scholarly journals MECoRank: cancer driver genes discovery simultaneously evaluating the impact of SNVs and differential expression on transcriptional networks

2019 ◽  
Vol 12 (S7) ◽  
Author(s):  
Ying Hui ◽  
Pi-Jing Wei ◽  
Junfeng Xia ◽  
Yu-Tian Wang ◽  
Chun-Hou Zheng
Keyword(s):  
Gene Expression ◽  
Gene List ◽  
Interaction Network ◽  
Transcriptional Network ◽  
Transcriptional Networks ◽  
Driver Genes ◽  
Cancer Driver ◽  
Novel Approach ◽  
Novel Method ◽  
The Impact

Abstract Background Although there are huge volumes of genomic data, how to decipher them and identify driver events is still a challenge. The current methods based on network typically use the relationship between genomic events and consequent changes in gene expression to nominate putative driver genes. But there may exist some relationships within the transcriptional network. Methods We developed MECoRank, a novel method that improves the recognition accuracy of driver genes. MECoRank is based on bipartite graph to propagates the scores via an iterative process. After iteration, we will obtain a ranked gene list for each patient sample. Then, we applied the Condorcet voting method to determine the most impactful drivers in a population. Results We applied MECoRank to three cancer datasets to reveal candidate driver genes which have a greater impact on gene expression. Experimental results show that our method not only can identify more driver genes that have been validated than other methods, but also can recognize some impactful novel genes which have been proved to be more important in literature. Conclusions We propose a novel approach named MECoRank to prioritize driver genes based on their impact on the expression in the molecular interaction network. This method not only assesses mutation’s effect on the transcriptional network, but also assesses the differential expression’s effect within the transcriptional network. And the results demonstrated that MECoRank has better performance than the other competing approaches in identifying driver genes.

scholarly journals Ranking cancer drivers via betweenness-based outlier detection and random walks

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Cesim Erten ◽  
Aissa Houdjedj ◽  
Hilal Kazan
Keyword(s):  
Cancer Genomics ◽  
Interaction Network ◽  
Molecular Data ◽  
Alternative Methods ◽  
Patient Specific ◽  
Cancer Genes ◽  
Driver Genes ◽  
Cancer Driver ◽  
Protein Protein Interaction ◽  
Genomic Studies

Abstract Background Recent cancer genomic studies have generated detailed molecular data on a large number of cancer patients. A key remaining problem in cancer genomics is the identification of driver genes. Results We propose BetweenNet, a computational approach that integrates genomic data with a protein-protein interaction network to identify cancer driver genes. BetweenNet utilizes a measure based on betweenness centrality on patient specific networks to identify the so-called outlier genes that correspond to dysregulated genes for each patient. Setting up the relationship between the mutated genes and the outliers through a bipartite graph, it employs a random-walk process on the graph, which provides the final prioritization of the mutated genes. We compare BetweenNet against state-of-the art cancer gene prioritization methods on lung, breast, and pan-cancer datasets. Conclusions Our evaluations show that BetweenNet is better at recovering known cancer genes based on multiple reference databases. Additionally, we show that the GO terms and the reference pathways enriched in BetweenNet ranked genes and those that are enriched in known cancer genes overlap significantly when compared to the overlaps achieved by the rankings of the alternative methods.

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 Identification of ovarian cancer driver genes by using module network integration of multi-omics data

2013 ◽  
Vol 3 (4) ◽  
pp. 20130013 ◽  
Author(s):  
Olivier Gevaert ◽  
Victor Villalobos ◽  
Branimir I. Sikic ◽  
Sylvia K. Plevritis
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Specific Gene ◽  
Omics Data ◽  
Driver Genes ◽  
Module Network ◽  
Cancer Driver ◽  
Specific Gene Expression ◽  
Cancer Drivers ◽  
Disease Specific

The increasing availability of multi-omics cancer datasets has created a new opportunity for data integration that promises a more comprehensive understanding of cancer. The challenge is to develop mathematical methods that allow the integration and extraction of knowledge from large datasets such as The Cancer Genome Atlas (TCGA). This has led to the development of a variety of omics profiles that are highly correlated with each other; however, it remains unknown which profile is the most meaningful and how to efficiently integrate different omics profiles. We developed AMARETTO, an algorithm to identify cancer drivers by integrating a variety of omics data from cancer and normal tissue. AMARETTO first models the effects of genomic/epigenomic data on disease-specific gene expression. AMARETTO's second step involves constructing a module network to connect the cancer drivers with their downstream targets. We observed that more gene expression variation can be explained when using disease-specific gene expression data. We applied AMARETTO to the ovarian cancer TCGA data and identified several cancer driver genes of interest, including novel genes in addition to known drivers of cancer. Finally, we showed that certain modules are predictive of good versus poor outcome, and the associated drivers were related to DNA repair pathways.

scholarly journals Transposable elements mediate genetic effects altering the expression of nearby genes in colorectal cancer

2021 ◽  
Author(s):  
Nikolaos Lykoskoufis ◽  
Evarist Planet ◽  
Halit Ongen ◽  
Didier Trono ◽  
Emmanouil T Dermitzakis
Keyword(s):  
Colorectal Cancer ◽  
Transposable Elements ◽  
Genetic Effect ◽  
Regulatory Sequences ◽  
Driver Genes ◽  
Cancer Driver ◽  
Tumor Tissues ◽  
Interspersed Repeats ◽  
Methylation Patterns ◽  
The Impact

Abstract Transposable elements (TEs) are interspersed repeats that contribute to more than half of the human genome, and TE-embedded regulatory sequences are increasingly recognized as major components of the human regulome. Perturbations of this system can contribute to tumorigenesis, but the impact of TEs on gene expression in cancer cells remains to be fully assessed. Here, we analyzed 275 normal colon and 276 colorectal cancer (CRC) samples from the SYSCOL colorectal cancer cohort and discovered 10,111 and 5,152 TE expression quantitative trait loci (eQTLs) in normal and tumor tissues, respectively. Amongst the latter, 376 were exclusive to CRC, likely driven by changes in methylation patterns. We identified that transcription factors are more enriched in tumor-specific TE-eQTLs than shared TE-eQTLs, indicating that TEs are more specifically regulated in tumor than normal. Using Bayesian Networks to assess the causal relationship between eQTL variants, TEs and genes, we identified that 1,758 TEs are mediators of genetic effect, altering the expression of 1,626 nearby genes significantly more in tumor compared to normal, of which 51 are cancer driver genes. We show that tumor-specific TE-eQTLs trigger the driver capability of TEs subsequently impacting expression of nearby genes. Collectively, our results highlight a global profile of a new class of cancer drivers, thereby enhancing our understanding of tumorigenesis and providing potential new candidate mechanisms for therapeutic target development.

scholarly journals Genomics of Viral Hepatitis-Associated Liver Tumors

2021 ◽  
Vol 10 (9) ◽  
pp. 1827
Author(s):  
Camille Péneau ◽  
Jessica Zucman-Rossi ◽  
Jean-Charles Nault
Keyword(s):  
Hepatitis B ◽  
Insertional Mutagenesis ◽  
Liver Tumors ◽  
Tumor Development ◽  
Driver Genes ◽  
Adeno Associated Virus ◽  
Cancer Driver ◽  
B Virus ◽  
The Impact

Virus-related liver carcinogenesis is one of the main contributors of cancer-related death worldwide mainly due to the impact of chronic hepatitis B and C infections. Three mechanisms have been proposed to explain the oncogenic properties of hepatitis B virus (HBV) infection: induction of chronic inflammation and cirrhosis, expression of HBV oncogenic proteins, and insertional mutagenesis into the genome of infected hepatocytes. Hepatitis B insertional mutagenesis modifies the function of cancer driver genes and could promote chromosomal instability. In contrast, hepatitis C virus promotes hepatocellular carcinoma (HCC) occurrence mainly through cirrhosis development whereas the direct oncogenic role of the virus in human remains debated. Finally, adeno associated virus type 2 (AAV2), a defective DNA virus, has been associated with occurrence of HCC harboring insertional mutagenesis of the virus. Since these tumors developed in a non-cirrhotic context and in the absence of a known etiological factor, AAV2 appears to be the direct cause of tumor development in these patients via a mechanism of insertional mutagenesis altering similar oncogenes and tumor suppressor genes targeted by HBV. A better understanding of virus-related oncogenesis will be helpful to develop new preventive strategies and therapies directed against specific alterations observed in virus-related HCC.

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.

scholarly journals NetMix: A network-structured mixture model for reduced-bias estimation of altered subnetworks

2020 ◽  
Author(s):  
Matthew A. Reyna ◽  
Uthsav Chitra ◽  
Rebecca Elyanow ◽  
Benjamin J. Raphael
Keyword(s):  
Probability Distributions ◽  
Gaussian Mixture Models ◽  
Interaction Network ◽  
Real Data ◽  
Gaussian Mixture ◽  
Differentially Expressed ◽  
Bias Estimation ◽  
Driver Genes ◽  
Cancer Driver ◽  
Classic Problem

AbstractA classic problem in computational biology is the identification of altered subnetworks: subnetworks of an interaction network that contain genes/proteins that are differentially expressed, highly mutated, or otherwise aberrant compared to other genes/proteins. Numerous methods have been developed to solve this problem under various assumptions, but the statistical properties of these methods are often unknown. For example, some widely-used methods are reported to output very large subnetworks that are difficult to interpret biologically. In this work, we formulate the identification of altered subnetworks as the problem of estimating the parameters of a class of probability distributions which we call the Altered Subset Distribution (ASD). We derive a connection between a popular method, jActiveModules, and the maximum likelihood estimator (MLE) of the ASD. We show that the MLE is statistically biased, explaining the large subnetworks output by jActiveModules. We introduce NetMix, an algorithm that uses Gaussian mixture models to obtain less biased estimates of the parameters of the ASD. We demonstrate that NetMix outperforms existing methods in identifying altered subnetworks on both simulated and real data, including the identification of differentially expressed genes from both microarray and RNA-seq experiments and the identification of cancer driver genes in somatic mutation data.AvailabilityNetMix is available online at https://github.com/raphael-group/[email protected]

scholarly journals Transkingdom network reveals bacterial players associated with cervical cancer gene expression program

2018 ◽  
Author(s):  
Khiem Chi Lam ◽  
Dariia Vyshenska ◽  
Jialu Hu ◽  
Richard Rosario Rodrigues ◽  
Anja Nilsen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cervical Cancer ◽  
Cancer Cell Line ◽  
Cervical Cancer Cell Line ◽  
Cancer Gene ◽  
Cervical Carcinogenesis ◽  
Driver Genes ◽  
Cancer Driver ◽  
Immune Genes ◽  
Chromosomal Amplifications

Cervical cancer is the fourth most common cancer in women worldwide with human papillomavirus (HPV) being the main cause of disease. Chromosomal amplifications have been identified as a source of upregulation of cervical cancer driver genes but cannot fully explain increased expression of immune genes in invasive carcinoma. Insight into additional factors that may tip the balance from making the immune system tolerate HPV to eliminate the virus may lead to markers for better diagnosis. We investigated whether microbiota affect molecular pathways in cervical carcinogenesis by performing microbiome analysis via sequencing 16S rRNA in tumor biopsies from 121 patients. While we detected a large number of intra-tumor taxa (289 OTUs), we focused on the thirty-eight most abundantly represented microbes. To search for microbes and host genes potentially involved in the interaction, we reconstructed a transkingdom network by integrating previously discovered cervical cancer gene expression network with our bacterial co-abundance network and employed bipartite betweenness centrality (BiBC). The top ranked microbes were represented by the families Bacillaceae, Halobacteriaceae, and Prevotellaceae. While we could not define the first two families to the species level, Prevotellaceae was assigned to Prevotella bivia. By co-culturing a cervical cancer cell line with P. bivia, we confirmed that three out of ten top predicted genes in the transkingdom network (LAMP3, STAT1, TAP1), all regulators of immunological pathways, were upregulated by this microorganism. Therefore, we propose that intra-tumor microbiota might contribute to cervical carcinogenesis through the induction of immune response drivers, including the well-known cancer gene LAMP3.

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 driveR: A Novel Method for Prioritizing Cancer Driver Genes Using Somatic Genomics Data

2020 ◽  
Author(s):  
Ege Ülgen ◽  
O. Uğur Sezerman
Keyword(s):  
Genomic Data ◽  
Biological Knowledge ◽  
Driver Gene ◽  
Driver Genes ◽  
Cancer Driver ◽  
Prior Biological Knowledge ◽  
Cancer Genomes ◽  
Novel Method ◽  
Cancer Drivers ◽  
Cancer Driver Genes

AbstractCancer develops due to “driver” alterations. Numerous approaches exist for predicting cancer drivers from cohort-scale genomic 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 genomic data, called driveR. Combining genomic 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, outperforms existing approaches, and is an accurate and easy-to-utilize approach for prioritizing driver genes in cancer genomes. driveR is available on CRAN: https://cran.r-project.org/package=driveR.

Sign in / Sign up

Export Citation Format

Share Document