scholarly journals Novel cancer subtyping method based on patient-specific gene regulatory network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mai Adachi Nakazawa ◽  
Yoshinori Tamada ◽  
Yoshihisa Tanaka ◽  
Marie Ikeguchi ◽  
Kako Higashihara ◽  
...  
Keyword(s):  
Gene Networks ◽  
Regulatory Networks ◽  
The Cancer Genome Atlas ◽  
Patient Specific ◽  
Specific Gene ◽  
Omics Data ◽  
Cancer Subtypes ◽  
Molecular Systems ◽  
Molecular Features ◽  
Identification Processes

AbstractThe identification of cancer subtypes is important for the understanding of tumor heterogeneity. In recent years, numerous computational methods have been proposed for this problem based on the multi-omics data of patients. It is widely accepted that different cancer subtypes are induced by different molecular regulatory networks. However, only a few incorporate the differences between their molecular systems into the identification processes. In this study, we present a novel method to identify cancer subtypes based on patient-specific molecular systems. Our method realizes this by quantifying patient-specific gene networks, which are estimated from their transcriptome data, and by clustering their quantified networks. Comprehensive analyses of The Cancer Genome Atlas (TCGA) datasets applied to our method confirmed that they were able to identify more clinically meaningful cancer subtypes than the existing subtypes and found that the identified subtypes comprised different molecular features. Our findings also show that the proposed method can identify the novel cancer subtypes even with single omics data, which cannot otherwise be captured by existing methods using multi-omics data.

scholarly journals Novel cancer subtyping method based on patient-specific gene regulatory network

2021 ◽  
Author(s):  
Mai Adachi Nakazawa ◽  
Yoshinori Tamada ◽  
Yoshihisa Tanaka ◽  
Marie Ikeguchi ◽  
Kako Higashihara ◽  
...  
Keyword(s):  
Gene Networks ◽  
Regulatory Networks ◽  
The Cancer Genome Atlas ◽  
Patient Specific ◽  
Specific Gene ◽  
Omics Data ◽  
Cancer Subtypes ◽  
Molecular Systems ◽  
Molecular Features ◽  
Cancer Genome Atlas

The identification of cancer subtypes is important for the understanding of tumor heterogeneity. In recent years, numerous computational methods have been proposed for this problem based on the multi-omics data of patients. It is widely accepted that different cancer subtypes are induced by different molecular regulatory networks. However, only a few incorporate the differences between their molecular systems into the classification processes. In this study, we present a novel method to classify cancer subtypes based on patient-specific molecular systems. Our method quantifies patient-specific gene networks, which are estimated from their transcriptome data. By clustering their quantified networks, our method allows for cancer subtyping, taking into consideration the differences in the molecular systems of patients. Comprehensive analyses of The Cancer Genome Atlas (TCGA) datasets applied to our method confirmed that they were able to identify more clinically meaningful cancer subtypes than the existing subtypes and found that the identified subtypes comprised different molecular features. Our findings show that the proposed method, based on a simple classification using the patient-specific molecular systems, can identify cancer subtypes even with single omics data, which cannot otherwise be captured by existing methods using multi-omics data.

scholarly journals Limit cycle dynamics can guide the evolution of gene regulatory networks towards point attractors

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Stuart P. Wilson ◽  
Sebastian S. James ◽  
Daniel J. Whiteley ◽  
Leah A. Krubitzer
Keyword(s):  
Gene Expression ◽  
Limit Cycles ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Expression Patterns ◽  
Specific Gene ◽  
Boolean Models ◽  
A Genome ◽  
Order Of Magnitude ◽  
Accelerate Evolution

AbstractDevelopmental dynamics in Boolean models of gene networks self-organize, either into point attractors (stable repeating patterns of gene expression) or limit cycles (stable repeating sequences of patterns), depending on the network interactions specified by a genome of evolvable bits. Genome specifications for dynamics that can map specific gene expression patterns in early development onto specific point attractor patterns in later development are essentially impossible to discover by chance mutation alone, even for small networks. We show that selection for approximate mappings, dynamically maintained in the states comprising limit cycles, can accelerate evolution by at least an order of magnitude. These results suggest that self-organizing dynamics that occur within lifetimes can, in principle, guide natural selection across lifetimes.

Machine learning-based analysis of multi-omics data on the cloud for investigating gene regulations

2020 ◽  
Author(s):  
Minsik Oh ◽  
Sungjoon Park ◽  
Sun Kim ◽  
Heejoon Chae
Keyword(s):  
Machine Learning ◽  
Gene Regulation ◽  
State Of The Art ◽  
Patient Specific ◽  
Specific Gene ◽  
Omics Data ◽  
Gene Expressions ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Disease Subtype

Abstract Gene expressions are subtly regulated by quantifiable measures of genetic molecules such as interaction with other genes, methylation, mutations, transcription factor and histone modifications. Integrative analysis of multi-omics data can help scientists understand the condition or patient-specific gene regulation mechanisms. However, analysis of multi-omics data is challenging since it requires not only the analysis of multiple omics data sets but also mining complex relations among different genetic molecules by using state-of-the-art machine learning methods. In addition, analysis of multi-omics data needs quite large computing infrastructure. Moreover, interpretation of the analysis results requires collaboration among many scientists, often requiring reperforming analysis from different perspectives. Many of the aforementioned technical issues can be nicely handled when machine learning tools are deployed on the cloud. In this survey article, we first survey machine learning methods that can be used for gene regulation study, and we categorize them according to five different goals: gene regulatory subnetwork discovery, disease subtype analysis, survival analysis, clinical prediction and visualization. We also summarize the methods in terms of multi-omics input types. Then, we explain why the cloud is potentially a good solution for the analysis of multi-omics data, followed by a survey of two state-of-the-art cloud systems, Galaxy and BioVLAB. Finally, we discuss important issues when the cloud is used for the analysis of multi-omics data for the gene regulation study.

scholarly journals Finding prognostic gene pairs for cancer from patient-specific gene networks

2019 ◽  
Vol 12 (S8) ◽  
Author(s):  
Byungkyu Park ◽  
Wook Lee ◽  
Inhee Park ◽  
Kyungsook Han
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Patient Specific ◽  
Specific Gene ◽  
Gene Signatures ◽  
Gene Pairs ◽  
Prognostic Gene ◽  
Gene Correlation

Abstract Background Molecular characterization of individual cancer patients is important because cancer is a complex and heterogeneous disease with many possible genetic and environmental causes. Many studies have been conducted to identify diagnostic or prognostic gene signatures for cancer from gene expression profiles. However, some gene signatures may fail to serve as diagnostic or prognostic biomarkers and gene signatures may not be found in gene expression profiles. Methods In this study, we developed a general method for constructing patient-specific gene correlation networks and for identifying prognostic gene pairs from the networks. A patient-specific gene correlation network was constructed by comparing a reference gene correlation network from normal samples to a network perturbed by a single patient sample. The main difference of our method from previous ones includes (1) it is focused on finding prognostic gene pairs rather than prognostic genes and (2) it can identify prognostic gene pairs from gene expression profiles even when no significant prognostic genes exist. Results Evaluation of our method with extensive data sets of three cancer types (breast invasive carcinoma, colon adenocarcinoma, and lung adenocarcinoma) showed that our approach is general and that gene pairs can serve as more reliable prognostic signatures for cancer than genes. Conclusions Our study revealed that prognosis of individual cancer patients is associated with the existence of prognostic gene pairs in the patient-specific network and the size of a subnetwork of the prognostic gene pairs in the patient-specific network. Although preliminary, our approach will be useful for finding gene pairs to predict survival time of patients and to tailor treatments to individual characteristics. The program for dynamically constructing patient-specific gene networks and for finding prognostic gene pairs is available at http://bclab.inha.ac.kr/pancancer.

scholarly journals Gene regulatory networks controlling vertebrate retinal regeneration

Science ◽  
2020 ◽  
Vol 370 (6519) ◽  
pp. eabb8598 ◽  
Author(s):  
Thanh Hoang ◽  
Jie Wang ◽  
Patrick Boyd ◽  
Fang Wang ◽  
Clayton Santiago ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Chromatin Accessibility ◽  
Specific Gene ◽  
Muller Glia ◽  
Müller Glia ◽  
Retinal Regeneration ◽  
Adult Mice ◽  
Gene Regulatory

Injury induces retinal Müller glia of certain cold-blooded vertebrates, but not those of mammals, to regenerate neurons. To identify gene regulatory networks that reprogram Müller glia into progenitor cells, we profiled changes in gene expression and chromatin accessibility in Müller glia from zebrafish, chick, and mice in response to different stimuli. We identified evolutionarily conserved and species-specific gene networks controlling glial quiescence, reactivity, and neurogenesis. In zebrafish and chick, the transition from quiescence to reactivity is essential for retinal regeneration, whereas in mice, a dedicated network suppresses neurogenic competence and restores quiescence. Disruption of nuclear factor I transcription factors, which maintain and restore quiescence, induces Müller glia to proliferate and generate neurons in adult mice after injury. These findings may aid in designing therapies to restore retinal neurons lost to degenerative diseases.

scholarly journals Lineage-Specific Epigenomic and Genomic Activation of the Oncogene HNF4A Promotes Gastrointestinal Adenocarcinomas

2019 ◽  
Author(s):  
Jian Pan ◽  
Tiago C. Silva ◽  
Nicole Gull ◽  
Qian Yang ◽  
Jasmine Plummer ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Large Scale ◽  
The Cancer Genome Atlas ◽  
Specific Gene ◽  
Distal Enhancer ◽  
Chromosome Conformation ◽  
Chip Sequencing ◽  
Specific Manner

AbstractBackgroundsGastrointestinal adenocarcinomas (GIACs) of the tubular GI tract including esophagus, stomach, colon and rectum comprise most GI cancers and share a spectrum of genomic features. However, the unified epigenomic changes specific to GIACs are less well-characterized.We applied mathematical algorithms to large-scale DNA methylome and transcriptome profiles to reconstruct transcription factor (TF) networks using 907 GIAC samples from The Cancer Genome Atlas (TCGA). Complementary epigenomic technologies were performed to investigate HNF4A activation, including Circularized Chromosome Conformation Capture (4C), Chromatin immunoprecipitation (ChIP) sequencing, Whole Genome Bisulfite Sequencing (WGBS), and Assay for Transposase-Accessible Chromatin (ATAC) sequencing. In vitro and in vivo cellular phenotypical assays were conducted to study HNF4A functions.ResultsWe identified a list of functionally hyperactive master regulator (MR)TFs shared across different GIACs. As the top candidate, HNF4A exhibited prominent genomic and epigenomic activation in a GIAC-specific manner. We further characterized a complex interplay between HNF4A promoter and three distal enhancer elements, which was coordinated by GIAC-specific MRTFs including ELF3, GATA4, GATA6 and KLF5. HNF4A also self-regulated its own promoter and enhancers. Functionally, HNF4A promoted cancer proliferation and survival by transcriptionally activating many downstream targets including HNF1A and factors of Interleukin signaling in a lineage-specific manner.ConclusionWe use a large cohort of patient samples and an unbiased mathematical approach to highlight lineage-specific oncogenic MRTFs, which provide new insights into the GIAC-specific gene regulatory networks, and identify potential therapeutic strategies against these common cancers.

scholarly journals MONTI: A Multi-Omics Non-negative Tensor Decomposition Framework for Gene-Level Integrative Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Inuk Jung ◽  
Minsu Kim ◽  
Sungmin Rhee ◽  
Sangsoo Lim ◽  
Sun Kim
Keyword(s):  
Breast Cancer ◽  
Case Studies ◽  
Tensor Decomposition ◽  
Integrative Analysis ◽  
Integrated Analysis ◽  
Molecular Characteristics ◽  
Specific Gene ◽  
Biological Traits ◽  
Omics Data ◽  
Cancer Subtypes

Multi-omics data is frequently measured to enrich the comprehension of biological mechanisms underlying certain phenotypes. However, due to the complex relations and high dimension of multi-omics data, it is difficult to associate omics features to certain biological traits of interest. For example, the clinically valuable breast cancer subtypes are well-defined at the molecular level, but are poorly classified using gene expression data. Here, we propose a multi-omics analysis method called MONTI (Multi-Omics Non-negative Tensor decomposition for Integrative analysis), which goal is to select multi-omics features that are able to represent trait specific characteristics. Here, we demonstrate the strength of multi-omics integrated analysis in terms of cancer subtyping. The multi-omics data are first integrated in a biologically meaningful manner to form a three dimensional tensor, which is then decomposed using a non-negative tensor decomposition method. From the result, MONTI selects highly informative subtype specific multi-omics features. MONTI was applied to three case studies of 597 breast cancer, 314 colon cancer, and 305 stomach cancer cohorts. For all the case studies, we found that the subtype classification accuracy significantly improved when utilizing all available multi-omics data. MONTI was able to detect subtype specific gene sets that showed to be strongly regulated by certain omics, from which correlation between omics types could be inferred. Furthermore, various clinical attributes of nine cancer types were analyzed using MONTI, which showed that some clinical attributes could be well explained using multi-omics data. We demonstrated that integrating multi-omics data in a gene centric manner improves detecting cancer subtype specific features and other clinical features, which may be used to further understand the molecular characteristics of interest. The software and data used in this study are available at: https://github.com/inukj/MONTI.

Abstract 386: Identification of Genetic Regulatory Networks for Insulin Resistance in Multiple Populations of Diverse Ethnicities

2017 ◽  
Vol 121 (suppl_1) ◽  
Author(s):  
Le Shu ◽  
Yuqi Zhao ◽  
Aldons J Lusis ◽  
Ke Hao ◽  
Thomas Quertermous ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Graphical Models ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Association Studies ◽  
Network Models ◽  
Mapk Signaling ◽  
Genetic Regulatory Networks ◽  
Specific Gene ◽  
Genome Wide Association Studies

Insulin resistance (IR) is a critical pathogenic factor for highly prevalent modern cardiometabolic diseases, including coronary artery disease (CAD) and type 2 diabetes (T2D). However, the molecular circuitries underlying IR remain to be elucidated. The GENEticS of Insulin Sensitivity Consortium (GENESIS) conducted genome-wide association studies (GWAS) for direct measures of IR using euglycemic clamp or insulin suppression test. We sought to identify gene networks and their key intervening drivers for IR by performing a comprehensive integrative analysis leveraging GWAS data from seven GENESIS cohorts representing three ethnic groups - Europeans, Asians and Hispanics, along with expression quantitative trait loci, ENCODE, and tissue-specific gene network models (both co-expression and graphical models) from IR relevant tissues. Integration of the multi-ethnic GWAS with diverse functional genomics information captured shared IR pathways and networks across ethnicities that are independent of body mass index, including GLUT4 translocation regulation, insulin signaling, MAPK signaling, interleukin signaling, extracellular matrix, branched-chain amino acids metabolisms, cell cycle, and oxidative phosphorylation. Further integration of these GWAS-informed IR processes with graphical gene networks uncovered potential key regulators including HADH, COX5A, VCAN and TOP2A , whose network neighbors are consistently enriched for the genetic association signals of IR across ethnicities, and show significant correlation with IR, fasting glucose and insulin levels in the transcriptomic-wide association data from a Hybrid Mouse Diversity Panel comprised of >100 strains fed with high-fat diet. Findings from this in-depth assessment of genetic and functional data from multiple human cohorts provide new understanding of the pathways, gene networks and potential regulators contributing to IR. These results will also facilitate future functional investigations to unveil how DNA variations translate into IR.

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