Prediction of Disease Genes Based on Stage-Specific Gene Regulatory Networks in Breast Cancer

Breast cancer is one of the most common malignant tumors in women, which seriously endangers women’s health. Great advances have been made over the last decades, however, most studies predict driver genes of breast cancer using biological experiments and/or computational methods, regardless of stage information. In this study, we propose a computational framework to predict the disease genes of breast cancer based on stage-specific gene regulatory networks. Firstly, we screen out differentially expressed genes and hypomethylated/hypermethylated genes by comparing tumor samples with corresponding normal samples. Secondly, we construct three stage-specific gene regulatory networks by integrating RNA-seq profiles and TF-target pairs, and apply WGCNA to detect modules from these networks. Subsequently, we perform network topological analysis and gene set enrichment analysis. Finally, the key genes of specific modules for each stage are screened as candidate disease genes. We obtain seven stage-specific modules, and identify 20, 12, and 22 key genes for three stages, respectively. Furthermore, 55%, 83%, and 64% of the genes are associated with breast cancer, for example E2F2, E2F8, TPX2, BUB1, and CKAP2L. So it may be of great importance for further verification by cancer experts.

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Linking common and rare disease genetics through gene regulatory networks

10.1101/2021.10.21.21265342 ◽

2021 ◽

Author(s):

Olivier B. Bakker ◽

Annique Claringbould ◽

Harm-Jan Westra ◽

Henry H. Wiersma ◽

Floranne Boulogne ◽

...

Keyword(s):

Gene Regulatory Networks ◽

Regulatory Networks ◽

Association Studies ◽

Genetic Diseases ◽

Common Disease ◽

Disease Genes ◽

Mendelian Disease ◽

Genome Wide Association Studies ◽

Gene Regulatory ◽

Key Genes

Genetic variants identified through genome-wide association studies (GWAS) are typically non-coding and exert small regulatory effects on downstream genes, but which downstream genes are ultimately impacted and how they confer risk remains mostly unclear. Conversely, variants that cause rare Mendelian diseases are often coding and have a more direct impact on disease development. We demonstrate that common and rare genetic diseases can be linked by studying the gene regulatory networks impacted by common disease-associated variants. We implemented this in the 'Downstreamer' method and applied it to 44 GWAS traits and find that predicted downstream "key genes" are enriched with Mendelian disease genes, e.g. key genes for height are enriched for genes that cause skeletal abnormalities and Ehlers-Danlos syndromes. We find that 82% of these key genes are located outside of GWAS loci, suggesting that they result from complex trans regulation rather than being impacted by disease-associated variants in cis. Finally, we discuss the challenges in reconstructing gene regulatory networks and provide a roadmap to improve identification of these highly connected genes for common traits and diseases.

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Context-specific gene regulatory networks subdivide intrinsic subtypes of breast cancer

BMC Bioinformatics ◽

10.1186/1471-2105-12-s2-s3 ◽

2011 ◽

Vol 12 (Suppl 2) ◽

pp. S3 ◽

Cited By ~ 4

Author(s):

Sara Nasser ◽

Heather E Cunliffe ◽

Michael A Black ◽

Seungchan Kim

Keyword(s):

Breast Cancer ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Specific Gene ◽

Intrinsic Subtypes ◽

Gene Regulatory ◽

Context Specific

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Context-specific gene regulatory networks subdivide intrinsic subtypes of breast cancer

Proceedings of the ACM fourth international workshop on Data and text mining in biomedical informatics - DTMBIO '10 ◽

10.1145/1871871.1871875 ◽

2010 ◽

Author(s):

Sara Nasser ◽

Heather Cunliffe ◽

Michael A. Black ◽

Seungchan Kim

Keyword(s):

Breast Cancer ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Specific Gene ◽

Intrinsic Subtypes ◽

Gene Regulatory ◽

Context Specific

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KBoost: a new method to infer gene regulatory networks from gene expression data

Scientific Reports ◽

10.1038/s41598-021-94919-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Luis F. Iglesias-Martinez ◽

Barbara De Kegel ◽

Walter Kolch

Keyword(s):

Breast Cancer ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Bayesian Model Averaging ◽

Model Averaging ◽

R Package ◽

Breast Cancer Patients ◽

Sources Of Information ◽

Cancer Subtypes ◽

Gene Regulatory

AbstractReconstructing gene regulatory networks is crucial to understand biological processes and holds potential for developing personalized treatment. Yet, it is still an open problem as state-of-the-art algorithms are often not able to process large amounts of data within reasonable time. Furthermore, many of the existing methods predict numerous false positives and have limited capabilities to integrate other sources of information, such as previously known interactions. Here we introduce KBoost, an algorithm that uses kernel PCA regression, boosting and Bayesian model averaging for fast and accurate reconstruction of gene regulatory networks. We have benchmarked KBoost against other high performing algorithms using three different datasets. The results show that our method compares favorably to other methods across datasets. We have also applied KBoost to a large cohort of close to 2000 breast cancer patients and 24,000 genes in less than 2 h on standard hardware. Our results show that molecularly defined breast cancer subtypes also feature differences in their GRNs. An implementation of KBoost in the form of an R package is available at: https://github.com/Luisiglm/KBoost and as a Bioconductor software package.

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Adaptive NetworkProfiler for Identifying Cancer Characteristic-Specific Gene Regulatory Networks

Journal of Computational Biology ◽

10.1089/cmb.2017.0120 ◽

2018 ◽

Vol 25 (2) ◽

pp. 130-145 ◽

Cited By ~ 3

Author(s):

Heewon Park ◽

Teppei Shimamura ◽

Seiya Imoto ◽

Satoru Miyano

Keyword(s):

Gene Regulatory Networks ◽

Regulatory Networks ◽

Specific Gene ◽

Gene Regulatory

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Integrative multi-omics analyses identify cell-type disease genes and regulatory networks across schizophrenia and Alzheimer’s disease

10.1101/2020.06.11.147314 ◽

2020 ◽

Author(s):

Mufang Ying ◽

Peter Rehani ◽

Panagiotis Roussos ◽

Daifeng Wang

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Regulatory Elements ◽

Disease Genes ◽

Omics Data ◽

Cell Type ◽

Cellular Resolution ◽

Gene Regulatory

AbstractStrong phenotype-genotype associations have been reported across brain diseases. However, understanding underlying gene regulatory mechanisms remains challenging, especially at the cellular level. To address this, we integrated the multi-omics data at the cellular resolution of the human brain: cell-type chromatin interactions, epigenomics and single cell transcriptomics, and predicted cell-type gene regulatory networks linking transcription factors, distal regulatory elements and target genes (e.g., excitatory and inhibitory neurons, microglia, oligodendrocyte). Using these cell-type networks and disease risk variants, we further identified the cell-type disease genes and regulatory networks for schizophrenia and Alzheimer’s disease. The celltype regulatory elements (e.g., enhancers) in the networks were also found to be potential pleiotropic regulatory loci for a variety of diseases. Further enrichment analyses including gene ontology and KEGG pathways revealed potential novel cross-disease and disease-specific molecular functions, advancing knowledge on the interplays among genetic, transcriptional and epigenetic risks at the cellular resolution between neurodegenerative and neuropsychiatric diseases. Finally, we summarized our computational analyses as a general-purpose pipeline for predicting gene regulatory networks via multi-omics data.

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Inference of differential gene regulatory networks based on gene expression and genetic perturbation data

Bioinformatics ◽

10.1093/bioinformatics/btz529 ◽

2019 ◽

Vol 36 (1) ◽

pp. 197-204 ◽

Cited By ~ 2

Author(s):

Xin Zhou ◽

Xiaodong Cai

Keyword(s):

Gene Expression ◽

Gene Regulatory Networks ◽

Structural Equation ◽

Regulatory Networks ◽

Supplementary Information ◽

Specific Gene ◽

Joint Inference ◽

Perturbation Data ◽

Gene Regulatory ◽

The Difference

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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