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Prostate Cancer Research Center

Faculty of Medicine and Health Technology

Microscopy picture on prostate cancer

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Emmert-Streib Group:

Prostate Cancer Gene Regulatory Network Inferred from RNA-Seq Data

Prostate cancer is a complex disease that can only be understood by interrogating the regulatory elements among genes and proteins. In this paper we inferred a prostate cancer Gene Regulatory Network (GRN) from a gene expression data set of patient RNA-seq profiles obtained from The Cancer Genome Atlas (TCGA) database. Our network-based analysis provides a practical systems biology approach for revealing large-scale interactions of prostate cancer. This allows a close interpretation of biological activities in terms of the hallmarks of cancer.

Daniel Moore, Ricardo de Matos Simoes, Matthias Dehmer, Frank Emmert-Streib
Curr Genomics. 2019 Jan;20(1):38-48.

Prostate cancer is a complex disease that can only be understood by interrogating the regulatory elements among genes and proteins. In this paper we inferred a prostate cancer Gene Regulatory Network (GRN) from a gene expression data set of patient RNA-seq profiles obtained from The Cancer Genome Atlas (TCGA) database. Our network-based analysis provides a practical systems biology approach for revealing large-scale interactions of prostate cancer. This allows a close interpretation of biological activities in terms of the hallmarks of cancer. Daniel Moore, Ricardo de Matos Simoes, Matthias Dehmer, Frank Emmert-Streib Curr Genomics. 2019 Jan;20(1):38-48.

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Prostate cancer is a complex disease that can only be understood by interrogating the regulatory elements among genes and proteins. In this paper we inferred a prostate cancer Gene Regulatory Network (GRN) from a gene expression data set of patient RNA-seq profiles obtained from The Cancer Genome Atlas (TCGA) database. Our network-based analysis provides a practical systems biology approach for revealing large-scale interactions of prostate cancer. This allows a close interpretation of biological activities in terms of the hallmarks of cancer. Daniel Moore, Ricardo de Matos Simoes, Matthias Dehmer, Frank Emmert-Streib Curr Genomics. 2019 Jan;20(1):38-48.

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