Evidence map›Paper›PMID 32581643›Full record

ArticleCurrent genomics2019

Deciphering the Novel Target Genes Involved in the Epigenetics of Hepatocellular Carcinoma Using Graph Theory Approach.

Nimisha Roy, Utkarsh Raj, Sneha Rai, Pritish K Varadwaj

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PubMed Full text at PMC Publisher
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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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 39% of its field
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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
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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 at 3 institutions in 1 country.

Nimisha Roy1Department of Bioinformatics and Applied Sciences, Indian Institute of Information Technology, Allahabad, Uttar Pradesh, India; 2Department of Biotechnology and Bioinformatics, NIIT University, Neemrana, Rajasthan, India; 3Division of Biotechnology, Netaji Subhas Institute of Technology, University of Delhi, New Delhi, India.
Utkarsh Raj1Department of Bioinformatics and Applied Sciences, Indian Institute of Information Technology, Allahabad, Uttar Pradesh, India; 2Department of Biotechnology and Bioinformatics, NIIT University, Neemrana, Rajasthan, India; 3Division of Biotechnology, Netaji Subhas Institute of Technology, University of Delhi, New Delhi, India.
Sneha Rai1Department of Bioinformatics and Applied Sciences, Indian Institute of Information Technology, Allahabad, Uttar Pradesh, India; 2Department of Biotechnology and Bioinformatics, NIIT University, Neemrana, Rajasthan, India; 3Division of Biotechnology, Netaji Subhas Institute of Technology, University of Delhi, New Delhi, India.
Pritish K Varadwaj1Department of Bioinformatics and Applied Sciences, Indian Institute of Information Technology, Allahabad, Uttar Pradesh, India; 2Department of Biotechnology and Bioinformatics, NIIT University, Neemrana, Rajasthan, India; 3Division of Biotechnology, Netaji Subhas Institute of Technology, University of Delhi, New Delhi, India.
Indian Institute of Information Technology Allahabad · INNIIT (India) · INUniversity of Delhi · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEven after decades of research, cancer, by and large, remains a challenge and is one of the major causes of death worldwide. For a very long time, it was believed that cancer is simply an outcome of changes at the genetic level but today, it has become a well-established fact that both genetics and epigenetics work together resulting in the transformation of normal cells to cancerous cells.

objectiveIn the present scenario, researchers are focusing on targeting epigenetic machinery. The main advantage of targeting epigenetic mechanisms is their reversibility. Thus, cells can be reprogrammed to their normal state. Graph theory is a powerful gift of mathematics which allows us to understand complex networks. METHODOLOGY: In this study, graph theory was utilized for quantitative analysis of the epigenetic network of hepato-cellular carcinoma (HCC) and subsequently finding out the important vertices in the network thus obtained. Secondly, this network was utilized to locate novel targets for hepato-cellular carcinoma epigenetic therapy.

resultsThe vertices represent the genes involved in the epigenetic mechanism of HCC. Topological parameters like clustering coefficient, eccentricity, degree,

conclusionThe top ten novel epigenetic target genes involved in HCC reported in this study are cdk6, cdk4, cdkn2a, smad7, smad3, ccnd1, e2f1, sf3b1, ctnnb1, and tgfb1.

Indexed as

clustering coefficienteccentricityepigenomeGraph theoryhepatocellular carcinomastress centrality

Identifiers

PMID32581643
PMCPMC7290056
OpenAlexW2998231401

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.