Identification of cancer biomarkers of prognostic value using specific gene regulatory networks (GRN): a novel role of RAD51AP1 for ovarian and lung cancers

Abstract Motivation Gene regulatory networks (GRNs) of the same organism can be different under different conditions, although the overall network structure may be similar. Understanding the difference in GRNs under different conditions is important to understand condition-specific gene regulation. When gene expression and other relevant data under two different conditions are available, they can be used by an existing network inference algorithm to estimate two GRNs separately, and then to identify the difference between the two GRNs. However, such an approach does not exploit the similarity in two GRNs, and may sacrifice inference accuracy. Results In this paper, we model GRNs with the structural equation model (SEM) that can integrate gene expression and genetic perturbation data, and develop an algorithm named fused sparse SEM (FSSEM), to jointly infer GRNs under two conditions, and then to identify difference of the two GRNs. Computer simulations demonstrate that the FSSEM algorithm outperforms the approaches that estimate two GRNs separately. Analysis of a dataset of lung cancer and another dataset of gastric cancer with FSSEM inferred differential GRNs in cancer versus normal tissues, whose genes with largest network degrees have been reported to be implicated in tumorigenesis. The FSSEM algorithm provides a valuable tool for joint inference of two GRNs and identification of the differential GRN under two conditions. Availability and implementation The R package fssemR implementing the FSSEM algorithm is available at https://github.com/Ivis4ml/fssemR.git. It is also available on CRAN. Supplementary information Supplementary data are available at Bioinformatics online.

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Comparison of gene regulatory networks to identify pathogenic genes for lymphoma

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720020500298 ◽

2020 ◽

Vol 18 (05) ◽

pp. 2050029

Author(s):

Xiao Yu ◽

Tongfeng Weng ◽

Changgui Gu ◽

Huijie Yang

Keyword(s):

Retinoic Acid ◽

Environmental Factors ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Retinoic Acid Receptor ◽

Specific Gene ◽

Acid Receptor ◽

Cancer Pathways ◽

Pathogenic Genes ◽

Gene Regulatory

Lymphoma is the most complicated cancer that can be divided into several tens of subtypes. It may occur in any part of body that has lymphocytes, and is closely correlated with diverse environmental factors such as the ionizing radiation, chemocarcinogenesis, and virus infection. All the environmental factors affect the lymphoma through genes. Identifying pathogenic genes for lymphoma is consequently an essential task to understand its complexity in a unified framework. In this paper, we propose a new method to expose high-confident edges in gene regulatory networks (GRNs) for a total of 32 organs, called Filtered GRNs (f-GRNs), comparison of which gives us a proper reference for the Lymphoma, i.e. the B-lymphocytes cells, whose f-GRN is closest with that for the Lymphoma. By using the Gene Ontology and Biological Process analysis we display the differences of the two networks’ hubs in biological functions. Matching with the Genecards shows that most of the hubs take part in the genetic information transmission and expression, except a specific gene of Retinoic Acid Receptor Alpha (RARA) that encodes the retinoic acid receptor. In the lymphoma, the genes in the RARA ego-network are involved in two cancer pathways, and the RARA is present only in these cancer pathways. For the lymphoid B cells, however, the genes in the RARA ego-network do not participate in cancer-related pathways.

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Role of motifs in topological robustness of gene regulatory networks

2017 IEEE International Conference on Communications (ICC) ◽

10.1109/icc.2017.7997033 ◽

2017 ◽

Cited By ~ 6

Author(s):

Satyaki Roy ◽

Mayank Raj ◽

Preetam Ghosh ◽

Sajal K. Das

Keyword(s):

Gene Regulatory Networks ◽

Regulatory Networks ◽

Gene Regulatory ◽

Topological Robustness

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Identification of context-specific gene regulatory networks with GEMULA—gene expression modeling using LAsso

Bioinformatics ◽

10.1093/bioinformatics/btr641 ◽

2011 ◽

Vol 28 (2) ◽

pp. 214-221 ◽

Cited By ~ 26

Author(s):

Geert Geeven ◽

Ronald E. van Kesteren ◽

August B. Smit ◽

Mathisca C. M. de Gunst

Keyword(s):

Gene Expression ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Specific Gene ◽

Gene Regulatory ◽

Context Specific

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The role of gene regulatory networks in promoting cancer progression and metastasis

Future Oncology ◽

10.2217/fon.13.264 ◽

2014 ◽

Vol 10 (5) ◽

pp. 735-748 ◽

Cited By ~ 9

Author(s):

Caroline Coghlin ◽

Graeme I Murray

Keyword(s):

Cancer Progression ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Gene Regulatory

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Predicting genotype-specific gene regulatory networks

10.1101/2021.01.18.427134 ◽

2021 ◽

Author(s):

Deborah Weighill ◽

Marouen Ben Guebila ◽

Kimberly Glass ◽

John Quackenbush ◽

John Platig

Keyword(s):

Gene Expression ◽

Gene Regulatory Network ◽

Gene Regulatory Networks ◽

Genetic Variants ◽

Regulatory Network ◽

Regulatory Networks ◽

Specific Gene ◽

Disease Etiology ◽

Gene Regulatory ◽

The Impact

AbstractThe majority of disease-associated genetic variants are thought to have regulatory effects, including the disruption of transcription factor (TF) binding and the alteration of downstream gene expression. Identifying how a person’s genotype affects their individual gene regulatory network has the potential to provide important insights into disease etiology and to enable improved genotype-specific disease risk assessments and treatments. However, the impact of genetic variants is generally not considered when constructing gene regulatory networks. To address this unmet need, we developed EGRET (Estimating the Genetic Regulatory Effect on TFs), which infers a genotype-specific gene regulatory network (GRN) for each individual in a study population by using message passing to integrate genotype-informed TF motif predictions - derived from individual genotype data, the predicted effects of variants on TF binding and gene expression, and TF motif predictions - with TF protein-protein interactions and gene expression. Comparing EGRET networks for two blood-derived cell lines identified genotype-associated cell-line specific regulatory differences which were subsequently validated using allele-specific expression, chromatin accessibility QTLs, and differential TF binding from ChIP-seq. In addition, EGRET GRNs for three cell types across 119 individuals captured regulatory differences associated with disease in a cell-type-specific manner. Our analyses demonstrate that EGRET networks can capture the impact of genetic variants on complex phenotypes, supporting a novel fine-scale stratification of individuals based on their genetic background. EGRET is available through the Network Zoo R package (netZooR v0.9; netzoo.github.io).

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Expression pattern determines regulatory logic

PLoS ONE ◽

10.1371/journal.pone.0244864 ◽

2021 ◽

Vol 16 (1) ◽

pp. e0244864

Author(s):

Carlos Mora-Martinez

Keyword(s):

Gene Expression ◽

Gene Regulatory Networks ◽

Dna Sequences ◽

In Silico ◽

Regulatory Networks ◽

Expression Patterns ◽

Cell Types ◽

Evolutionary Strategy ◽

Specific Gene ◽

Gene Regulatory

Large amounts of effort have been invested in trying to understand how a single genome is able to specify the identity of hundreds of cell types. Inspired by some aspects of Caenorhabditis elegans biology, we implemented an in silico evolutionary strategy to produce gene regulatory networks (GRNs) that drive cell-specific gene expression patterns, mimicking the process of terminal cell differentiation. Dynamics of the gene regulatory networks are governed by a thermodynamic model of gene expression, which uses DNA sequences and transcription factor degenerate position weight matrixes as input. In a version of the model, we included chromatin accessibility. Experimentally, it has been determined that cell-specific and broadly expressed genes are regulated differently. In our in silico evolved GRNs, broadly expressed genes are regulated very redundantly and the architecture of their cis-regulatory modules is different, in accordance to what has been found in C. elegans and also in other systems. Finally, we found differences in topological positions in GRNs between these two classes of genes, which help to explain why broadly expressed genes are so resilient to mutations. Overall, our results offer an explanatory hypothesis on why broadly expressed genes are regulated so redundantly compared to cell-specific genes, which can be extrapolated to phenomena such as ChIP-seq HOT regions.

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The Role of Gene Conversion between Transposable Elements in Rewiring Regulatory Networks

Genome Biology and Evolution ◽

10.1093/gbe/evz124 ◽

2019 ◽

Vol 11 (7) ◽

pp. 1723-1729

Author(s):

Jeffrey A Fawcett ◽

Hideki Innan

Keyword(s):

Transposable Elements ◽

Gene Conversion ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Alternative Mechanism ◽

Beneficial Mutations ◽

Future Studies ◽

Cellular Processes ◽

Gene Regulatory

Abstract Nature has found many ways to utilize transposable elements (TEs) throughout evolution. Many molecular and cellular processes depend on DNA-binding proteins recognizing hundreds or thousands of similar DNA motifs dispersed throughout the genome that are often provided by TEs. It has been suggested that TEs play an important role in the evolution of such systems, in particular, the rewiring of gene regulatory networks. One mechanism that can further enhance the rewiring of regulatory networks is nonallelic gene conversion between copies of TEs. Here, we will first review evidence for nonallelic gene conversion in TEs. Then, we will illustrate the benefits nonallelic gene conversion provides in rewiring regulatory networks. For instance, nonallelic gene conversion between TE copies offers an alternative mechanism to spread beneficial mutations that improve the network, it allows multiple mutations to be combined and transferred together, and it allows natural selection to work efficiently in spreading beneficial mutations and removing disadvantageous mutations. Future studies examining the role of nonallelic gene conversion in the evolution of TEs should help us to better understand how TEs have contributed to evolution.

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