Evidence map›Paper›PMID 31712650›Full record

ArticleScientific reports2019

Identification of key regulators in prostate cancer from gene expression datasets of patients.

Irengbam Rocky Mangangcha, Md Zubbair Malik, Ömer Küçük, Shakir Ali, R K Brojen Singh

Abstract read
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. The role of Aha1 in cancer and neurodegeneration.Frontiers in molecular neuroscience · 2024
    Review
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. The TRIM proteins in cancer: from expression to emerging regulatory mechanisms.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2022
    Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Co-Targeting Plk1 and DNMT3a in Advanced Prostate Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2021
    Article
  16. Article
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

5 authors.

Irengbam Rocky MangangchaSchool of Interdisciplinary Sciences and Technology, Jamia Hamdard, New Delhi, 110062, India.
Md Zubbair MalikSchool of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, 110067, India.
Ömer KüçükWinship Cancer Institute of Emory University, 1365 Clifton Road NE, Atlanta, GA, 30322, USA.
Shakir AliSchool of Interdisciplinary Sciences and Technology, Jamia Hamdard, New Delhi, 110062, India. sali@jamiahamdard.ac.in.ORCID http://orcid.org/0000-0002-4002-1231
R K Brojen SinghSchool of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, 110067, India. brojen@jnu.ac.in.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Identification of key regulators and regulatory pathways is an important step in the discovery of genes involved in cancer. Here, we propose a method to identify key regulators in prostate cancer (PCa) from a network constructed from gene expression datasets of PCa patients. Overexpressed genes were identified using BioXpress, having a mutational status according to COSMIC, followed by the construction of PCa Interactome network using the curated genes. The topological parameters of the network exhibited power law nature indicating hierarchical scale-free properties and five levels of organization. Highest degree hubs (k ≥ 65) were selected from the PCa network, traced, and 19 of them was identified as novel key regulators, as they participated at all network levels serving as backbone. Of the 19 hubs, some have been reported in literature to be associated with PCa and other cancers. Based on participation coefficient values most of these are connector or kinless hubs suggesting significant roles in modular linkage. The observation of non-monotonicity in the rich club formation suggested the importance of intermediate hubs in network integration, and they may play crucial roles in network stabilization. The network was self-organized as evident from fractal nature in topological parameters of it and lacked a central control mechanism.

Indexed as

Biomarkers, TumorGene Expression Regulation, NeoplasticAlgorithmsComputational BiologyDatasets as TopicGene Expression ProfilingGene Regulatory NetworksHumansMaleProstatic NeoplasmsProtein Interaction MappingProtein Interaction MapsBiomarkers, Tumor

Identifiers

PMID31712650
PMCPMC6848149

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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.