Prostate cancer gene regulatory network inferred from RNA-seq data

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Prostate cancer gene regulatory network inferred from RNA-seq data. / Moore, Daniel; Simoes, Ricardo de Matos; Dehmer, Matthias; Emmert-Streib, Frank.

In: CURRENT GENOMICS, Vol. 20, No. 1, 2019, p. 38-48.

Research output: Contribution to journal › Article › Scientific › peer-review

Author

Moore, Daniel ; Simoes, Ricardo de Matos ; Dehmer, Matthias ; Emmert-Streib, Frank. / Prostate cancer gene regulatory network inferred from RNA-seq data. In: CURRENT GENOMICS. 2019 ; Vol. 20, No. 1. pp. 38-48.

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@article{f4ab7b87c02c406e8cf88cb09ba89a6e,

title = "Prostate cancer gene regulatory network inferred from RNA-seq data",

abstract = "Background: Cancer is a complex disease with a lucid etiology and in understanding the causation, we need to appreciate this complexity. Objective: Here we are aiming to gain insights into the genetic associations of prostate cancer through a network-based systems approach using the BC3Net algorithm. Methods: Specifically, we infer a prostate cancer Gene Regulatory Network (GRN) from a large-scale gene expression data set of 333 patient RNA-seq profiles obtained from The Cancer Genome Atlas (TCGA) database. Results: We analyze the functional components of the inferred network by extracting subnetworks based on biological process information and interpret the role of known cancer genes within each process. Furthermore, we investigate the local landscape of prostate cancer genes and discuss pathological associations that may be relevant in the development of new targeted cancer therapies. Conclusion: Our network-based analysis provides a practical systems biology approach to reveal the collective gene-interactions of prostate cancer. This allows a close interpretation of biological activity in terms of the hallmarks of cancer.",

keywords = "Data science, Gene regulatory network, Genomics, Network inference, Precision medicine, Prostate cancer, Systems biology",

author = "Daniel Moore and Simoes, {Ricardo de Matos} and Matthias Dehmer and Frank Emmert-Streib",

year = "2019",

doi = "10.2174/1389202919666181107122005",

language = "English",

volume = "20",

pages = "38--48",

journal = "CURRENT GENOMICS",

issn = "1389-2029",

publisher = "Bentham Science Publishers",

number = "1",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Prostate cancer gene regulatory network inferred from RNA-seq data

AU - Moore, Daniel

AU - Simoes, Ricardo de Matos

AU - Dehmer, Matthias

AU - Emmert-Streib, Frank

PY - 2019

Y1 - 2019

N2 - Background: Cancer is a complex disease with a lucid etiology and in understanding the causation, we need to appreciate this complexity. Objective: Here we are aiming to gain insights into the genetic associations of prostate cancer through a network-based systems approach using the BC3Net algorithm. Methods: Specifically, we infer a prostate cancer Gene Regulatory Network (GRN) from a large-scale gene expression data set of 333 patient RNA-seq profiles obtained from The Cancer Genome Atlas (TCGA) database. Results: We analyze the functional components of the inferred network by extracting subnetworks based on biological process information and interpret the role of known cancer genes within each process. Furthermore, we investigate the local landscape of prostate cancer genes and discuss pathological associations that may be relevant in the development of new targeted cancer therapies. Conclusion: Our network-based analysis provides a practical systems biology approach to reveal the collective gene-interactions of prostate cancer. This allows a close interpretation of biological activity in terms of the hallmarks of cancer.

AB - Background: Cancer is a complex disease with a lucid etiology and in understanding the causation, we need to appreciate this complexity. Objective: Here we are aiming to gain insights into the genetic associations of prostate cancer through a network-based systems approach using the BC3Net algorithm. Methods: Specifically, we infer a prostate cancer Gene Regulatory Network (GRN) from a large-scale gene expression data set of 333 patient RNA-seq profiles obtained from The Cancer Genome Atlas (TCGA) database. Results: We analyze the functional components of the inferred network by extracting subnetworks based on biological process information and interpret the role of known cancer genes within each process. Furthermore, we investigate the local landscape of prostate cancer genes and discuss pathological associations that may be relevant in the development of new targeted cancer therapies. Conclusion: Our network-based analysis provides a practical systems biology approach to reveal the collective gene-interactions of prostate cancer. This allows a close interpretation of biological activity in terms of the hallmarks of cancer.

KW - Data science

KW - Gene regulatory network

KW - Genomics

KW - Network inference

KW - Precision medicine

KW - Prostate cancer

KW - Systems biology

U2 - 10.2174/1389202919666181107122005

DO - 10.2174/1389202919666181107122005

M3 - Article

VL - 20

SP - 38

EP - 48

JO - CURRENT GENOMICS

JF - CURRENT GENOMICS

SN - 1389-2029

IS - 1

ER -

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