Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2016 Mar 15;18(1):33.
doi: 10.1186/s13058-016-0690-8.

Comprehensive transcriptome analysis identifies novel molecular subtypes and subtype-specific RNAs of triple-negative breast cancer

Yi-Rong Liu  1   2   3 , Yi-Zhou Jiang  1   2   3 , Xiao-En Xu  1   2   3 , Ke-Da Yu  1   2   3 , Xi Jin  1   2   3 , Xin Hu  1   2   3 , Wen-Jia Zuo  1   2   3 , Shuang Hao  1   2   3 , Jiong Wu  1   2   3 , Guang-Yu Liu  1   2   3 , Gen-Hong Di  1   2   3 , Da-Qiang Li  1   2   3   4 , Xiang-Huo He  1   2   3   4 , Wei-Guo Hu  1   2   3   4 , Zhi-Ming Shao  5   6   7   8
Affiliations

Comprehensive transcriptome analysis identifies novel molecular subtypes and subtype-specific RNAs of triple-negative breast cancer

Yi-Rong Liu et al. Breast Cancer Res. .

Abstract

Background: Triple-negative breast cancer (TNBC) is a highly heterogeneous group of cancers, and molecular subtyping is necessary to better identify molecular-based therapies. While some classifiers have been established, no one has integrated the expression profiles of long noncoding RNAs (lncRNAs) into such subtyping criterions. Considering the emerging important role of lncRNAs in cellular processes, a novel classification integrating transcriptome profiles of both messenger RNA (mRNA) and lncRNA would help us better understand the heterogeneity of TNBC.

Methods: Using human transcriptome microarrays, we analyzed the transcriptome profiles of 165 TNBC samples. We used k-means clustering and empirical cumulative distribution function to determine optimal number of TNBC subtypes. Gene Ontology (GO) and pathway analyses were applied to determine the main function of the subtype-specific genes and pathways. We conducted co-expression network analyses to identify interactions between mRNAs and lncRNAs.

Results: All of the 165 TNBC tumors were classified into four distinct clusters, including an immunomodulatory subtype (IM), a luminal androgen receptor subtype (LAR), a mesenchymal-like subtype (MES) and a basal-like and immune suppressed (BLIS) subtype. The IM subtype had high expressions of immune cell signaling and cytokine signaling genes. The LAR subtype was characterized by androgen receptor signaling. The MES subtype was enriched with growth factor signaling pathways. The BLIS subtype was characterized by down-regulation of immune response genes, activation of cell cycle, and DNA repair. Patients in this subtype experienced worse recurrence-free survival than others (log rank test, P = 0.045). Subtype-specific lncRNAs were identified, and their possible biological functions were predicted using co-expression network analyses.

Conclusions: We developed a novel TNBC classification system integrating the expression profiles of both mRNAs and lncRNAs and determined subtype-specific lncRNAs that are potential biomarkers and targets. If further validated in a larger population, our novel classification system could facilitate patient counseling and individualize treatment of TNBC.

Keywords: Long non-coding RNA; Messenger RNA; Molecular subtypes; Transcriptome analysis; Triple-negative breast cancer.

PubMed Disclaimer

Figures

Fig. 1
Fig. 1
The identification of novel subtypes of triple-negative breast cancer. a Consensus clustering displaying the robustness of classification. b Consensus empirical cumulative distribution function (CDF) of all given cluster numbers. c Plot of delta area changes with number of clusters
Fig. 2
Fig. 2
A heat map shows the relative expression of the top differentially expressed RNAs (SD >0.65) in each subtype. Top Gene Ontology (GO) and canonical pathways of each subtype are shown (left). Upward-pointing arrow upregulated function, downward-pointing arrow downregulated function. FUSCC Fudan University Shanghai Cancer Center, IM immunomodulatory, LAR luminal androgen receptor, MES mesenchymal-like, BLIS basal-like and immune suppressed, BL basal-like, M claudin-low-enriched mesenchymal, MSL mesenchymal stem-like, ECM extracellular matrix, TGF transforming growth factor
Fig. 3
Fig. 3
Interaction analysis of the Lehmann/Pietenpol and Fudan University Shanghai Cancer Center (FUSCC) classifications. X-axis shows the subtypes of the new system. Circle size varies in proportion to the number of samples. MSL mesenchymal stem-like, LAR luminal androgen receptor, M claudin-low-enriched mesenchymal, IM immunomodulatory, BL basal-like, MES mesenchymal-like, BLIS basal-like and immune-suppressed
Fig. 4
Fig. 4
Kaplan-Meier plot and logrank test compared recurrence-free survival (RFS) in different subtypes according to the Fudan University Shanghai Cancer Center (FUSCC) classification. a Difference in RFS among four subtypes. b RFS in patients with the basal-like 1 (BL1) subtype compared to other subtypes. IM immunomodulatory, LAR luminal androgen receptor, MES mesenchymal-like, BLIS basal-like and immune-suppressed
Fig. 5
Fig. 5
Subtype-specific long noncoding RNAs (lncRNAs) and analysis of their co-expressed messenger RNAs (RNAs). Table shows details of the lncRNAs. The highest expression group was selected as the reference. Student’s t test, ***P <0.001. IM immunomodulatory, LAR luminal androgen receptor, MES mesenchymal-like, BLIS basal-like and immune-suppressed, MSL mesenchymal stem-like
See this image and copyright information in PMC

References

    1. Chi Y, Huang S, Yuan L, Liu M, Huang N, Zhou S, et al. Role of BC040587 as a predictor of poor outcome in breast cancer. Cancer Cell Int. 2014;14(1):123. doi: 10.1186/s12935-014-0123-7. - DOI - PMC - PubMed
    1. Liu B, Sun L, Liu Q, Gong C, Yao Y, Lv X, et al. A Cytoplasmic NF-ĸB Interacting long noncoding RNA blocks IĸB phosphorylation and suppresses breast cancer metastasis. Cancer Cell. 2015;27(3):370–381. doi: 10.1016/j.ccell.2015.02.004. - DOI - PubMed
    1. Shi Y, Li J, Liu Y, Ding J, Fan Y, Tian Y, et al. The long noncoding RNA SPRY4-IT1 increases the proliferation of human breast cancer cells by upregulating ZNF703 expression. Mol Cancer. 2015;14(1):51. doi: 10.1186/s12943-015-0318-0. - DOI - PMC - PubMed
    1. Trimarchi T, Bilal E, Ntziachristos P, Fabbri G, Dalla-Favera R, Tsirigos A, et al. Genome-wide mapping and characterization of Notch-regulated long noncoding RNAs in acute leukemia. Cell. 2014;158(3):593–606. doi: 10.1016/j.cell.2014.05.049. - DOI - PMC - PubMed
    1. Wang KC, Chang HY. Molecular mechanisms of long noncoding RNAs. Mol Cell. 2011;43(6):904–914. doi: 10.1016/j.molcel.2011.08.018. - DOI - PMC - PubMed

Publication types

MeSH terms