Evidence map›Paper›PMID 31015790›Full record

ArticleCurrent genomics2019

Prostate Cancer Gene Regulatory Network Inferred from RNA-Seq Data.

Daniel Moore, Ricardo de Matos Simoes, Matthias Dehmer, Frank Emmert-Streib

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Current genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Review
  2. Article
  3. Co-Targeting Plk1 and DNMT3a in Advanced Prostate Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2021
    Article
  4. Article
  5. Article
  6. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Daniel Moore1School of Pharmacy, Queen's University Belfast, Belfast, BT9 7BL, UK; 2Department of Medical Oncology, Dana-Farber Cancer Institute and Department of Medicine, Harvard Medical School, Boston, MA02115, USA; 3Department of Mechatronics and Biomedical Computer Science, University for Health Sciences, Medical Informatics and Technology, 6060 Hall in Tirol, Austria; 4College of Computer and Control Engineering, Nankai University, Tianjin, China; 5Institute for Intelligent Production, Faculty for Management, University of Applied Sciences Upper Austria, Steyr, Austria; 6Department of Signal Processing, Predictive Medicine and Data Analytics Laboratory, Tampere University of Technology, Tampere33720, Finland; 7Institute of Biosciences and Medical Technology, Tampere, Finland.
Ricardo de Matos Simoes1School of Pharmacy, Queen's University Belfast, Belfast, BT9 7BL, UK; 2Department of Medical Oncology, Dana-Farber Cancer Institute and Department of Medicine, Harvard Medical School, Boston, MA02115, USA; 3Department of Mechatronics and Biomedical Computer Science, University for Health Sciences, Medical Informatics and Technology, 6060 Hall in Tirol, Austria; 4College of Computer and Control Engineering, Nankai University, Tianjin, China; 5Institute for Intelligent Production, Faculty for Management, University of Applied Sciences Upper Austria, Steyr, Austria; 6Department of Signal Processing, Predictive Medicine and Data Analytics Laboratory, Tampere University of Technology, Tampere33720, Finland; 7Institute of Biosciences and Medical Technology, Tampere, Finland.
Matthias Dehmer1School of Pharmacy, Queen's University Belfast, Belfast, BT9 7BL, UK; 2Department of Medical Oncology, Dana-Farber Cancer Institute and Department of Medicine, Harvard Medical School, Boston, MA02115, USA; 3Department of Mechatronics and Biomedical Computer Science, University for Health Sciences, Medical Informatics and Technology, 6060 Hall in Tirol, Austria; 4College of Computer and Control Engineering, Nankai University, Tianjin, China; 5Institute for Intelligent Production, Faculty for Management, University of Applied Sciences Upper Austria, Steyr, Austria; 6Department of Signal Processing, Predictive Medicine and Data Analytics Laboratory, Tampere University of Technology, Tampere33720, Finland; 7Institute of Biosciences and Medical Technology, Tampere, Finland.
Frank Emmert-Streib1School of Pharmacy, Queen's University Belfast, Belfast, BT9 7BL, UK; 2Department of Medical Oncology, Dana-Farber Cancer Institute and Department of Medicine, Harvard Medical School, Boston, MA02115, USA; 3Department of Mechatronics and Biomedical Computer Science, University for Health Sciences, Medical Informatics and Technology, 6060 Hall in Tirol, Austria; 4College of Computer and Control Engineering, Nankai University, Tianjin, China; 5Institute for Intelligent Production, Faculty for Management, University of Applied Sciences Upper Austria, Steyr, Austria; 6Department of Signal Processing, Predictive Medicine and Data Analytics Laboratory, Tampere University of Technology, Tampere33720, Finland; 7Institute of Biosciences and Medical Technology, Tampere, Finland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCancer is a complex disease with a lucid etiology and in understanding the causation, we need to appreciate this complexity.

objectiveHere we are aiming to gain insights into the genetic associations of prostate cancer through a network-based systems approach using the BC3Net algorithm.

methodsSpecifically, 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.

resultsWe 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. Fur-thermore, we investigate the local landscape of prostate cancer genes and discuss pathological associa-tions that may be relevant in the development of new targeted cancer therapies.

conclusionOur 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.

Indexed as

Data scienceGene regulatory networkGenomicsNetwork inferencePrecision medicineProstate cancerSystems biology

Identifiers

PMID31015790
PMCPMC6446481

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.