DiSNEP: a Disease-Specific gene Network Enhancement to improve Prioritizing candidate disease genes

2020 ◽  
Author(s):  
Peifeng Ruan ◽  
Shuang Wang
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Gene Network ◽  
Gene Interactions ◽  
Specific Gene ◽  
Comparison Methods ◽  
Cancer Types ◽  
Disease Associated Genes ◽  
Methylation Signal ◽  
Disease Specific

Abstract Biological network-based strategies are useful in prioritizing genes associated with diseases. Several comprehensive human gene networks such as STRING, GIANT and HumanNet were developed and used in network-assisted algorithms to identify disease-associated genes. However, none of these networks are disease-specific and may not accurately reflect gene interactions for a specific disease. Aiming to improve disease gene prioritization using networks, we propose a Disease-Specific Network Enhancement Prioritization (DiSNEP) framework. DiSNEP first enhances a comprehensive gene network specifically for a disease through a diffusion process on a gene–gene similarity matrix derived from disease omics data. The enhanced disease-specific gene network thus better reflects true gene interactions for the disease and may improve prioritizing disease-associated genes subsequently. In simulations, DiSNEP that uses an enhanced disease-specific network prioritizes more true signal genes than comparison methods using a general gene network or without prioritization. Applications to prioritize cancer-associated gene expression and DNA methylation signal genes for five cancer types from The Cancer Genome Atlas (TCGA) project suggest that more prioritized candidate genes by DiSNEP are cancer-related according to the DisGeNET database than those prioritized by the comparison methods, consistently across all five cancer types considered, and for both gene expression and DNA methylation signal genes.

scholarly journals Integrative Analysis Reveals Comprehensive Altered Metabolic Genes Linking with Tumor Epigenetics Modification in Pan-Cancer

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Yahui Shi ◽  
Jinfen Wei ◽  
Zixi Chen ◽  
Yuchen Yuan ◽  
Xingsong Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Histone Acetylation ◽  
Epigenetic Modification ◽  
Metabolic Reprogramming ◽  
The Cancer Genome Atlas ◽  
Altered Expression ◽  
Metabolic Genes ◽  
Cancer Types ◽  
Pan Cancer

Background. Cancer cells undergo various rewiring of metabolism and dysfunction of epigenetic modification to support their biosynthetic needs. Although the major features of metabolic reprogramming have been elucidated, the global metabolic genes linking epigenetics were overlooked in pan-cancer. Objectives. Identifying the critical metabolic signatures with differential expressions which contributes to the epigenetic alternations across cancer types is an urgent issue for providing the potential targets for cancer therapy. Method. The differential gene expression and DNA methylation were analyzed by using the 5726 samples data from the Cancer Genome Atlas (TCGA). Results. Firstly, we analyzed the differential expression of metabolic genes and found that cancer underwent overall metabolism reprogramming, which exhibited a similar expression trend with the data from the Gene Expression Omnibus (GEO) database. Secondly, the regulatory network of histone acetylation and DNA methylation according to altered expression of metabolism genes was summarized in our results. Then, the survival analysis showed that high expression of DNMT3B had a poorer overall survival in 5 cancer types. Integrative altered methylation and expression revealed specific genes influenced by DNMT3B through DNA methylation across cancers. These genes do not overlap across various cancer types and are involved in different function annotations depending on the tissues, which indicated DNMT3B might influence DNA methylation in tissue specificity. Conclusions. Our research clarifies some key metabolic genes, ACLY, SLC2A1, KAT2A, and DNMT3B, which are most disordered and indirectly contribute to the dysfunction of histone acetylation and DNA methylation in cancer. We also found some potential genes in different cancer types influenced by DNMT3B. Our study highlights possible epigenetic disorders resulting from the deregulation of metabolic genes in pan-cancer and provides potential therapy in the clinical treatment of human cancer.

scholarly journals Impact of Transposable Elements on Methylation and Gene Expression across Natural Accessions of Brachypodium distachyon

2020 ◽  
Vol 12 (11) ◽  
pp. 1994-2001 ◽  
Author(s):  
Michele Wyler ◽  
Christoph Stritt ◽  
Jean-Claude Walser ◽  
Célia Baroux ◽  
Anne C Roulin
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Transposable Elements ◽  
Brachypodium Distachyon ◽  
Large Fraction ◽  
Expression Patterns ◽  
Specific Gene ◽  
Differential Gene ◽  
The Relationship ◽  
Silent State

Abstract Transposable elements (TEs) constitute a large fraction of plant genomes and are mostly present in a transcriptionally silent state through repressive epigenetic modifications, such as DNA methylation. TE silencing is believed to influence the regulation of adjacent genes, possibly as DNA methylation spreads away from the TE. Whether this is a general principle or a context-dependent phenomenon is still under debate, pressing for studying the relationship between TEs, DNA methylation, and nearby gene expression in additional plant species. Here, we used the grass Brachypodium distachyon as a model and produced DNA methylation and transcriptome profiles for 11 natural accessions. In contrast to what is observed in Arabidopsis thaliana, we found that TEs have a limited impact on methylation spreading and that only few TE families are associated with a low expression of their adjacent genes. Interestingly, we found that a subset of TE insertion polymorphisms is associated with differential gene expression across accessions. Thus, although not having a global impact on gene expression, distinct TE insertions may contribute to specific gene expression patterns in B. distachyon.

Assaying disease-specific gene expression by CDNA micro-arrays

1997 ◽  
Vol 98 (2) ◽  
pp. 150
Author(s):  
M.L. Bittner ◽  
J. DeRisi ◽  
P.S. Meltzer ◽  
Y. Chen ◽  
L. Penland ◽  
...  
Keyword(s):  
Gene Expression ◽  
Specific Gene ◽  
Specific Gene Expression ◽  
Disease Specific

A Probabilistic Integrated Gene Network Model of Multiple Hematopoietic Lineage Specification

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 4767-4767
Author(s):  
Hai Fang ◽  
Kankan Wang ◽  
Ji Zhang
Keyword(s):  
Gene Expression ◽  
Gene Network ◽  
Expression Patterns ◽  
Biological Data ◽  
Specific Gene ◽  
Hematopoietic Stem ◽  
Protein Protein Interaction ◽  
Proteomic Data ◽  
Hematopoietic Lineage ◽  
Regulatory Analysis

Abstract Hematopoietic stem cells and their progenitor hierarchy are highly controlled by the underlying gene regulatory network. In past decades, great progress has been made in elucidating lineage-restricted transcription factors and lineage-specific gene expression patterns. With accumulation of genome-wide biological data, it is of great value to expand beyond mere transcriptional regulatory analysis to the systemic understanding. Here, we utilized a probabilistic integrated gene network for integrating heterogeneous functional genomic and proteomic data sources into predictive model of hematopoietic lineage diversification. We first constructed a naïve Bayesian network by incorporating disparate biological data, including time-series gene expression during re-dedifferentiation of leukemia along alternative paths into granulocyte or monocyte upon the treatment of differentiation-inducing agents, computationally generated transcription factor regulatory sites and microRNA targets, well-curated physical protein-protein interaction, and functional annotation data. The resultant network, coupled with binomial-based statistical analysis of the interplay between node properties and the network topology, predicted the specific hematopoietic lineage, generating testable hypotheses regarding their unified reprogramming principles. This study demonstrates the utility of the growing biological data in detailed elucidations of the orchestration of distinct hematopoietic cell fates.

Epigenetic Effects of IDH1/IDH2 Mutations

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. SCI-33-SCI-33 ◽  
Author(s):  
Ari M. Melnick ◽  
Ross L Levine ◽  
Maria E Figueroa ◽  
Craig B. Thompson ◽  
Omar Abdel-Wahab
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Stem Cells ◽  
Hematopoietic Stem Cells ◽  
Malignant Transformation ◽  
Covalent Modification ◽  
Specific Gene ◽  
Gene Promoters ◽  
Loss Of Function ◽  
Hematopoietic Stem

Abstract Abstract SCI-33 Epigenetic deregulation of gene expression through aberrant DNA methylation or histone modification plays an important role in the malignant transformation of hematopoietic cells. In particular, acute myeloid leukemias (AMLs) can be classified according to epigenetic signatures affecting DNA methylation or histone modifications affecting specific gene sets. Heterozygous somatic mutations in the loci encoding isocitrate dehydrogenase 1 and 2 (IDH1/2) occur in ∼20% of AMLs and are accompanied by global DNA hypermethylation and hypermethylation and silencing of a number of specific gene promoters. IDH1/2 mutations are almost completely mutually exclusive with somatic loss-of-function mutations in TET2, which hydroxylates methylcytosine (mCpG). DNA hydroxymethylation can function as an intermediate step in mCpG demethylation. TET2 mutant de novo AMLs also display global and promoter specific hypermethylation partially overlapping with IDH1/2 mutant cases. Mutations in the IDH1/2 loci result in a neomorphic enzyme that generates the aberrant oncometabolite 2-hydroxyglutarate (2HG) using α-ketoglutarate (αKG) as a substrate. 2HG can disrupt the activity of enzymes that use αKG as a cofactor, including TET2 and the jumonji family of histone demethylases. Expression of mutant IDH isoforms inhibits TET2 hydroxymethylation and jumonji histone demethylase functions. IDH and TET2 mutant AMLs accordingly exhibit reduced levels of hydroxymethylcytosine and a trend towards increased histone methylation. Mutant IDH or TET2 loss of function causes differentiation blockade and expansion of hematopoietic stem cells and TET2 knockout results in a myeloproliferative phenotype in mice. Hydroxymethylcytosine is in abundance in hematopoietic stem cells and displays specific distribution patterns, yet the function of this covalent modification is not fully understood. Recent data link TET2 with the function of cytosine deaminases as a pathway towards DNA demethylation, which has implications as well for B cell lymphomas and CML lymphoid blast crisis, which are linked with the actions of activation induced cytosine deaminase. Altogether, the available data implicate mutations in IDH1/2 and TET2 in promoting malignant transformation in several tissues, by disrupting epigenomics programming and altering gene expression patterning. Disclosures: Thompson: Agios Pharmaceuticals: Consultancy.

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.

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