Evidence map›Paper›PMID 28814324›Full record

ArticleBMC bioinformatics2017

Cancerouspdomains: comprehensive analysis of cancer type-specific recurrent somatic mutations in proteins and domains.

Seirana Hashemi, Abbas Nowzari Dalini, Adrin Jalali, Ali Mohammad Banaei-Moghaddam, Zahra Razaghi-Moghadam

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In one paragraph

Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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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.

Seirana HashemiCollege of Science, University of Tehran, Tehran, Iran.
Abbas Nowzari DaliniCollege of Science, University of Tehran, Tehran, Iran.
Adrin JalaliMax Planck Institute for Informatics, Saarland Informatics, Campus, 66123, Saarbrücken, Germany.
Ali Mohammad Banaei-MoghaddamInstitute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.
Zahra Razaghi-MoghadamFaculty of New Sciences and Technologies, University of Tehran, North Kargar St, Tehran, Tehran, 1439957131, Iran. razzaghi@ut.ac.ir.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDiscriminating driver mutations from the ones that play no role in cancer is a severe bottleneck in elucidating molecular mechanisms underlying cancer development. Since protein domains are representatives of functional regions within proteins, mutations on them may disturb the protein functionality. Therefore, studying mutations at domain level may point researchers to more accurate assessment of the functional impact of the mutations.

resultsThis article presents a comprehensive study to map mutations from 29 cancer types to both sequence- and structure-based domains. Statistical analysis was performed to identify candidate domains in which mutations occur with high statistical significance. For each cancer type, the corresponding type-specific domains were distinguished among all candidate domains. Subsequently, cancer type-specific domains facilitated the identification of specific proteins for each cancer type. Besides, performing interactome analysis on specific proteins of each cancer type showed high levels of interconnectivity among them, which implies their functional relationship. To evaluate the role of mitochondrial genes, stem cell-specific genes and DNA repair genes in cancer development, their mutation frequency was determined via further analysis.

conclusionsThis study has provided researchers with a publicly available data repository for studying both CATH and Pfam domain regions on protein-coding genes. Moreover, the associations between different groups of genes/domains and various cancer types have been clarified. The work is available at http://www.cancerouspdomains.ir .

Indexed as

Databases, GeneticDNA RepairHumansInternetMitochondriaMutationNeoplasmsNeoplastic Stem CellsProtein Interaction MapsProteinsUser-Computer InterfaceProteinsCancerCathPan-cancerPfamProtein domainSomatic mutationTCGA exome sequencing data

Identifiers

PMID28814324
PMCPMC5559820

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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.