Screening and identification of potential biomarkers in triple-negative breast cancer by integrated analysis

  • Authors:
    • Jilong Guo
    • Guohua Gong
    • Bin Zhang
  • View Affiliations

  • Published online on: August 21, 2017     https://doi.org/10.3892/or.2017.5911
  • Pages: 2219-2228
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Abstract

Triple-negative breast cancer (TNBC) has attracted great attention due to its unique biology, poor prognosis, and aggressiveness. TNBC patients are more likely to suffer from metastasis. We screened and identified the TNBC-specific genes as potential biomarkers. A total of 167 breast cancer samples (45 TNBC and 122 non-TNBC) were used in the integrated analysis. Gene expression microarrays were used to screen the differentially expressed genes. We identified 65 core DEGs. According to the GO and KEGG analysis, the gene function enrichment in TNBC was revealed, such as basal cell carcinoma, prostate cancer, oocyte meiosis and choline metabolism in cancer pathways. Moreover, the PPI network reconstruction would benefit the screening of hubs. A RFS analysis of TNBC-specific genes was also conducted. RT-PCR was used to validate the expression pattern of hubs in TNBC. Finally, nine genes were identified and all of them were novel, specific and higher dysregulation expressed genes in TNBC. Such that, these genes will serve as potential biomarkers in TNBC and benefit further research in TNBC.

Introduction

Breast cancer is one of the most common malignancies in women of United States, which results in >40,000 deaths every year. These breast tumors consist of phenotypically diverse populations (1). Breast cancer can be divided into subtypes of two estrogen receptor (ER)-positive (luminal A and luminal B types), human epidermal growth factor receptor 2 (HER2) enriched, and triple-negative breast cancer (TNBC) (2). Triple-negative breast cancer is ER-negative, PgR-negative, and HER2-negative using clinical assays, which accounts for approximately 15% of all types of breast cancer (3,4). Furthermore, TNBC has attracted a tremendous amount of attention due to its unique biology, overall poor prognosis, aggressive, and pattern of metastases (5). Therefore, it is urgent to gain insight into the therapeutic targets when compared with endocrine-sensitive and HER2-positive breast cancer.

Gene expression profiling could categorize the characteristics of different subtypes and verify the genes as novel therapeutic targets (6). A limited number of studies have been conducted on the gene expression profile of TNBC. Yang et al discovered that FZD7 plays a critical role in cell proliferation in TNBC (7). Their finding have suggested that several Wnt pathway genes, such as FZD7, low density lipoprotein receptor-related protein 6 and TCF7 are overexpressed in TNBC (7). In another study, Mathe et al identified the novel genes associated with the lymph node metastasis in TNBC. According to an analysis of 33 TNBCs, 17 normal adjacent tissues and 15 lymph node metastases were identified (8). Wang et al identified the CDK7-dependent transcriptional addiction in triple-negative breast cancer (9). Furthermore, Abramovitz et al identified a 30-biomarker gene set that could distinguish the breast cancer into subtypes. This study also uses the subset genes for prognostication of OS and RFS (10).

He et al conducted an analysis to study the molecular characteristics of triple-negative breast cancer using microarray (4). Al-Ejeh et al conducted a meta-analysis for the gene expression profile in TNBC and clarified the genes for prognostication and therapy. In this integrated analysis, the combination of clinical samples with different types of chemotherapy from some databases would increase the heterogeneity (10,11).

Although the gene expression profiles convey significant findings in TNBC, there is a lack of sufficient conclusions to uncover the central mechanisms in TNBC. This situation requires the integration of different datasets. In the present study, we first conducted an integrated analysis of TNBC relative to the non-TNBC. The differentially expressed genes were identified by statistical analysis. Then, the core DEGs were selected for functional annotation. The PPI was also reconstructed. These analyses were conducted to reveal the biological pathway and molecular mechanisms regarding TNBC. Finally, the selected hub genes would be the potential and specific biomarkers with prognostic value and used as the treatment targets in the future.

Materials and methods

Microarray data analysis

To identify the gene expression mode of TNBC, microarray data were collected from the Gene Expression Ominibus (GEO, https://www.ncbi.nlm.nih.gov/geo/), which is freely available for users. Three independent microarray databases (GSE27447, GSE61724, GSE18864) were downloaded. A total of 19 samples were studied in GSE27447 including 14 TNBC and 5 non-TNBC. GSE61724 consisted of 16 TNBC and 48 non-TNBC breast cancer samples. Moreover, 24 TNBC and 60 non-TNBC samples were involved in GSE18864.

Identification of differentially expressed genes (DEGs)

Each dataset was analyzed independently. The raw data were normalized by R/Bioconductor software. The method of linear models for microarray data (LIMMA) was employed to screen the differentially expressed genes (DEGs). P-value <0.05 and |logFC| >1 were the cut-off criteria to identify the DEGs. The overall DEGs in the 4 datasets were shown in a volcano plot (12).

The relative expression pattern of core DEGs in TNBC

The raw data was normalized. The relative expression equals the log2FC in each dataset. It was performed with the quotient of average log expression of TNBC and non-TNBC. The heat map was used to demonstrate and visualize the result using Package ‘gplots’ of R (13). A cluster analysis is also presented. According to the average of the relative expression, the expression pattern of core DEGs are presented.

Functional annotation of DEGs

The GO enrichment analysis was conducted to gain insight into the biological process of DEGs. GO included 3 groups: molecular function, biological process and cellular component. Kyoto Encyclopedia of Genes Genomes (KEGG, http://www.genome.jp/kegg/) is a knowledge database for systematic analysis of function annotation. In this study, the GO and KEGG were performed using web-based software KOBAS (http://kobas.cbi.pku.edu.cn/). P<0.05 was set as the threshold.

PPI network reconstruction

A protein-protein interaction (PPIs) analysis was conducted to visualize the functional relationships between the DEGs and other genes at a molecular level (4), which helped uncover the mechanisms in TNBC. The DEGs were used for protein-protein interaction (PPI) networks. These genes were submitted to the Biological General Repository for Interaction Datasets (BioGRID) (http://thebiogrid.org/) and retrieval of interactors. A topological analysis and visualization were conducted by CytoHubba plugin of Cytoscape (cytoHubba identifying hub) to screen the hub protein of the network.

Survival analysis of DEGs

Kaplan-Meier plotter (KM plotter, www.kmplot.com) is an online survival analysis tool to assess the effect of 22,277 genes on breast cancer prognosis from the microarrays of 1,809 patients (14). The patients with TNBC were split into two groups by the expression status of specific genes. The relapse-free survival (RFS) was depicted including the hazard ratio (HR) with 90% confidence intervals and the log rank P-value (15).

RT-PCR validation

To identify the markers, three TNBC lines (MDA-MB-231, MDA-MB-435, MDA-MB-468) and three non-TNBC lines (MCF-7, MDA-MB-453, SK-BR-3) were chosen (4). The total RNA was extracted with the TRIzol method. First-strand cDNA synthesis and RT-PCR validation were performed (4). The primers were designed with Primer Premier5. Reaction was set with 3 replicates, and run under the following conditions: 94°C for 2 min, 35 cycles of 94°C for 30 sec, 55°C for 30 sec, 72°C for 30 sec, and 72°C for 10 min. The relative expression used the 2−∆∆Ct calculation (16) with the human GAPDH gene as endogenous control for gene expression analysis. Nine genes were selected to perform real-time PCR in seven human cancer cell lines, in which PROM1 and KLK6 were upregulated genes and KRT18, GPR160, CMBL, AGR3, CREB3L4, CRIP1 and SDR16C5 were downregulated genes.

Results

Microarray analysis

In this study, the available gene expression datasets (GSE27447, GSE61724, and GSE18864) were used to gain insight into the molecular characteristics of TNBC. A total of 167 samples were analyzed (Table I). According to the statistical analysis, a total of 814 genes were contained in GSE27447 (442 downregulated and 372 upregulated genes) (Fig. 1A). Only 51 genes were identified in GSE61724 (37 downregulated genes and 14 upregulated genes) (Fig. 1B). In GSE18864, 159 genes were selected including 81 downregulated and 78 upregulated genes (Fig. 1C). The results are presented in Table I and Fig. 1.

Table I.

Microarray database and DEGs in the present study.

Table I.

Microarray database and DEGs in the present study.

GEO seriesNo. of TNBCNo. of non-TNBCDownregulated genesUpregulated genesTotal DEGs
GSE27447  514442372814
GSE617241648  37  14  51
GSE188642460  81  78159
Core DEGs of TNBC

In order to screen common specific genes in TNBC, the Veen diagram was processed. The overlap between the 3 datasets was investigated to identify how many common genes were involved in at least 2 datasets. These genes were named as the ‘core DEGs’. Twelve upregulated genes and 53 downregulated genes were identified. The upregulated genes are shown in Fig. 2A. In addition, the downregulated genes are shown in Fig. 2B. Only HORMAD1 in the upregulated genes and 5 genes (GPR160, NAT1, AGR2, AGR3 and ERBB4) were common in all of the three datasets (Fig. 2).

The relative expression pattern of core DEGs in TNBC

Among the core DEGs, three datasets revealed similar expression pattern. The cluster result of the sample revealed that GSE18864 was most consistent with the average expression, followed by GSE61724 and GSE27447. HORMAD1, ELF5, KLK6 and GABRP represented higher expression than other genes. AGR2, AGR3, ANKRD30A, NME5 and CYP4Z3P had lower expression (Fig. 3A and B). These significant genes would be potential biomarkers to characterize TNBC.

KEGG and GO results

To gain insight into the biological pathways in TNBC, A KEGG pathway analysis was conducted (Fig. 4A and B). A plot was made with R package clusterProfiler with a P-value cut-off of <0.05 (17). The result showed that ‘nicotine addiction’ (GABRP) and ‘basal cell carcinoma’ (FZD7) pathways were involved in the ‘upregulated genes. In addition, ‘ubiquinone and other terpenoid-quinone biosynthesis pathways’ (NQO1), ‘prostate cancer’ (CREB3L4, AR), ‘oocyte meiosis’ (AR, PGR), ‘nitrogen metabolism’ (CA12), ‘caffeine metabolism’ (NAT1), ‘choline metabolism in cancer’ (SLC44A5, SLC44A4), ‘chemical carcinogenesis’ (GSTM3, NAT1) were included in downregulated genes.

Based the GO function annotation, the upregulated genes were categorized into 39 groups (Fig. 4C). The top five terms were: ectodermal cell differentiation, ectoderm development, cell fate commitment involved in formation of primary germ layer, tissue regeneration and negative regulation of cysteine-type endopeptidase activity involved in apoptotic process. Furthermore, 32 GO terms were involved in the downregulated genes. The top five terms were: xenobiotic metabolic process, cellular response to xenobiotic stimulus, choline transmembrane transporter activity, response to xenobiotic stimulus and dystroglycan binding (Fig. 4C). P-value <0.01 was the cut-off criterion.

Construction of PPI network in TNBC

The reconstruction of the PPI network in TNBC was based on the 65 core DEGs with their interactors. As a result, the PPI network consisted of 133 nodes and 188 edges in the upregulated interactions (Fig. 5A), and in order to screen the hub, the top 50 groups were selected from the network to construct a subnetwork (Fig. 5B). Furthermore, 872 nodes and 995 edges in the downregulated interactions (Fig. 5C). The top 50 groups were also selected from the network to construct a subnetwork (Fig. 5D).

A local-based metric (i.e., degree), and a global-based metric (i.e., betweenness centrality) were adopted to determine the main genes (18). By these indexes, a set of genes were identified, including 5 upregulated genes (CRYAB, LAMP3, CSF2RB, KLK6, FZD7) and 5 downregulated genes (AR, KRT18, WWP1, PGR, ERBB4) (Table II). More attention should be paid to these genes in further research since they were chosen as the potential candidate biomarkers of TNBC.

Table II.

Topological characters of the core genes.

Table II.

Topological characters of the core genes.

GEO seriesProtein symbolDegreeBetweenness
Upregulated hubCRYAB  641957.5
LAMP3  641957.5
CSF2RB  141105
KLK6  12946
FZD7  10117
Downregulated hubAR238210081.7991
KRT18  9094918.36244
WWP1  8479340.42971
PGR  6331428.61489
ERBB4  5138581.13683
The RFS analysis of TNBC-specific genes

The prognostic value of TNBC-specific genes was also implemented in www.kmplot.com. The relapse-free survival (RFS) for patients with TNBC was assessed by the low and high expression of each gene. By the log-rank P-value, the top 6 significant genes (LAMP3, C16orf54, CSF2RB, CA12, ERBB4 and GPR160) were listed (Fig. 6). The HR of LAMP3 was 0.5 and log-rank P-value of it was 0.0019 (Fig. 6A). The HR of C16orf54 was 0.45 and log-rank P-value was 0.0051 (Fig. 6B). The HR of CSF2RB was 0.55 and log rank P-value was 0.0067 (Fig. 6C). The HR of CA12 was 1.77 and log rank P-value was 0.0087 (Fig. 6D). The HR of ERBB4 was 0.52 and log rank P-value was 0.01 (Fig. 6E). The HR of GPR160 was 0.53 and log-rank P-value was 0.026 (Fig. 6F). The result showed that the high expression of CA12 and the low expression of LAMP3, C16orf54, CSF2RB, ERBB4 and GPR160 exhibited associations with unfavorable relapse-free survival.

RT-PCR validation

To validate the DEGs in the integrated analysis, 9 TNBC-specific genes were chosen from DEGs by combining the PPI and marker selection result. PROM1 and KLK6 were selected as the upregulated genes. In addition, KRT18, GPR160, CMBL, AGR3, CREB3L4, CRIP1 and SDR16C5 were chosen as the downregulated genes. Primers are listed in Table III. In general, the expression patterns in TNBC and non-TNBC lines were consistent with that in the integrated analysis (Fig. 7). The KLK6 and PROM1 genes were highly expressed in TNBC cell lines (Fig. 7A and B). In addition, the SDR16C5 was lowly expressed in TNBC cell lines (Fig. 7C). The KRT18 was highly expressed in TNBC cell lines (Fig. 7D). The GPR160 was lowly expressed in TNBC cell lines (Fig. 7E). The CMBL was lowly expressed in TNBC cell lines (Fig. 7F). The AGR3 was lowly expressed in TNBC cell lines (Fig. 7G). The CREB3L4 was also lowly expressed in TNBC cell lines (Fig. 7H). However, the expression of CRIP1 was slightly higher in TNBC lines compared with that in non-TNBC lines (Fig. 7I). Finally, the RT-PCR result could be also used to identify the reliability of the integrated analysis.

Table III.

The primers used in the RT-PCR validation.

Table III.

The primers used in the RT-PCR validation.

Gene symbolGene full nameSequence primers from 5′ to 3′
GAPDH Glyceraldehyde-3-phosphate dehydrogenaseF: GCACCGTCAAGGCTGAGAAC
R: GGATCTCGCTCCTGGAAGATG
AGR3Anterior gradient 3, protein disulphide isomerase family memberF: CCAACCTTGCCATTGCAAT
R: TCATCTCCCCATCCTCTTGAGA
CMBL Carboxymethylenebutenolidase homologF: CCCTCTGGCGACTGGTCTATC
R: GCACTGATCTCTCTATCGATCTTCTG
CREB3L4cAMP responsive element binding protein 3 like 4F: CCCAGCTTCAGTCCATTCCA
R: AAGTCACTCCGTGAGGCTGGTA
CRIP1Cysteine rich protein 1F: GCTGAGCACGAAGGCAAAC
R: CAAACATGGCTGCGTAGCA
GPR160G protein-coupled receptor 160F: TTTCAGTCCTTGCTTATGTTTTGG
R: CATTCTGTGCCTTCAGGCTTT
KLK6Kallikrein related peptidase 6F: GCCTACCCTGGCCAGATCA
R: ATCACCCTGGCAGGAATCC
KRT18Keratin 18F: CTCCGCAAGGTCATTGATGA
R: TACTTCCTCTTCGTGGTTCTTCTTC
PROM1Prominin 1F: TTCCCAGAAGATACTTTGAGAA
R: CATACAAAAGAAATACCCCACCAGAG
SDR16C5Short chain dehydrogenase/reductase family 16C member 5F: AGTAGCCGACCAGGTTAAAAAAGA
R: TGTTACGATTCCGGCATTGTT

[i] F, forward; R, reverse.

Discussion

Identification of valid biomarkers in diagnosis, treatment and prognosis in TNBC is a challenging task. Although individual datasets have produced hundreds of genes (Table I), only six common DEGs are achieved. In consideration of the sample limitation and heterogeneity from patients and array platforms, individual analyses were only confined to uncover the central mechanisms behind TNBC (18). The integrated analysis from different gene expression profiles would be efficient for insight into TNBC.

In our study, we achieved 65 DEGs from at least 2 datasets (Fig. 2). Furthermore, KEGG enrichment result is related to a range of cancer pathways, such as basal cell carcinoma (FZD7), prostate cancer (CREB3L4, AR), choline metabolism in cancer (SLC44A5, SLC44A4) and chemical carcinogenesis (GSTM3, NAT1), which provide more evidence to uncover the mechanisms of TNBC (Fig. 4). The GO annotation was mainly enriched in ectodermal cell differentiation, tissue regeneration, xenobiotic metabolic process, and prostate gland development. It is consistent with KEGG annotation. Then, the PPI network was reconstructed.

We chose a global and local metric to investigate the interaction between the DEGs and other intimate genes in TNBC. The main genes in the network would benefit the screening of hubs. These hub genes deserve more attention and further study since they are potential candidate biomarkers for TNBC.

It has been reported that FZD7 is overexpressed in 67% of TNBC (19). The FZD7-involved canonical Wnt pathway is the basis for the formation of TNBC. It could be regarded as the biomarker and potential therapeutic target of TNBC (7). LAMP3 is a protein included in cell proliferation term of GO. The activation of the IFN pathway is an early event following AC chemotherapy in 9 TNBC (20). Nagelkerke et al reported the role of UPR-induced LAMP3 in hypoxia-mediated migration of breast cancer cells (21).

FAM171A1 has been identified as an overexpressed gene in a large meta-analysis of seven patients with ER-negative BrCa tumor, which is also revealed as a hub gene (22). CSF2RB is related to the molecular profiling and computational network of TAZ-mediated mammary tumorigenesis (23). The overexpression of IRX1 is associated with growth arrest in gastric cancer, which can inhibit peritoneal spreading and metastasis (24). However, it has not been reported in the TNBC. In the present study, it was found that IRX1 serves as a common gene in GSE27447 and GSE18864, which is a highly-expressed gene with average lgFC of 1.14 in TNBC compared with non-TNBC (Figs. 2 and 3). C16orf54 is reported in immune/inflammation-related genes in the stromal gene set of breast cancer in multivariate Cox proportional hazard models with HR of 0.507. There is no research on C16orf54 in TNBC (25).

HORMAD1 is the only common gene in the 3 datasets, which holds the interaction with BRCA1 in the PPI network (Fig. 2). Watkins et al has reported that the HORMAD1 overexpression contributes to homologous recombination deficiency in TNBC. Higher expression of HORMAD1 inhibits the RAD51-dependent homologous recombination and promotes the use of alternative forms of DNA repair (26). Komatsu et al also reported that HORMAD1 is involved in the carcinogenesis of TNBC (27). In the present study, HORMAD1 possesses the lgFC 1.71, which could be a potential biomarker in TNBC. Chakrabarti et al reported that the ELF5 regulates the mammary gland stem cell fate by influencing the notch signaling (28). CRYAB, ELF5, and GABRP have been reported as the molecular targeted therapies (29). KLK6 may be a promising biomarker in epithelial-to-mesenchymal transition (EMT) due to the fact that it belongs to a family of serine proteases, which involves the clinical biomarker KLK3 in prostate cancer (30). However, there is no research on KLK6 in TNBC.

Among the downregulated core DEGs, the present analysis showed that some novel genes were not investigated in TNBC patients previously, such as KRT18, GPR160, CMBL, AGR3, CREB3L4, CRIP1 and SDR16C5. Several downregulated genes have been reported in previous research including STC2, FOXA1, ATP8B1, SLC39A6, DUSP4, KRT18, TSPAN1, ERBB4, GATA3 (18). FOXA1, ANKRD30A, CMBL, GPR160 and AGR2 were downregulated genes in TNBC compared with non-TNBC (4). AR has been used to indicate the prognostic value in TNBC based on a tissue microarray (31). In the PPI network, AR is the top core gene with a degree of 238. NAT1 and PGR were enriched for genes associated with luminal biology (32). AGR2 has been reported highly associated with the properties of breast cancer stem cells (33). AGR3 is regarded as a suitable serum-based biomarker for early cancer detection with overexpression (34). Chen et al reported the amplified WWP1 as the potential molecular target in breast cancer (35). We have identified WWP1 as the core gene in the PPI network (Fig. 5 and Table II).

In conclusion, the TNBC-specific gene expression profiles have been identified and the RFS analyses of these TNBC-specific genes have been performed. Furthermore, by integrated analysis, we achieved a set of core DEGs in TNBC compared with non-TNBC. The function annotation and PPI network reconstruction would be conducive to understand the underlying mechanisms in TNBC. According to the combination of the relative expression pattern, GO and KEGG annotation and PPI network reconstruction, a set of hub genes have been identified, including HORMAD1, PROM1 and KLK6 from upregulated genes, KRT18, GPR160, CMBL, AGR3, CREB3L4, CRIP1 and SDR16C5 from the downregulated genes, which are closely associated with TNBC. These hub genes are novel and specific genes that have not been investigated much. They also have higher expression of dysregulation. In this way, these hub genes will act as potential biomarkers in TNBC and contribute to the study of TNBC.

Acknowledgements

This study was supported by the National Natural Science Foundation of China (nos. 81373573 and 81460655) and the Mongolian Medicine Systems Biology Science and Technology Innovation Team Plan of Inner Mongolia.

References

1 

Al-Hajj M, Wicha MS, Benito-Hernandez A, Morrison SJ and Clarke MF: Prospective identification of tumorigenic breast cancer cells. Proc Natl Acad Sci USA. 100:3983–3988. 2003. View Article : Google Scholar : PubMed/NCBI

2 

Yang XR, Sherman ME, Rimm DL, Lissowska J, Brinton LA, Peplonska B, Hewitt SM, Anderson WF, Szeszenia-Dabrowska N, Bardin-Mikolajczak A, et al: Differences in risk factors for breast cancer molecular subtypes in a population-based study. Cancer Epidemiol Biomarkers Prev. 16:439–443. 2007. View Article : Google Scholar : PubMed/NCBI

3 

Cho EY, Chang MH, Choi YL, Lee JE, Nam SJ, Yang JH, Park YH, Ahn JS and Im YH: Potential candidate biomarkers for heterogeneity in triple-negative breast cancer (TNBC). Cancer Chemother Pharmacol. 68:753–761. 2011. View Article : Google Scholar : PubMed/NCBI

4 

He J, Yang J, Chen W, Wu H, Yuan Z, Wang K, Li G, Sun J and Yu L: Molecular features of triple negative breast cancer: Microarray evidence and further integrated analysis. PLoS One. 10:e01298422015. View Article : Google Scholar : PubMed/NCBI

5 

Anders CK and Carey LA: Biology, metastatic patterns, and treatment of patients with triple-negative breast cancer. Clin Breast Cancer. 9 Suppl 2:S73–S81. 2009. View Article : Google Scholar : PubMed/NCBI

6 

Petricoin EF III, Hackett JL, Lesko LJ, Puri RK, Gutman SI, Chumakov K, Woodcock J, Feigal DW Jr, Zoon KC and Sistare FD: Medical applications of microarray technologies: A regulatory science perspective. Nat Genet. 32:S474–S479. 2002. View Article : Google Scholar

7 

Yang L, Wu X, Wang Y, Zhang K, Wu J, Yuan YC, Deng X, Chen L, Kim CC, Lau S, et al: FZD7 has a critical role in cell proliferation in triple negative breast cancer. Oncogene. 30:4437–4446. 2011. View Article : Google Scholar : PubMed/NCBI

8 

Mathe A, Wong-Brown M, Morten B, Forbes JF, Braye SG, Avery-Kiejda KA and Scott RJ: Novel genes associated with lymph node metastasis in triple negative breast cancer. Sci Rep. 5:158322015. View Article : Google Scholar : PubMed/NCBI

9 

Wang Y, Zhang T, Kwiatkowski N, Abraham BJ, Lee TI, Xie S, Yuzugullu H, Von T, Li H, Lin Z, et al: CDK7-dependent transcriptional addiction in triple-negative breast cancer. Cell. 163:174–186. 2015. View Article : Google Scholar : PubMed/NCBI

10 

Abramovitz M, Barwick BG, Willis S, Young B, Catzavelos C, Li Z, Kodani M, Tang W, Bouzyk M, Moreno CS, et al: Molecular characterisation of formalin-fixed paraffin-embedded (FFPE) breast tumour specimens using a custom 512-gene breast cancer bead array-based platform. Br J Cancer. 105:1574–1581. 2011. View Article : Google Scholar : PubMed/NCBI

11 

Al-Ejeh F, Simpson PT, Sanus JM, Klein K, Kalimutho M, Shi W, Miranda M, Kutasovic J, Raghavendra A, Madore J, et al: Meta-analysis of the global gene expression profile of triple-negative breast cancer identifies genes for the prognostication and treatment of aggressive breast cancer. Oncogenesis. 3:e1002014. View Article : Google Scholar : PubMed/NCBI

12 

Tauber E, Miller-Fleming L, Mason RP, Kwan W, Clapp J, Butler NJ, Outeiro TF, Muchowski PJ and Giorgini F: Functional gene expression profiling in yeast implicates translational dysfunction in mutant huntingtin toxicity. J Biol Chem. 286:410–419. 2011. View Article : Google Scholar : PubMed/NCBI

13 

Warnes GR, Bolker B, Bonebakker L, Gentleman R, Huber W, Liaw A, Lumley T, Maechler M, Magnusson A, Moeller S, et al: Package ‘gplots’: Various R programming tools for plotting data. R package version 2. 2009.https://scholar.google.com/citations?view_op=view_citation&hl=en&user=o7GsfJwAAAAJ&citation_for_view=o7GsfJwAAAAJ:u-x6o8ySG0sC

14 

Györffy B, Lanczky A, Eklund AC, Denkert C, Budczies J, Li Q and Szallasi Z: An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients. Breast Cancer Res Treat. 123:725–731. 2010. View Article : Google Scholar : PubMed/NCBI

15 

Xu Z, Zhou Y, Cao Y, Dinh TLA, Wan J and Zhao M: Identification of candidate biomarkers and analysis of prognostic values in ovarian cancer by integrated bioinformatics analysis. Med Oncol. 33:1302016. View Article : Google Scholar : PubMed/NCBI

16 

Arocho A, Chen B, Ladanyi M and Pan Q: Validation of the 2-DeltaDeltaCt calculation as an alternate method of data analysis for quantitative PCR of BCR-ABL P210 transcripts. Diagn Mol Pathol. 15:56–61. 2006. View Article : Google Scholar : PubMed/NCBI

17 

Paranjpe SS, Jacobi UG, van Heeringen SJ and Veenstra GJ: A genome-wide survey of maternal and embryonic transcripts during Xenopus tropicalis development. BMC Genomics. 14:7622013. View Article : Google Scholar : PubMed/NCBI

18 

Karagoz K, Sinha R and Arga KY: Triple negative breast cancer: A multi-omics network discovery strategy for candidate targets and driving pathways. OMICS. 19:115–130. 2015. View Article : Google Scholar : PubMed/NCBI

19 

Elsamany S and Abdullah S: Triple-negative breast cancer: Future prospects in diagnosis and management. Med Oncol. 31:8342014. View Article : Google Scholar : PubMed/NCBI

20 

Legrier M-E, Bièche I, Gaston J, Beurdeley A, Yvonnet V, Déas O, Thuleau A, Château-Joubert S, Servely JL, Vacher S, et al: Activation of IFN/STAT1 signalling predicts response to chemotherapy in oestrogen receptor-negative breast cancer. Br J Cancer. 114:177–187. 2016. View Article : Google Scholar : PubMed/NCBI

21 

Nagelkerke A, Bussink J, Mujcic H, Wouters BG, Lehmann S, Sweep FC and Span PN: Hypoxia stimulates migration of breast cancer cells via the PERK/ATF4/LAMP3-arm of the unfolded protein response. Breast Cancer Res. 15:R22013. View Article : Google Scholar : PubMed/NCBI

22 

Stayrook KR, Mack JK, Cerabona D, Edwards DF, Bui HH, Niewolna M, Fournier PG, Mohammad KS, Waning DL and Guise TA: TGFβ-mediated induction of SphK1 as a potential determinant in human MDA-MB-231 breast cancer cell bone metastasis. Bonekey Rep. 4:7192015. View Article : Google Scholar : PubMed/NCBI

23 

Frangou C, Li Y-W, Shen H, Yang N, Wilson KE, Blijlevens M, Guo J, Nowak NJ and Zhang J: Molecular profiling and computational network analysis of TAZ-mediated mammary tumorigenesis identifies actionable therapeutic targets. Oncotarget. 5:12166–12176. 2014. View Article : Google Scholar : PubMed/NCBI

24 

Jiang J, Liu W, Guo X, Zhang R, Zhi Q, Ji J, Zhang J, Chen X, Li J, Zhang J, et al: IRX1 influences peritoneal spreading and metastasis via inhibiting BDKRB2-dependent neovascularization on gastric cancer. Oncogene. 30:4498–4508. 2011. View Article : Google Scholar : PubMed/NCBI

25 

Winslow S, Leandersson K, Edsjö A and Larsson C: Prognostic stromal gene signatures in breast cancer. Breast Cancer Res. 17:232015. View Article : Google Scholar : PubMed/NCBI

26 

Watkins J, Weekes D, Shah V, Gazinska P, Joshi S, Sidhu B, Gillett C, Pinder S, Vanoli F, Jasin M, et al: Genomic complexity profiling reveals that HORMAD1 overexpression contributes to homologous recombination deficiency in triple-negative breast cancers. Cancer Discov. 5:488–505. 2015. View Article : Google Scholar : PubMed/NCBI

27 

Komatsu M, Yoshimaru T, Matsuo T, Kiyotani K, Miyoshi Y, Tanahashi T, Rokutan K, Yamaguchi R, Saito A, Imoto S, et al: Molecular features of triple-negative breast cancer cells by genome-wide gene expression profiling analysis. Int J Oncol. 42:478–506. 2013. View Article : Google Scholar : PubMed/NCBI

28 

Chakrabarti R, Wei Y, Romano RA, DeCoste C, Kang Y and Sinha S: Elf5 regulates mammary gland stem/progenitor cell fate by influencing notch signaling. Stem Cells. 30:1496–1508. 2012. View Article : Google Scholar : PubMed/NCBI

29 

Tordai A, Wang B, Andre F, Liedtke C, Symmans W, Esteva F and Pusztai L: Screening for expression of novel marker proteins for triple negative breast cancer in breast cancer cell lines. Cancer Res. Suppl 9:67:19782007.http://cancerres.aacrjournals.org/content/67/9_Supplement/1978

30 

Daniele V, Pasquale S, Frank J, Trerotola M, Giudetti A, Capobianco L, Tinelli A, Bellomo C, Fournier I, Gaballo A, et al: Translating epithelial mesenchymal transition markers into the clinic: Novel insights from proteomics. EuPA Open Proteom. 10:31–41. 2016.http://www.sciencedirect.com/science/article/pii/S2212968516300034 View Article : Google Scholar

31 

He J, Peng R, Yuan Z, Wang S, Peng J, Lin G, Jiang X and Qin T: Prognostic value of androgen receptor expression in operable triple-negative breast cancer: A retrospective analysis based on a tissue microarray. Med Oncol. 29:406–410. 2012. View Article : Google Scholar : PubMed/NCBI

32 

Anders C, Deal AM, Abramson V, Liu MC, Storniolo AM, Carpenter JT, Puhalla S, Nanda R, Melhem-Bertrandt A, Lin NU, et al: TBCRC 018: Phase II study of iniparib in combination with irinotecan to treat progressive triple negative breast cancer brain metastases. Breast Cancer Res Treat. 146:557–566. 2014. View Article : Google Scholar : PubMed/NCBI

33 

Jang P, Holleran T, Burns M, Gebizlioglu K and Governale A: Identifying gene signatures associated with cancer stem cells and drug resistance from triple negative breast cancer cells after gene targeting treatment. Austin J Biomed Eng. 1:10182014.

34 

Garczyk S, von Stillfried S, Antonopoulos W, Hartmann A, Schrauder MG, Fasching PA, Anzeneder T, Tannapfel A, Ergönenc Y, Knüchel R, et al: AGR3 in breast cancer: Prognostic impact and suitable serum-based biomarker for early cancer detection. PLoS One. 10:e01221062015. View Article : Google Scholar : PubMed/NCBI

35 

Chen C, Zhou Z, Ross JS, Zhou W and Dong JT: The amplified WWP1 gene is a potential molecular target in breast cancer. Int J Cancer. 121:80–87. 2007. View Article : Google Scholar : PubMed/NCBI

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October-2017
Volume 38 Issue 4

Print ISSN: 1021-335X
Online ISSN:1791-2431

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Spandidos Publications style
Guo J, Gong G and Zhang B: Screening and identification of potential biomarkers in triple-negative breast cancer by integrated analysis. Oncol Rep 38: 2219-2228, 2017
APA
Guo, J., Gong, G., & Zhang, B. (2017). Screening and identification of potential biomarkers in triple-negative breast cancer by integrated analysis. Oncology Reports, 38, 2219-2228. https://doi.org/10.3892/or.2017.5911
MLA
Guo, J., Gong, G., Zhang, B."Screening and identification of potential biomarkers in triple-negative breast cancer by integrated analysis". Oncology Reports 38.4 (2017): 2219-2228.
Chicago
Guo, J., Gong, G., Zhang, B."Screening and identification of potential biomarkers in triple-negative breast cancer by integrated analysis". Oncology Reports 38, no. 4 (2017): 2219-2228. https://doi.org/10.3892/or.2017.5911