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BMC Genomics. 2019 Jun 17;20(1):503. doi: 10.1186/s12864-019-5887-7.

Refinement of breast cancer molecular classification by miRNA expression profiles.

Author information

1
Division of Oncology, Department of Clinical Sciences Lund, Lund University, Medicon Village, Scheelevägen 2, 223 81, Lund, Sweden.
2
Clinical Pathology, Laboratory Medicine, Skåne University Hospital, Lund, Sweden.
3
German Cancer Research Center DKFZ, Division of Functional Genome Analysis, Heidelberg, Germany.
4
Division of Translational Cancer Research, Lund University, Lund, Sweden.
5
BioCARE, Strategic Cancer Research Program, Lund, Sweden.
6
Division of Oncology, Skåne University Hospital, Lund, Sweden.
7
Department of Surgery, Skåne University Hospital, Lund, Sweden.
8
Division of Oncology, Department of Clinical Sciences Lund, Lund University, Medicon Village, Scheelevägen 2, 223 81, Lund, Sweden. carlos.rovira@med.lu.se.
9
BioCARE, Strategic Cancer Research Program, Lund, Sweden. carlos.rovira@med.lu.se.

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.

KEYWORDS:

Breast cancer; Differential expression; LINC00478; Mir-4728; Molecular subtypes; Non-coding RNA; miR-99a/let-7c/miR-125b; microRNA

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