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BMC Cancer. 2020 Feb 21;20(1):141. doi: 10.1186/s12885-020-6600-6.

Integrative analysis of breast cancer profiles in TCGA by TNBC subgrouping reveals novel microRNA-specific clusters, including miR-17-92a, distinguishing basal-like 1 and basal-like 2 TNBC subtypes.

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Department of Computer Science, Baylor University, Waco, TX, USA.
Instiute of Biomedical Studies, Baylor University, Waco, TX, USA.
Department of Biology, Baylor University, Waco, TX, USA.
Department of Computer and Telecommunication Engineering, Yonsei University, Wonju, South Korea.
Instiute of Biomedical Studies, Baylor University, Waco, TX, USA.
Department of Biology, Baylor University, Waco, TX, USA.



The term triple-negative breast cancer (TNBC) is used to describe breast cancers without expression of estrogen receptor, progesterone receptor or HER2 amplification. To advance targeted treatment options for TNBC, it is critical that the subtypes within this classification be described in regard to their characteristic biology and gene expression. The Cancer Genome Atlas (TCGA) dataset provides not only clinical and mRNA expression data but also expression data for microRNAs.


In this study, we applied the Lehmann classifier to TCGA-derived TNBC cases which also contained microRNA expression data and derived subtype-specific microRNA expression patterns. Subsequent analyses integrated known and predicted microRNA-mRNA regulatory nodes as well as patient survival data to identify key networks. Notably, basal-like 1 (BL1) TNBCs were distinguished from basal-like 2 TNBCs through up-regulation of members of the miR-17-92 cluster of microRNAs and suppression of several known miR-17-92 targets including inositol polyphosphate 4-phosphatase type II, INPP4B.


These data demonstrate TNBC subtype-specific microRNA and target mRNA expression which may be applied to future biomarker and therapeutic development studies.


Breast cancer; INPP4B; Molecular subtypes; TNBCtype; miR-17; microRNA

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