Open Access

Integrated analysis of differentially expressed genes and pathways in triple‑negative breast cancer

  • Authors:
    • Cancan Peng
    • Wenli Ma
    • Wei Xia
    • Wenling Zheng
  • View Affiliations

  • Published online on: Wednesday, January 4, 2017
  • Pages:1087-1094 DOI: 10.3892/mmr.2017.6101
0

Abstract

Triple‑negative breast cancer (TNBC) is a heterogeneous disease characterized by an aggressive phenotype and reduced survival. The aim of the present study was to investigate the molecular mechanisms involved in the carcinogenesis of TNBC and to identify novel target molecules for therapy. The differentially expressed genes (DEGs) in TNBC and normal adjacent tissue were assessed by analyzing the GSE41970 microarray data using Qlucore Omics Explorer, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes. Pathway enrichment analyses for DEGs were performed using the Database for Annotation, Visualization and Integrated Discovery online resource. A protein‑protein interaction (PPI) network was constructed using Search Tool for the Retrieval of Interacting Genes, and subnetworks were analyzed by ClusterONE. The PPI network and subnetworks were visualized using Cytoscape software. A total of 121 DEGs were obtained, of which 101 were upregulated and 20 were downregulated. The upregulated DEGs were significantly enriched in 14 pathways and 83 GO biological processes, while the downregulated DEGs were significantly enriched in 18 GO biological processes. The PPI network with 118 nodes and 1,264 edges was constructed and three subnetworks were extracted from the entire network. The significant hub DEGs with high degrees were identified, including TP53, glyceraldehyde‑3‑phosphate dehydrogenase, cyclin D1, HRAS and proliferating cell nuclear antigen, which were predominantly enriched in the cell cycle pathway and pathways in cancer. A number of critical genes and pathways were revealed to be associated with TNBC. The present study may provide an improved understanding of the pathogenesis of TNBC and contribute to the development of therapeutic targets for TNBC.

Introduction

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with a poor prognosis and high mortality, which is diagnosed more frequently in young and premenopausal women (1,2). Due to the absence of estrogen receptors, progesterone receptor and the human epidermal growth factor receptor-2 in TNBC tumor types, patients with TNBC do not respond to hormone- or trastuzumab-based therapies, leaving cytotoxic chemotherapy as the current therapy (3). Therefore, the identification of critical genes involved in TNBC carcinogenesis may provide a strategy for molecular target therapy of TNBC (4).

Microarray technology, which may be used to detect the global gene expression, has provided an alternative method for the molecular classification of different types of cancer, as well as the exploration of potential prognostic biomarkers and therapeutic targets. Based on microarray analysis, breast cancer has been divided into distinctive subtypes according to different gene expression patterns (511). Furthermore, Rakha et al (4) performed a microarray analysis on a relatively large set of 1,944 cases of invasive breast cancer that contained TNBC tissues, as well as information on tumor size, lymph node involvement and androgen receptor expression levels, and determined these three parameters as the most useful prognosticators of TNBC. Therefore, a systematic analysis of gene expression patterns in TNBC may aid researchers to comprehend the development and the treatment of this disease and to identify novel therapeutic targets.

In the present study, biological microarray analysis was used to analyze the gene expression profile of TNBC and to screen the differentially expressed genes (DEGs) between TNBC and adjacent normal tissues. Furthermore, altered biological pathways in TNBC were identified using bioinformatics tools. Additionally, a protein-protein interaction (PPI) network of DEGs was constructed in order to identify the crucial genes involved in the process of TNBC. The present study aimed to improve the understanding of the underlying pathological mechanism and facilitate the discovery of potential novel therapeutic targets for TNBC.

Materials and methods

Gene expression data

The GSE41970 gene expression profiles of TNBC were downloaded from Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo) of the National Center for Biotechnology Information (Bethesda, MD, USA) based on the GPL16299 platform data (NanoString nCounter mRNA Human Cancer Reference Kit; NanoString, Inc., Ticino, Bellinzona, Switzerland) (12). In total, 200 specimens, including 160 primary TNBC specimens and 40 normal samples, were used in the present study.

Data processing and differential analysis

Qlucore Omics Explorer (QOE) software (version 3; Qlucore AB, Lund, Sweden) was used to analyze the data from the microarray (13,14). Intensity values of each probe-set were log2 transformed, and a gene-specific t-test was subsequently performed between TNBC samples and matched normal samples. P<0.01 and [log2(fold change)>2] were regarded as the cut-off criteria to screen out DEGs. To generate an overview of the gene expression profile, hierarchical clustering was also performed by QOE.

Gene ontology (GO) and pathway enrichment analysis

To determine the biological pathways altered in TNBC, GO biological process terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the DEGs using Database for Annotation, Visualization and Integration Discovery (version 6.7; DAVID; http://david.abcc.ncifcrf.gov) (15). P<0.01 and false discovery rate (FDR) <0.01 were set as the cut-off value.

Construction and analysis of PPI network

It is possible to utilize PPI research to study DEG protein functions at the molecular level, and cellular regulatory mechanisms can be interpreted by elucidation of genome-wide protein interactions (16). The PPI network was constructed for the DEGs using information provided by the Search Tool for the Retrieval of Interacting Genes, version 10 (STRING; http://string-db.org) (17), and was subsequently visualized using Cytoscape version 3.2.1 (http://cytoscape.org) (18). The interactions of protein pairs with a combined score >0.5 were retained in the network and the hub genes were screened according to the degree score (the number of neighbors). The subnetworks were then analyzed by Clustering with Overlapping Neighborhood Expansion (ClusterONE; http://www.paccanarolab.org/clusterone). GO functional and KEGG pathway enrichment analyses of the most significant subnetworks were performed with a threshold of P<0.01 and FDR<0.01.

Results

Identification of DEGs in TNBC

Using QOE, 121 DEGs were identified between 160 TNBC and 40 normal tissues, including 101 upregulated genes and 20 downregulated genes. The 10 most significantly up- or downregulated genes are listed in Table I.

Table I.

Top 10 most significantly up- or downregulated differentially expressed genes in triple-negative breast cancer compared with normal tissue.

Table I.

Top 10 most significantly up- or downregulated differentially expressed genes in triple-negative breast cancer compared with normal tissue.

Gene symbolP-valueFold-change
Upregulated genes
  BIRC5 6.50×10−3648.63
  MYBL2 4.32×10−3457.33
  TOP2A 3.49×10−3141.91
  CDC2 4.07×10−2932.75
  MMP9 2.09×10−2321.35
  CHEK1 8.68×10−2313.37
  SPP1 2.21×10−2240.41
  TYMS 4.26×10−2218.21
  E2F1 4.38×10−2111.18
  PCNA 1.27×10−20   6.87
Downregulated genes
  IGFBP6 2.19×10−14   0.12
  ESR1 1.09×10−13   0.09
  DLC1 1.27×10−12   0.14
  EGR1 6.87×10−11   0.20
  IGF1 1.72×10−10   0.27
  TGFBR3 3.37×10−10   0.16
  PPARG 3.84×10−10   0.23
  NGFR 3.93×10−10   0.12
  CD34 6.67×10−9   0.27
  FOS 5.83×10−8   0.30
Clustering of DEGs

The 121 DEGs in TNBC compared with normal tissues were selected for hierarchical clustering analysis. As presented in Fig. 1, the DEGs were divided into two major groups, separating 101 upregulated genes from 20 downregulated genes.

GO of biological process enrichment analysis of up- and downregulated DEGs

The upregulated DEGs were significantly enriched in 83 biological processes and the top five significantly enriched processes were mainly associated with the regulation of cell proliferation, cell cycle and cell apoptosis (Table II). The downregulated DEGs were significantly enriched in 18 biological processes, among which the most significant were mainly involved in the regulation of cell proliferation, macromolecule metabolic process and cell migration (Table II).

Table II.

Top five significantly enriched biology processes for differentially expressed genes.

Table II.

Top five significantly enriched biology processes for differentially expressed genes.

GO IDTermGene countsP-valueFDR
Upregulated
  GO:0042127Regulation of cell proliferation35 2.22×10−18 3.76×10−15
  GO:0051726Regulation of cell cycle24 8.20×10−17 1.89×10−13
  GO:0008284Positive regulation of cell proliferation26 8.35×10−17 1.89×10−13
  GO:0042981Regulation of apoptosis31 2.15×10−14 3.63×10−11
  GO:0043067Regulation of programmed cell death31 2.79×10−14 4.72×10−11
Downregulated
  GO:0042127Regulation of cell proliferation12 1.19×10−9 1.88×10−6
  GO:0010604Positive regulation of macromolecule metabolic process11 5.40×10−8 8.57×10−5
  GO:0030334Regulation of cell migration  7 8.25×10−8 1.31×10−4
  GO:0040012Regulation of locomotion  7 1.76×10−7 2.79×10−4
  GO:0051270Regulation of cell motion  7 1.81×10−7 2.88×10−4

[i] GO, gene ontology; Gene counts, number of genes; FDR, false discovery rate.

KEGG pathway enrichment analysis of up- and downregulated DEGs

The upregulated DEGs were significantly enriched in 14 pathways, which mainly involved the cell cycle and pathways associated with cancer, including the p53 signaling pathway (Table III). In the downregulated DEGs, four pathways were identified, however they were without statistical significance (P>0.01 or FDR>0.01).

Table III.

Significantly enriched pathways for differentially expressed genes.

Table III.

Significantly enriched pathways for differentially expressed genes.

KEGG IDTermGene countsP-valueFDR
hsa05200Pathways in cancer37 3.77×10−24 4.19×10−21
hsa05219Bladder cancer17 8.90×10−20 9.87×10−17
hsa05223Non-small cell lung cancer14 3.04×10−13 3.38×10−10
hsa04110Cell cycle18 1.03×10−12 1.14×10−9
hsa05215Prostate cancer16 1.05×10−12 1.16×10−9
hsa05212Pancreatic cancer14 1.59×10−11 1.76×10−8
hsa05220Chronic myeloid leukemia14 2.74×10−11 3.04×10−8
hsa05214Glioma13 5.32×10−11 5.91×10−8
hsa05218Melanoma13 2.33×10−10 2.59×10−7
hsa05213Endometrial cancer11 2.22×10−9 2.46×10−6
hsa05222Small cell lung cancer12 2.30×10−8 2.56×10−5
hsa05216Thyroid cancer  8 1.25×10−7 1.39×10−4
hsa05210Colorectal cancer10 2.74×10−6 3.04×10−3
hsa04115p53 signaling pathway  9 5.03×10−6 5.58×10−3

[i] KEGG, Kyoto Encyclopedia of Genes and Genomes; Gene counts, number of genes; FDR, false discovery rate.

PPI network analysis

Based on STRING database analysis, a total of 1,264 protein pairs with the combined score >0.5 were identified. The PPI network including 118 nodes and 1,264 edges was constructed (Fig. 2) and the connectivity degree of each node was calculated. The top five nodes TP53 (degrees, 86), glyceraldehyde-3-phosphate dehydrogenase (GAPDH; degrees, 62), cyclin D1 (CCND1; degrees, 58), HRAS (degrees, 58) and proliferating cell nuclear antigen (PCNA; degrees, 52) with degrees >50 were screened as hub proteins in the PPI network.

Three subnetworks (1,2 and 3) with P<0.05 were established using the ClusterONE plugin (Fig. 3). The hub proteins TP53, GAPDH, CCND1, HRAS and PCNA were demonstrated to be involved in all of these three subnetworks. Subnetwork 1 was mainly associated with regulation of cell proliferation and cell communication, while the significant pathways were mainly associated with pathways in cancer (Tables IV and V). By contrast, the other subnetworks were mainly associated with the cell cycle (Table IV). Additionally, the significant pathways correlated with subnetwork 2 and 3 were the cell cycle, the p53 signaling pathway and pathways in cancer (Table V).

Table IV.

Top five significantly enriched biology processes for differentially expressed genes in three subnetworks.

Table IV.

Top five significantly enriched biology processes for differentially expressed genes in three subnetworks.

GO IDTermGene countsP-valueFDR
Subnetwork 1
  GO:0042127Regulation of cell proliferation30 3.15×10−20 5.31×10−17
  GO:0010647Positive regulation of cell communication21 7.11×10−18 1.20×10−14
  GO:0009967Positive regulation of signal transduction20 1.89×10−17 3.19×10−14
  GO:0008284Positive regulation of cell proliferation22 3.59×10−17 6.06×10−14
  GO:0009719Response to endogenous stimulus21 4.01×10−16 7.44×10−13
Subnetwork 2
  GO:0022402Cell cycle process24 1.05×10−20 1.65×10−17
  GO:0007049Cell cycle26 3.03×10−20 4.79×10−17
  GO:0051726Regulation of cell cycle19 3.49×10−18 5.51×10−15
  GO:0022403Cell cycle phase20 7.57×10−18 1.20×10−14
  GO:0051329Interphase of mitotic cell cycle13 3.33×10−16 5.22×10−13
Subnetwork 3
  GO:0022402Cell cycle process23 1.66×10−20 2.59×10−17
  GO:0007049Cell cycle25 2.88×10−20 4.51×10−17
  GO:0051726Regulation of cell cycle19 4.22×10−19 6.61×10−16
  GO:0022403Cell cycle phase19 2.27×10−17 3.55×10−14
  GO:0006259DNA metabolic process20 3.35×10−17 5.25×10−14

[i] GO, gene ontology; Gene counts, number of genes; FDR, false discovery rate.

Table V.

Top five significantly enriched pathways for differentially expressed genes in three subnetworks.

Table V.

Top five significantly enriched pathways for differentially expressed genes in three subnetworks.

KEGG IDTermGene countsP-valueFDR
Subnetwork 1
  hsa05200Pathways in cancer32 3.04×10−25 3.30×10−22
  hsa05219Bladder cancer15 4.66×10−19 5.05×10−16
  hsa05215Prostate cancer14 1.23×10−12 1.34×10−9
  hsa05213Endometrial cancer11 3.80×10−11 4.12×10−8
  hsa05218Melanoma12 4.29×10−11 4.65×10−8
Subnetwork 2
  hsa04110Cell cycle19 3.60×10−20 3.72×10−17
  hsa05200Pathways in cancer21 5.11×10−15 5.27×10−12
  hsa05215Prostate cancer11 3.82×10−10 3.94×10−7
  hsa04115p53 signaling pathway10 7.34×10−10 7.57×10−7
  hsa05220Chronic myeloid leukemia10 1.81×10−9 1.86×10−6
Subnetwork 3
  hsa04110Cell cycle18 2.32×10−19 2.37×10−16
  hsa05200Pathways in cancer19 2.03×10−13 2.07×10−10
  hsa04115p53 signaling pathway  9 9.25×10−9 9.43×10−6
  hsa05220Chronic myeloid leukemia  9 2.04×10−8 2.08×10−5
  hsa05223Non-small cell lung cancer  8 4.40×10−8 4.49×10−5

[i] KEGG, Kyoto Encyclopedia of Genes and Genomes; Gene counts, number of genes; FDR, false discovery rate.

Discussion

TNBC is one of the most deadly breast cancer subtypes due to the lack of an effective treatment. The improvement of the diagnosis and therapeutic methods of this disease relies on the discovery of novel potential molecular markers. In the present study, a total of 121 DEGs between TNBC and normal tissues were identified, consisting of 101 upregulated and 20 downregulated genes. With 121 gene signatures mapped from the STRING database, a giant component PPI network was established with 118 nodes and 1,264 edges. After applying the ClusterONE clustering algorithm, three significant subnetworks with highly connected nodes were obtained. The top five ranked genes as hub nodes with maximum degrees were identified, including TP53, GAPDH, CCND1, HRAS and PCNA. In addition, it was revealed that these hub nodes existed in all of the three subnetworks, which contributed to the biological processes of cell growth and the cell cycle and connected the cell cycle, p53 signaling pathway and pathways in cancer.

TP53, also termed p53, encodes a tumor suppressor protein that controls the cell cycle arrest, apoptosis, senescence, DNA repair and changes in metabolism (19). Mutations in this gene are associated with a variety of human cancer types, including TNBC, lung cancer and high-grade serous ovarian tumor types (20). p53 is mutated and overexpressed in ~25–30% of human breast cancer (21), and had an increased incidence in TNBC (22,23). Nishimura et al (24) also found that high proliferation rate and frequent p53 overexpression occurred in TNBC. Furthermore, p53 is also expressed as smaller isoforms, some of which inhibit wild-type p53. Avery-Kiejda et al (25) reported that Δ40p53, one of the p53 isoforms, was significantly upregulated in tumor tissue when compared with the normal breast and was closely associated with TNBC. Mutation and isoforms of p53 may provide an alternate explanation for the malfunction of p53 pathway and deregulated p53 signaling in TNBC. The present study demonstrated that the p53 gene was elevated in the TNBC samples and that p53 was a hub protein with a degree score of 86 in the established PPI network. Therefore, the p53 gene may be a key regulator in TNBC development.

GAPDH, originally identified as a glycolytic enzyme is considered a housekeeping gene. It is frequently used as an internal standard for gene expression in RNA or protein experiments. However, the abnormal expression of GAPDH has been confirmed to have a close association with various types of cancer (26), as it serves an important role in carcinogenesis and cell death, as well as energy metabolism (27). Notably, increased levels of GAPDH were observed in most types of human cancer and were often correlated with reduced survival (28,29). Results of the present study indicated that GAPDH is predicated as a pivotal gene associated with TNBC and the cell cycle, and may be a potential biomarker for detection and prevention of TNBC.

CCND1, a member of the cyclin-dependent kinase regulator family, is required for the activation of CDK4 and CDK6, and is recognized as a critical modulator of the cell cycle and a positive regulator of cell proliferation (30). Aberrant amplification and overexpression of CCND1 are a driving force in 13–20% of human breast cancer, and are associated with poor disease outcome (31). However, Mylona et al (32) suggested that CCND1 overexpression may serve as a marker for prolonged survival of patients with TNBC. Although the exact role of CCND1 in TNBC remains controversial, according to the findings of the present study, CCND1 overexpression was detected in TNBC tissues, and it acted as a hub gene in the PPI network of TNBC. These data implied that CCND1 is of clinical importance for TNBC.

HRAS, a member of the ras superfamily of genes, encodes for a 21-kDa protein (p21), which takes part in the signal transduction pathways that control proliferation and apoptosis, and regulate the cell cycle. Ras genes are involved in a wide variety of human tumor types and there is a known positive correlation between HRAS activation and breast cancer (33). In addition, previous studies (34,35) have shown that HRAS can induce invasion and migration of the breast cancer cell line, MCF10A. From the present study, HRAS may serve a significant role in the pathogenesis of TNBC and, given its functional significance in various types of cancer, it may also be a potential therapeutic target in the treatment of TNBC.

PCNA is well known as a coordinator of essential cellular processes for cell growth, death, and maintenance. Accumulated evidence suggests that an enhanced level of PCNA often correlates with carcinogenesis and that PCNA levels can be used as a prognostic marker in certain cases (3639). Yu et al (40) demonstrated that when PCNA activity was inhibited by a peptide, it suppressed the growth of TNBC cells. Given the critical function of PCNA in cancer growth, it is possible that the targeting of PCNA may be a viable therapeutic method for TNBC.

Pathways in cancer and pathways involving the cell cycle were demonstrated to be highly enriched in the DEGs for the PPI network and the subnetworks. All these pathways have been implicated in the development of breast cancer. Notably, the most significantly enriched pathways were pathways in cancer, which refers to some classical cancer pathways, including p53 signaling, Wnt signaling, and Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathways. Certain previous studies (4143) suggested that these critical signaling nodes, including p53, β-catenin and JAK/STAT3, can be used as therapeutic targets for the treatment of TNBC. In addition, cancer has been viewed as a cell cycle disease (44). Substantial evidence has indicated that the hub genes, P53, CCND1, HRAS and PCNA, are related to the pathways of the cell cycle, which is in accordance with the functions of networks identified in the present study.

In conclusion, 121 genes were revealed to be differentially expressed between TNBC and normal tissues by integrated analysis. Among them were TP53, GAPDH, CCND1, HRAS and PCNA, and these may be involved in TNBC progression via the cell cycle pathways and pathways in cancer. These findings may improve the understanding of the pathogenesis of TNBC and the development of targeted treatments for TNBC. Further experiments are required to confirm the findings of this work and the hypotheses put forward.

Acknowledgements

The current research was supported by the Institute of Genetic Engineering at the Southern Medical University (Guangzhou, China) and the present study was funded by the National Natural Science Foundation of China (grant no. 39880032), the Guangdong Foundation for Leading Talented Scientists (grant no. C1030925) and the China Postdoctoral Science Foundation Funded Project (grant no. 2016M602487). The authors would like to acknowledge the excellent technical assistance of Dr Kaiyuan Ji (School of Basic Medicine, Southern Medical University, China) and Dr Zijun Qiao (Institutes of Biomedical Sciences, Fudan University, China) during the present study.

References

1 

Dawson SJ, Provenzano E and Caldas C: Triple negative breast cancers: Clinical and prognostic implications. Eur J Cancer. 45:(Suppl 1). S27–S40. 2009. View Article : Google Scholar

2 

Reis-Filho JS and Tutt AN: Triple negative tumours: A critical review. Histopathology. 52:108–118. 2008. View Article : Google Scholar : PubMed/NCBI

3 

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

4 

Rakha EA, El-Sayed ME, Green AR, Lee AH, Robertson JF and Ellis IO: Prognostic markers in triple-negative breast cancer. Cancer. 109:25–32. 2007. View Article : Google Scholar : PubMed/NCBI

5 

Kapp AV, Jeffrey SS, Langerød A, Børresen-Dale AL, Han W, Noh DY, Bukholm IR, Nicolau M, Brown PO and Tibshirani R: Discovery and validation of breast cancer subtypes. BMC Genomics. 7:2312006. View Article : Google Scholar : PubMed/NCBI

6 

Mattie MD, Benz CC, Bowers J, Sensinger K, Wong L, Scott GK, Fedele V, Ginzinger D, Getts R and Haqq C: Optimized high-throughput microRNA expression profiling provides novel biomarker assessment of clinical prostate and breast cancer biopsies. Mol Cancer. 5:242006. View Article : Google Scholar : PubMed/NCBI

7 

Hu Z, Fan C, Oh DS, Marron JS, He X, Qaqish BF, Livasy C, Carey LA, Reynolds E, Dressler L, et al: The molecular portraits of breast tumors are conserved across microarray platforms. BMC Genomics. 7:962006. View Article : Google Scholar : PubMed/NCBI

8 

El-Rehim Abd DM, Ball G, Pinder SE, Rakha E, Paish C, Robertson JF, Macmillan D, Blamey RW and Ellis IO: High-throughput protein expression analysis using tissue microarray technology of a large well-characterised series identifies biologically distinct classes of breast cancer confirming recent cDNA expression analyses. Int J Cancer. 116:340–350. 2005. View Article : Google Scholar : PubMed/NCBI

9 

Sorlie T, Tibshirani R, Parker J, Hastie T, Marron JS, Nobel A, Deng S, Johnsen H, Pesich R, Geisler S, et al: Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA. 100:8418–8423. 2003. View Article : Google Scholar : PubMed/NCBI

10 

Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M, Jeffrey SS, et al: Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA. 98:10869–10874. 2001. View Article : Google Scholar : PubMed/NCBI

11 

Perou CM, Sørlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA, et al: Molecular portraits of human breast tumours. Nature. 406:747–752. 2000. View Article : Google Scholar : PubMed/NCBI

12 

Cascione L, Gasparini P, Lovat F, Carasi S, Pulvirenti A, Ferro A, Alder H, He G, Vecchione A, Croce CM, et al: Integrated microRNA and mRNA signatures associated with survival in triple negative breast cancer. PLoS One. 8:e559102013. View Article : Google Scholar : PubMed/NCBI

13 

Ma H, Morey R, O'Neil RC, He Y, Daughtry B, Schultz MD, Hariharan M, Nery JR, Castanon R, Sabatini K, et al: Abnormalities in human pluripotent cells due to reprogramming mechanisms. Nature. 511:177–183. 2014. View Article : Google Scholar : PubMed/NCBI

14 

Killian JK, Kim SY, Miettinen M, Smith C, Merino M, Tsokos M, Quezado M, Smith WI Jr, Jahromi MS, Xekouki P, et al: Succinate dehydrogenase mutation underlies global epigenomic divergence in gastrointestinal stromal tumor. Cancer Discov. 3:648–657. 2013. View Article : Google Scholar : PubMed/NCBI

15 

Sherman BT, da Huang W, Tan Q, Guo Y, Bour S, Liu D, Stephens R, Baseler MW, Lane HC and Lempicki RA: DAVID Knowledgebase: A gene-centered database integrating heterogeneous gene annotation resources to facilitate high-throughput gene functional analysis. BMC Bioinformatics. 8:4262007. View Article : Google Scholar : PubMed/NCBI

16 

Li S, Armstrong CM, Bertin N, Ge H, Milstein S, Boxem M, Vidalain PO, Han JD, Chesneau A, Hao T, et al: A map of the interactome network of the metazoan C. elegans. Science. 303:540–543. 2004. View Article : Google Scholar : PubMed/NCBI

17 

Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, et al: STRING v10: Protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43:D447–D452. 2015. View Article : Google Scholar : PubMed/NCBI

18 

Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B and Ideker T: Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13:2498–2504. 2003. View Article : Google Scholar : PubMed/NCBI

19 

Freed-Pastor WA and Prives C: Mutant p53: One name, many proteins. Genes Dev. 26:1268–1286. 2012. View Article : Google Scholar : PubMed/NCBI

20 

Kandoth C, McLellan MD, Vandin F, Ye K, Niu B, Lu C, Xie M, Zhang Q, McMichael JF, Wyczalkowski MA, et al: Mutational landscape and significance across 12 major cancer types. Nature. 502:333–339. 2013. View Article : Google Scholar : PubMed/NCBI

21 

Tennis M, Krishnan S, Bonner M, Ambrosone CB, Vena JE, Moysich K, Swede H, McCann S, Hall P, Shields PG and Freudenheim JL: p53 Mutation analysis in breast tumors by a DNA microarray method. Cancer Epidemiol Biomarkers Prev. 15:80–85. 2006. View Article : Google Scholar : PubMed/NCBI

22 

Hanby AM: Aspects of molecular phenotype and its correlations with breast cancer behaviour and taxonomy. Br J Cancer. 92:613–617. 2005. View Article : Google Scholar : PubMed/NCBI

23 

Li XR, Liu M, Zhang YJ, Wang JD, Zheng YQ, Li J, Ma B and Song X: CK5/6, EGFR, Ki-67, cyclin D1, and nm23-H1 protein expressions as predictors of pathological complete response to neoadjuvant chemotherapy in triple-negative breast cancer patients. Med Oncol. 28:(Suppl 1). S129–S134. 2011. View Article : Google Scholar : PubMed/NCBI

24 

Nishimura R and Arima N: Is triple negative a prognostic factor in breast cancer? Breast Cancer. 15:303–308. 2008. View Article : Google Scholar : PubMed/NCBI

25 

Avery-Kiejda KA, Morten B, Wong-Brown MW, Mathe A and Scott RJ: The relative mRNA expression of p53 isoforms in breast cancer is associated with clinical features and outcome. Carcinogenesis. 35:586–596. 2014. View Article : Google Scholar : PubMed/NCBI

26 

Caradec J, Sirab N, Revaud D, Keumeugni C and Loric S: Is GAPDH a relevant housekeeping gene for normalisation in colorectal cancer experiments? Br J Cancer. 103:1475–1476. 2010. View Article : Google Scholar : PubMed/NCBI

27 

Colell A, Green DR and Ricci JE: Novel roles for GAPDH in cell death and carcinogenesis. Cell Death Differ. 16:1573–1581. 2009. View Article : Google Scholar : PubMed/NCBI

28 

Altenberg B and Greulich KO: Genes of glycolysis are ubiquitously overexpressed in 24 cancer classes. Genomics. 84:1014–1020. 2004. View Article : Google Scholar : PubMed/NCBI

29 

Guo C, Liu S and Sun MZ: Novel insight into the role of GAPDH playing in tumor. Clin Transl Oncol. 15:167–172. 2013. View Article : Google Scholar : PubMed/NCBI

30 

Hadzisejdić I, Mustać E, Jonjić N, Petković M and Grahovac B: Nuclear EGFR in ductal invasive breast cancer: Correlation with cyclin-D1 and prognosis. Mod Pathol. 23:392–403. 2010. View Article : Google Scholar : PubMed/NCBI

31 

Courjal F, Cuny M, Simony-Lafontaine J, Louason G, Speiser P, Zeillinger R, Rodriguez C and Theillet C: Mapping of DNA amplifications at 15 chromosomal localizations in 1875 breast tumors: Definition of phenotypic groups. Cancer Res. 57:4360–4367. 1997.PubMed/NCBI

32 

Mylona E, Tzelepis K, Theohari I, Giannopoulou I, Papadimitriou C and Nakopoulou L: Cyclin D1 in invasive breast carcinoma: Favourable prognostic significance in unselected patients and within subgroups with an aggressive phenotype. Histopathology. 62:472–480. 2013. View Article : Google Scholar : PubMed/NCBI

33 

Watson DM, Elton RA, Jack WJ, Dixon JM, Chetty U and Miller WR: The H-ras oncogene product p21 and prognosis in human breast cancer. Breast Cancer Res Treat. 17:161–169. 1991. View Article : Google Scholar : PubMed/NCBI

34 

Moon A, Kim MS, Kim TG, Kim SH, Kim HE, Chen YQ and Kim HR: H-ras, but not N-ras, induces an invasive phenotype in human breast epithelial cells: A role for MMP-2 in the H-ras-induced invasive phenotype. Int J Cancer. 85:176–181. 2000. View Article : Google Scholar : PubMed/NCBI

35 

Yong HY, Hwang JS, Son H, Park HI, Oh ES, Kim HH, Kim DK, Choi WS, Lee BJ, Kim HR and Moon A: Identification of H-Ras-specific motif for the activation of invasive signaling program in human breast epithelial cells. Neoplasia. 13:98–107. 2011. View Article : Google Scholar : PubMed/NCBI

36 

Malkas LH, Herbert BS, Abdel-Aziz W, Dobrolecki LE, Liu Y, Agarwal B, Hoelz D, Badve S, Schnaper L, Arnold RJ, et al: A cancer-associated PCNA expressed in breast cancer has implications as a potential biomarker. Proc Natl Acad Sci USA. 103:19472–19477. 2006. View Article : Google Scholar : PubMed/NCBI

37 

Bukholm IR, Bukholm G, Holm R and Nesland JM: Association between histology grade, expression of HsMCM2, and cyclin A in human invasive breast carcinomas. J Clin Pathol. 56:368–373. 2003. View Article : Google Scholar : PubMed/NCBI

38 

Lee JS, Kim HS, Jung JJ, Kim YB, Park CS and Lee MC: Correlation between angiogenesis, apoptosis and cell proliferation in invasive ductal carcinoma of the breast and their relation to tumor behavior. Anal Quant Cytol Histol. 23:161–168. 2001.PubMed/NCBI

39 

Aziz SA, Pervez S, Khan SM, Kayani N and Nasir MI: Prognostic value of proliferating cell nuclear antigen (PCNA) in infiltrating ductal carcinoma breast. J Coll Physicians Surg Pak. 15:225–229. 2005.PubMed/NCBI

40 

Yu YL, Chou RH, Liang JH, Chang WJ, Su KJ, Tseng YJ, Huang WC, Wang SC and Hung MC: Targeting the EGFR/PCNA signaling suppresses tumor growth of triple-negative breast cancer cells with cell-penetrating PCNA peptides. PLoS One. 8:e613622013. View Article : Google Scholar : PubMed/NCBI

41 

Turner N, Moretti E, Siclari O, Migliaccio I, Santarpia L, D'Incalci M, Piccolo S, Veronesi A, Zambelli A, Del Sal G and Di Leo A: Targeting triple negative breast cancer: Is p53 the answer? Cancer Treat Rev. 39:541–550. 2013. View Article : Google Scholar : PubMed/NCBI

42 

Dey N, Barwick BG, Moreno CS, Ordanic-Kodani M, Chen Z, Oprea-Ilies G, Tang W, Catzavelos C, Kerstann KF, Sledge GW Jr, et al: Wnt signaling in triple negative breast cancer is associated with metastasis. BMC Cancer. 13:5372013. View Article : Google Scholar : PubMed/NCBI

43 

Poage GM, Hartman ZC and Brown PH: Revealing targeted therapeutic opportunities in triple-negative breast cancers: A new strategy. Cell Cycle. 12:2705–2706. 2013. View Article : Google Scholar : PubMed/NCBI

44 

Sherr CJ: Cancer cell cycles. Science. 274:1672–1677. 1996. View Article : Google Scholar : PubMed/NCBI

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March 2017
Volume 15 Issue 3

Print ISSN: 1791-2997
Online ISSN:1791-3004

2015 Impact Factor: 1.559
Ranked #48/123 Medicine Research and Experimental
(total number of cites)

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APA
Peng, C., Ma, W., Xia, W., & Zheng, W. (2017). Integrated analysis of differentially expressed genes and pathways in triple‑negative breast cancer. Molecular Medicine Reports, 15, 1087-1094. http://dx.doi.org/10.3892/mmr.2017.6101
MLA
Peng, C., Ma, W., Xia, W., Zheng, W."Integrated analysis of differentially expressed genes and pathways in triple‑negative breast cancer". Molecular Medicine Reports 15.3 (2017): 1087-1094.
Chicago
Peng, C., Ma, W., Xia, W., Zheng, W."Integrated analysis of differentially expressed genes and pathways in triple‑negative breast cancer". Molecular Medicine Reports 15, no. 3 (2017): 1087-1094. http://dx.doi.org/10.3892/mmr.2017.6101