Cancer driver gene discovery in transcriptional regulatory networks using influence maximization approach

Rahimi, Majid; Teimourpour, Babak; Marashi, Sayed-Amir

doi:10.1016/j.compbiomed.2019.103362

Cited by 20 publications

(8 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Forty-four genes were recently annotated as CRC driver genes by Abdullah and Muhammad [ 16 ], where a computational approach was applied to identify CRC driver genes using a bioinformatics pipeline that consisted of the Cancer Genome Interpreter (CGI) [ 32 ] and Integrated Cancer Genome Score (iCAGES) [ 33 ] analysis platforms. Driver genes refer to genes whose mutations promote tumor growth [ 34 ], and further investigation of these genes is critical in precision oncology [ 35 ]. Table 6 lists the number of genes and driver genes for each patient.…”

Section: Resultsmentioning

confidence: 99%

TCGA-My: A Systematic Repository for Systems Biology of Malaysian Colorectal Cancer

Azuwar

Muhammad

Afiqah‐Aleng

et al. 2022

Life

View full text Add to dashboard Cite

Colorectal cancer (CRC) ranks second among the most commonly occurring cancers in Malaysia, and unfortunately, its pathobiology remains unknown. CRC pathobiology can be understood in detail with the implementation of omics technology that is able to generate vast amounts of molecular data. The generation of omics data has introduced a new challenge for data organization. Therefore, a knowledge-based repository, namely TCGA-My, was developed to systematically store and organize CRC omics data for Malaysian patients. TCGA-My stores the genome and metabolome of Malaysian CRC patients. The genome and metabolome datasets were organized using a Python module, pandas. The variants and metabolites were first annotated with their biological information using gene ontologies (GOs) vocabulary. The TCGA-My relational database was then built using HeidiSQL PorTable 9.4.0.512, and Laravel was used to design the web interface. Currently, TCGA-My stores 1,517,841 variants, 23,695 genes, and 167,451 metabolites from the samples of 50 CRC patients. Data entries can be accessed via search and browse menus. TCGA-My aims to offer effective and systematic omics data management, allowing it to become the main resource for Malaysian CRC research, particularly in the context of biomarker identification for precision medicine.

show abstract

Section: Resultsmentioning

confidence: 99%

TCGA-My: A Systematic Repository for Systems Biology of Malaysian Colorectal Cancer

Azuwar

Muhammad

Afiqah‐Aleng

et al. 2022

Life

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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/).

show abstract

“…DriverML was applied to 31 cancer mutation datasets from TCGA and compared with 20 other common tools as the benchmark. The research article [35] presents IMaxDriver framework for driver genes prediction. It is a network-based tool using the maximization algorithm on the human transcriptional regularity network.…”

Section: Literature Reviewmentioning

confidence: 99%

IDriveGenes: Cancer Driver Genes Prediction Using Machine Learning

et al. 2023

View full text Add to dashboard Cite

The development of high throughput sequencing technologies i.e. Next Generation Sequencing (NGS) is revolutionizing the exploration of cancer. Though sequence datasets are highly complex, mutation can occur randomly in DNA or RNA sequences that can make cells sicker or less fit. The unusual growth and behavior of genes in cells cause cancer. Cancer-driver gene cells grow when mutation occurs. Identification of cancer driver genes is a critical and challenging issue for researchers. In the proposed work, initially, robust features are extracted from the sequence dataset through Position Relative Incidence Matrix (PRIM) integrated with Accumulative Absolute Position Incidence Vector (AAPIV) generation. PRIM and AAPIV convert the single-dimensional sequence data into 2-dimensional numeric data. Support Vector Machine (SVM), Neural Network (NN), and Random Forest (RF) are used to train the model. The proposed model is validated with different validation methods i.e., independent testing, k-fold crossvalidation, self-consistency, and jackknife testing. The proposed model predicts whether the given primary structure corresponds to cancer driver genes or not. Results analyses show 95%, 92%, and 69% accuracy on RF, Artificial Neural Networks (ANN), and SVM respectively. The comparative analysis with existing state-of-the-art models i.e., 20/20+ and Multimodal Deep Neural Network by integrating Multi-dimensional Data (NDNNMD) shows that the proposed model outperforms the existing techniques.

show abstract

Cancer driver gene discovery in transcriptional regulatory networks using influence maximization approach

Cited by 20 publications

References 30 publications

TCGA-My: A Systematic Repository for Systems Biology of Malaysian Colorectal Cancer

TCGA-My: A Systematic Repository for Systems Biology of Malaysian Colorectal Cancer

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

IDriveGenes: Cancer Driver Genes Prediction Using Machine Learning

Contact Info

Product

Resources

About