Evidence map›Paper›PMID 21143787›Full record

ArticleBMC genomics2010

A comparative study of cancer proteins in the human protein-protein interaction network.

Jingchun Sun, Zhongming Zhao

Open access · goldAbstract readComparative Study
In one paragraph

Article in BMC genomics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.

0numbers the graph read from it
0cells of the map it votes in
51citing papers in PubMed
4.5field-weighted citation impact, top 5% 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

51 citing papers in PubMed, 102 citations in OpenAlex.

  1. Trial
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  5. Computed cancer interactome explains the effects of somatic mutations in cancers.Protein science : a publication of the Protein Society · 2022
    Article
  6. Review
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  8. Characterization of the Survival Influential Genes in Carcinogenesis.International journal of molecular sciences · 2021
    Article
  9. Article
  10. Article
  11. Integrating molecular networks with genetic variant interpretation for precision medicine.Wiley interdisciplinary reviews. Systems biology and medicine · 2019
    Review
  12. Applications of molecular networks in biomedicine.Biology methods & protocols · 2019
    Review
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  14. Review
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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

2 authors at 1 institution in 1 country.

Jingchun SunDepartment of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA. jingchun.sun@vanderbilt.edu
Zhongming Zhao
Vanderbilt University · US

Funding

Generation of Ethanol Response Gene Resource by Large-Scale Data IntegrationR21AA017437 · NIAAA · VIRGINIA COMMONWEALTH UNIVERSITY · PI ZHAO, ZHONGMING · 2008 to 2009
$398k
Investigating CpG islands in mammalian genomesR03LM009598 · NLM · VIRGINIA COMMONWEALTH UNIVERSITY · PI ZHAO, ZHONGMING · 2008 to 2010
$191k
NIAAA NIH HHS R21AA017437NLM NIH HHS R03LM009598
6 · The paper itself

Abstract

backgroundCancer is a complex disease. So far, many genes have been reported to involve in the development of cancer. Rather than the traditional approach to studying individual genes or loci, a systematic investigation of cancer proteins in the human protein-protein interaction network may provide important biological information for uncovering the molecular mechanisms of cancer and, potentially, other complex diseases.

resultsWe explored global and local network characteristics of the proteins encoded by cancer genes (cancer proteins) in the human interactome. We found that the network topology of the cancer proteins was much different from that of the proteins encoded by essential genes (essential proteins) or control genes (control proteins). Relative to the essential proteins or control proteins, cancer proteins tended to have higher degree, higher betweenness, shorter shortest-path distance, and weaker clustering coefficient in the human interactome. We further separated the cancer proteins into two groups (recessive and dominant cancer proteins) and compared their topological features. Recessive cancer proteins had higher betweenness than dominant cancer proteins, while their degree distribution and characteristic shortest path distance were also significantly different. Finally, we found that cancer proteins were not randomly distributed in the human interactome and they connected strongly with each other.

conclusionOur study revealed much stronger protein-protein interaction characteristics of cancer proteins relative to the essential proteins or control proteins in the whole human interactome. We also found stronger network characteristics of recessive than dominant cancer proteins. The results are helpful for cancer candidate gene prioritization and verification, biomarker discovery, and, ultimately, understanding the etiology of cancer at the systems biological level.

Indexed as

Databases, GeneticHumansNeoplasm ProteinsNeoplasmsProtein Interaction MappingNeoplasm Proteins

Identifiers

PMID21143787
PMCPMC2999350
OpenAlexW2017274683

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.