miRNome of inflammatory breast cancer

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Abstract

Background

Inflammatory breast cancer (IBC) is an extremely malignant form of breast cancer which can be easily misdiagnosed. Conclusive prognostic IBC molecular biomarkers which are also providing the perspectives for targeted therapy are lacking so far. The aim of this study was to reveal the IBC-specific miRNA expression profile and to evaluate its association with clinicopathological parameters.

Methods

miRNA expression profiles of 13 IBC and 17 non-IBC patients were characterized using comprehensive Affymetrix GeneChip miRNA 3.0 microarray platform. Bioinformatic analysis was used to reveal IBC-specific miRNAs, deregulated pathways and potential miRNA targets.

Results

31 differentially expressed miRNAs characterize IBC and mRNAs regulated by them and their associated pathways can functionally be attributed to IBC progression. In addition, a minimal predictive set of 4 miRNAs characteristic for the IBC phenotype and associated with the TP53 mutational status in breast cancer patients was identified.

Conclusions

We have characterized the complete miRNome of inflammatory breast cancer and found differentially expressed miRNAs which reliably classify the patients to IBC and non-IBC groups. We found that the mRNAs and pathways likely regulated by these miRNAs are highly relevant to cancer progression. Furthermore a minimal IBC-related predictive set of 4 miRNAs associated with the TP53 mutational status and survival for breast cancer patients was identified.

Electronic supplementary material

The online version of this article (doi:10.1186/1756-0500-7-871) contains supplementary material, which is available to authorized users.

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Most cited references 60

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miRecords: an integrated resource for microRNA–target interactions

Feifei Xiao, Zhixiang Zuo, Guoshuai Cai … (2009)

MicroRNAs (miRNAs) are an important class of small noncoding RNAs capable of regulating other genes’ expression. Much progress has been made in computational target prediction of miRNAs in recent years. More than 10 miRNA target prediction programs have been established, yet, the prediction of animal miRNA targets remains a challenging task. We have developed miRecords, an integrated resource for animal miRNA–target interactions. The Validated Targets component of this resource hosts a large, high-quality manually curated database of experimentally validated miRNA–target interactions with systematic documentation of experimental support for each interaction. The current release of this database includes 1135 records of validated miRNA–target interactions between 301 miRNAs and 902 target genes in seven animal species. The Predicted Targets component of miRecords stores predicted miRNA targets produced by 11 established miRNA target prediction programs. miRecords is expected to serve as a useful resource not only for experimental miRNA researchers, but also for informatics scientists developing the next-generation miRNA target prediction programs. The miRecords is available at http://miRecords.umn.edu/miRecords.

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miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions

Sheng-Da Hsu, Yu-Ting Tseng, Sirjana Devi Shrestha … (2013)

MicroRNAs (miRNAs) are small non-coding RNA molecules capable of negatively regulating gene expression to control many cellular mechanisms. The miRTarBase database (http://mirtarbase.mbc.nctu.edu.tw/) provides the most current and comprehensive information of experimentally validated miRNA-target interactions. The database was launched in 2010 with data sources for >100 published studies in the identification of miRNA targets, molecular networks of miRNA targets and systems biology, and the current release (2013, version 4) includes significant expansions and enhancements over the initial release (2010, version 1). This article reports the current status of and recent improvements to the database, including (i) a 14-fold increase to miRNA-target interaction entries, (ii) a miRNA-target network, (iii) expression profile of miRNA and its target gene, (iv) miRNA target-associated diseases and (v) additional utilities including an upgrade reminder and an error reporting/user feedback system.

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TP53 mutations in human cancers: functional selection and impact on cancer prognosis and outcomes.

A. Petitjean, M Achatz, A L Borresen-Dale … (2007)

A large amount of data is available on the functional impact of missense mutations in TP53 and on mutation patterns in many different cancers. New data on mutant p53 protein function, cancer phenotype and prognosis have recently been integrated in the International Agency for Research on Cancer TP53 database (http://www-p53.iarc.fr/). Based on these data, we summarize here current knowledge on the respective roles of mutagenesis and biological selection of mutations with specific functional characteristic in shaping the patterns and phenotypes of mutations observed in human cancers. The main conclusion is that intrinsic mutagenicity rates, loss of transactivation activities, and to a lesser extent, dominant-negative activities are the main driving forces that determine TP53 mutation patterns and influence tumor phenotype. In contrast, current experimental data on the acquisition of oncogenic activities (gain of function) by p53 mutants are too scarce and heterogenous to assess whether this property has an impact on tumor development and outcome. In the case of inherited TP53 mutations causing Li-Fraumeni and related syndromes, the age at onset of some tumor types is in direct relation with the degree of loss of transactivation capacity of missense mutations. Finally, studies on large case series demonstrate that TP53 mutations are independent markers of bad prognosis in breast and several other cancers, and that the exact type and position of the mutation influences disease outcome. Further studies are needed to determine how TP53 haplotypes or loss of alleles interact with mutations to modulate their impact on cancer development and prognosis.

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Author and article information

Contributors

Diana V Maltseva: d.maltseva@bioclinicum.com

Vladimir V Galatenko: vgalat@imscs.msu.ru

Timur R Samatov: t.samatov@bioclinicum.com

Svetlana O Zhikrivetskaya: mail@bioclinicum.com

Nadezhda A Khaustova: khaunadia@gmail.com

Ilya N Nechaev: ilyanech@gmail.com

Maxim U Shkurnikov: mshkurnikov@gmail.com

Alexey E Lebedev: alleb@list.ru

Irina A Mityakina: irina.mityakina@gmail.com

Andrey D Kaprin: mail@bioclinicum.com

Udo Schumacher: uschumac@uke.de

Alexander G Tonevitsky: tonevitsky@mail.ru

Journal

Journal ID (nlm-ta): BMC Res Notes

Journal ID (iso-abbrev): BMC Res Notes

Title: BMC Research Notes

Publisher: BioMed Central (London )

ISSN (Electronic): 1756-0500

Publication date (Electronic): 4 December 2014

Publication date Collection: 2014

Volume: 7

Issue: 1

Electronic Location Identifier: 871

Affiliations

[ ]SRC Bioclinicum, Ugreshskaya str 2/85, 115088 Moscow, Russia

[ ]Moscow State University, Leninskie Gory, 119991 Moscow, Russia

[ ]P.A. Hertsen Moscow Research Oncology Institute, 2nd Botkinskii p. 3, Moscow, 125284 Russia

[ ]Department of Anatomy and Experimental Morphology, University Cancer Center, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, D-20246 Germany

Article

Publisher ID: 3400

DOI: 10.1186/1756-0500-7-871

PMC ID: 4289319

PubMed ID: 25471792

SO-VID: 9290a1b1-74ea-4fc8-957a-7c36f347a463

License:

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 26 August 2014

Date accepted : 28 November 2014

Custom metadata

ScienceOpen disciplines: Medicine

Keywords: inflammatory breast cancer,mirna,microarray,tp53 mutational status

Data availability:

ScienceOpen disciplines: Medicine

Keywords: inflammatory breast cancer, mirna, microarray, tp53 mutational status

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miRNome of inflammatory breast cancer

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Abstract

Background

Methods

Results

Conclusions

Electronic supplementary material

Related collections

Biomarkers for Inflammatory Lung Disease

Most cited references 60

miRecords: an integrated resource for microRNA–target interactions

miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions

TP53 mutations in human cancers: functional selection and impact on cancer prognosis and outcomes.

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Cited by 11

Most referenced authors 1,486