Dataset Information

Refinement of breast cancer molecular classification by miRNA expression profiles.

ABSTRACT:

Background

Accurate classification of breast cancer using gene expression profiles has contributed to a better understanding of the biological mechanisms behind the disease and has paved the way for better prognostication and treatment prediction.

Results

We found that miRNA profiles largely recapitulate intrinsic subtypes. In the case of HER2-enriched tumors a small set of miRNAs including the HER2-encoded mir-4728 identifies the group with very high specificity. We also identified differential expression of the miR-99a/let-7c/miR-125b miRNA cluster as a marker for separation of the Luminal A and B subtypes. High expression of this miRNA cluster is linked to better overall survival among patients with Luminal A tumors. Correlation between the miRNA cluster and their precursor LINC00478 is highly significant suggesting that its expression could help improve the accuracy of present day's signatures.

Conclusions

We show here that miRNA expression can be translated into mRNA profiles and that the inclusion of miRNA information facilitates the molecular diagnosis of specific subtypes, in particular the clinically relevant sub-classification of luminal tumors.

SUBMITTER: Sokilde R

PROVIDER: S-EPMC6580620 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Publications

Refinement of breast cancer molecular classification by miRNA expression profiles.

Søkilde Rolf R Persson Helena H Ehinger Anna A Pirona Anna Chiara AC Fernö Mårten M Hegardt Cecilia C Larsson Christer C Loman Niklas N Malmberg Martin M Rydén Lisa L Saal Lao L Borg Åke Å Vallon-Christerson Johan J Rovira Carlos C

BMC genomics 20190617 1

<h4>Background</h4>Accurate classification of breast cancer using gene expression profiles has contributed to a better understanding of the biological mechanisms behind the disease and has paved the way for better prognostication and treatment prediction.<h4>Results</h4>We found that miRNA profiles largely recapitulate intrinsic subtypes. In the case of HER2-enriched tumors a small set of miRNAs including the HER2-encoded mir-4728 identifies the group with very high specificity. We also identifi ...[more]

PMID: 31208318

Similar Datasets

Project description:BackgroundMicroRNAs (miRNAs) are important key regulators in multiple cellular functions, due to their a crucial role in different physiological processes. MiRNAs are differentially expressed in specific tissues, during specific cell status, or in different diseases as tumours. RNA sequencing (RNA-seq) is a Next Generation Sequencing (NGS) method for the analysis of differential gene expression. Using machine learning algorithms, it is possible to improve the functional significance interpretation of miRNA in the analysis and interpretation of data from RNA-seq. Furthermore, we tried to identify some patterns of deregulated miRNA in human breast cancer (BC), in order to give a contribution in the understanding of this type of cancer at the molecular level.ResultsWe adopted a biclustering approach, using the Iterative Signature Algorithm (ISA) algorithm, in order to evaluate miRNA deregulation in the context of miRNA abundance and tissue heterogeneity. These are important elements to identify miRNAs that would be useful as prognostic and diagnostic markers. Considering a real word breast cancer dataset, the evaluation of miRNA differential expressions in tumours versus healthy tissues evidenced 12 different miRNA clusters, associated to specific groups of patients. The identified miRNAs were deregulated in breast tumours compared to healthy controls. Our approach has shown the association between specific sub-class of tumour samples having the same immuno-histo-chemical and/or histological features. Biclusters have been validated by means of two online repositories, MetaMirClust database and UCSC Genome Browser, and using another biclustering algorithm.ConclusionsThe obtained results with biclustering algorithm aimed first of all to give a contribute in the differential expression analysis in a cohort of BC patients and secondly to support the potential role that these non-coding RNA molecules could play in the clinical practice, in terms of prognosis, evolution of tumour and treatment response.

Dataset Information

Refinement of breast cancer molecular classification by miRNA expression profiles.

Background

Results

Conclusions

Publications

Refinement of breast cancer molecular classification by miRNA expression profiles.

Similar Datasets

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