somatic mutations. Out of all small-scale somatic aberrations in coding regions, 95% are base substitutions, with 90% being missense mutations. While multiple studies focused on the importance of this mutation type, a machine learning method based on the number of protein–protein interactions (PPIs) has not been fully explored. This study aims to develop an improved computational method for driver identification, validation and evaluation (DRIVE), which is compared to other methods for assessing its performance. DRIVE aims at distinguishing between driver and passenger mutations using a feature-based learning approach comprising two levels of biological classification for a pan-cancer assessment of somatic mutations. Gene-level features include the maximum number of protein–protein interactions, the biological process and the type of post-translational modifications (PTMs) while mutation-level features are based on pathogenicity scores. Multiple supervised classification algorithms were trained on Genomics Evidence Neoplasia Information Exchange (GENIE) project data and then tested on an independent dataset from The Cancer Genome Atlas (TCGA) study. Finally, the most powerful classifier using DRIVE was evaluated on a benchmark dataset, which showed a better overall performance compared to other state-of-the-art methodologies, however, considerable care must be taken due to the reduced size of the dataset. DRIVE outlines the outstanding potential that multiple levels of a feature-based learning model will play in the future of oncology-based precision medicine.

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Section: Discussionsupporting

confidence: 80%

Identifying Cancer Drivers Using DRIVE: A Feature-Based Machine Learning Model for a Pan-Cancer Assessment of Somatic Missense Mutations

Dragomir

Akbar

Cassidy

et al. 2021

Cancers

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“…This gold standard known cancer gene set includes 576 genes (July 2019) 1 . Many cancer studies use CGC genes as the benchmark for the evaluation ( Bashashati et al, 2012 ; Hou and Ma, 2014 ; Bertrand et al, 2015 ; Wei et al, 2017 ; Guo et al, 2018 ).…”

Section: Methodsmentioning

confidence: 99%

An Efficient and Easy-to-Use Network-Based Integrative Method of Multi-Omics Data for Cancer Genes Discovery

Wei

Luo

et al. 2021

Front. Genet.

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Identifying personalized driver genes is essential for discovering critical biomarkers and developing effective personalized therapies of cancers. However, few methods consider weights for different types of mutations and efficiently distinguish driver genes over a larger number of passenger genes. We propose MinNetRank (Minimum used for Network-based Ranking), a new method for prioritizing cancer genes that sets weights for different types of mutations, considers the incoming and outgoing degree of interaction network simultaneously, and uses minimum strategy to integrate multi-omics data. MinNetRank prioritizes cancer genes among multi-omics data for each sample. The sample-specific rankings of genes are then integrated into a population-level ranking. When evaluating the accuracy and robustness of prioritizing driver genes, our method almost always significantly outperforms other methods in terms of precision, F1 score, and partial area under the curve (AUC) on six cancer datasets. Importantly, MinNetRank is efficient in discovering novel driver genes. SP1 is selected as a candidate driver gene only by our method (ranked top three), and SP1 RNA and protein differential expression between tumor and normal samples are statistically significant in liver hepatocellular carcinoma. The top seven genes stratify patients into two subtypes exhibiting statistically significant survival differences in five cancer types. These top seven genes are associated with overall survival, as illustrated by previous researchers. MinNetRank can be very useful for identifying cancer driver genes, and these biologically relevant marker genes are associated with clinical outcome. The R package of MinNetRank is available at https://github.com/weitinging/MinNetRank.

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“…However, to date, no golden standard of cancer drivers exists, and the CGC stands as the most robust and comprehensive resource available. Thus, it serves as the main reference point that the majority of studies use to evaluate their predicted driver genes and method [50][51][52][53][54][55][56][57][58][59][60][61]. To our knowledge, a similar well-curated resource of cancer driver genes driven by methylation changes does not exist.…”

Section: Oncogenic Mediator Log2fcmentioning

confidence: 99%

Revealing cancer driver genes through integrative transcriptomic and epigenomic analyses with Moonlight

Nourbakhsh,

Zheng,

Noor

et al. 2024

Preprint

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Cancer involves dynamic changes caused by (epi)genetic alterations such as mutations or abnormal DNA methylation patterns which occur in cancer driver genes. These driver genes are divided into oncogenes and tumor suppressors depending on their function and mechanism of action. Discovering driver genes in different cancer (sub)types is important not only for increasing current understanding of carcinogenesis but also from prognostic and therapeutic perspectives. We have previously developed a framework called Moonlight which uses a systems biology multi-omics approach for prediction of driver genes. Here, we present further updates to Moonlight by incorporating a DNA methylation layer which provides epigenetic evidence for deregulated expression profiles of driver genes. To this end, we present a novel functionality called Gene Methylation Analysis (GMA) which investigates abnormal DNA methylation patterns to predict driver genes. This is achieved by integrating the tool EpiMix which is designed to detect such aberrant DNA methylation patterns in a cohort of patients and further couples these patterns with changes in gene expression. To showcase GMA, we applied it to three cancer (sub)types (basal-like breast cancer, lung adenocarcinoma, and thyroid carcinoma) where we discovered 33, 190, and 263 epigenetically driven genes, respectively. A subset of these driver genes had prognostic effects with expression levels significantly affecting survival of the patients. Moreover, a subset of the driver genes demonstrated therapeutic potential as drug targets. This study provides a framework for exploring the driving forces behind cancer and provides novel insights into the landscape of three cancer sub(types) by integrating gene expression and methylation data. Moonlight2R is available on GitHub (https://github.com/ELELAB/Moonlight2R) and BioCondcutor (https://bioconductor.org/packages/release/bioc/html/Moonlight2R.html) and the associated case studies presented in this study are available on GitHub (https://github.com/ELELAB/Moonlight2_GMA_case_studies) and OSF (https://osf.io/j4n8q/).

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DriverFinder: A Gene Length-Based Network Method to Identify Cancer Driver Genes

Cited by 11 publications

References 51 publications

Identifying Cancer Drivers Using DRIVE: A Feature-Based Machine Learning Model for a Pan-Cancer Assessment of Somatic Missense Mutations

Identifying Cancer Drivers Using DRIVE: A Feature-Based Machine Learning Model for a Pan-Cancer Assessment of Somatic Missense Mutations

An Efficient and Easy-to-Use Network-Based Integrative Method of Multi-Omics Data for Cancer Genes Discovery

Revealing cancer driver genes through integrative transcriptomic and epigenomic analyses with Moonlight

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