Identification of key genes as potential biomarkers for triple‑negative breast cancer using integrating genomics analysis

Zhong,Guansheng; Lou,Weiyang; Shen,Qinyan; Yu,Kun; Zheng,Yajuan

doi:10.3892/mmr.2019.10867

Identification of key genes as potential biomarkers for triple‑negative breast cancer using integrating genomics analysis

Authors:
- Guansheng Zhong
- Weiyang Lou
- Qinyan Shen
- Kun Yu
- Yajuan Zheng
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Affiliations: Department of Thyroid and Breast Surgery, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, Zhejiang 310014, P.R. China, Program of Innovative Therapeutics, First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang 310014, P.R. China, Department of Surgical Oncology, Dongyang People's Hospital of Zhejiang, Dongyang, Zhejiang 322100, P.R. China
Published online on: December 6, 2019 https://doi.org/10.3892/mmr.2019.10867
Pages: 557-566
Copyright: © Zhong et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Triple‑negative breast cancer (TNBC) accounts for the worst prognosis of all types of breast cancers due to a high risk of recurrence and a lack of targeted therapeutic options. Extensive effort is required to identify novel targets for TNBC. In the present study, a robust rank aggregation (RRA) analysis based on genome‑wide gene expression datasets involving TNBC patients from the Gene Expression Omnibus (GEO) database was performed to identify key genes associated with TNBC. A total of 194 highly ranked differentially expressed genes (DEGs) were identified in TNBC vs. non‑TNBC. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analysis was utilized to explore the biological functions of the identified genes. These DEGs were mainly involved in the biological processes termed positive regulation of transcription from RNA polymerase II promoter, negative regulation of apoptotic process, response to drug, response to estradiol and negative regulation of cell growth. Genes were mainly involved in the KEGG pathway termed estrogen signaling pathway. The aberrant expression of several randomly selected DEGs were further validated in cell lines, clinical tissues and The Cancer Genome Atlas (TCGA) cohort. Furthermore, all the top‑ranked DEGs underwent survival analysis using TCGA database, of which overexpression of 4 genes (FABP7, ART3, CT83, and TTYH1) were positively correlated to the life expectancy (P<0.05) of TNBC patients. In addition, a model consisting of two genes (FABP7 and CT83) was identified to be significantly associated with the overall survival (OS) of TNBC patients by means of Cox regression, Kaplan‑Meier, and receiver operating characteristic (ROC) analyses. In conclusion, the present study identified a number of key genes as potential biomarkers involved in TNBC, which provide novel insights into the tumorigenesis of TNBC at the gene level and may serve as independent prognostic factors for TNBC prognosis.

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1	Siegel RL, Miller KD and Jemal A: Cancer statistics, 2018. CA Cancer J Clin. 68:7–30. 2018. View Article : Google Scholar : PubMed/NCBI
2	Foulkes WD, Smith IE and Reis-Filho JS: Triple-negative breast cancer. N Engl J Med. 363:1938–1948. 2010. View Article : Google Scholar : PubMed/NCBI
3	Dent R, Trudeau M, Pritchard KI, Hanna WM, Kahn HK, Sawka CA, Lickley LA, Rawlinson E, Sun P and Narod SA: Triple-negative breast cancer: Clinical features and patterns of recurrence. Clin Cancer Res. 13:4429–4434. 2007. View Article : Google Scholar : PubMed/NCBI
4	Carey LA, Perou CM, Livasy CA, Dressler LG, Cowan D, Conway K, Karaca G, Troester MA, Tse CK, Edmiston S, et al: Race, breast cancer subtypes, and survival in the Carolina Breast Cancer Study. JAMA. 295:2492–2502. 2006. View Article : Google Scholar : PubMed/NCBI
5	Carey L, Winer E, Viale G, Cameron D and Gianni L: Triple-negative breast cancer: Disease entity or title of convenience? Nat Rev Clin Oncol. 7:683–692. 2010. View Article : Google Scholar : PubMed/NCBI
6	Rouzier R, Perou CM, Symmans WF, Ibrahim N, Cristofanilli M, Anderson K, Hess KR, Stec J, Ayers M, Wagner P, et al: Breast cancer molecular subtypes respond differently to preoperative chemotherapy. Clin Cancer Res. 11:5678–5685. 2005. View Article : Google Scholar : PubMed/NCBI
7	von Minckwitz G, Untch M, Blohmer JU, Costa SD, Eidtmann H, Fasching PA, Gerber B, Eiermann W, Hilfrich J, Huober J, et al: Definition and impact of pathologic complete response on prognosis after neoadjuvant chemotherapy in various intrinsic breast cancer subtypes. J Clin Oncol. 30:1796–1804. 2012. View Article : Google Scholar : PubMed/NCBI
8	Byler S, Goldgar S, Heerboth S, Leary M, Housman G, Moulton K and Sarkar S: Genetic and epigenetic aspects of breast cancer progression and therapy. Anticancer Res. 34:1071–1077. 2014.PubMed/NCBI
9	Lehmann BD, Bauer JA, Chen X, Sanders ME, Chakravarthy AB, Shyr Y and Pietenpol JA: Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J Clin Invest. 121:2750–2767. 2011. View Article : Google Scholar : PubMed/NCBI
10	Burstein MD, Tsimelzon A, Poage GM, Covington KR, Contreras A, Fuqua SA, Savage MI, Osborne CK, Hilsenbeck SG, Chang JC, et al: Comprehensive genomic analysis identifies novel subtypes and targets of triple-negative breast cancer. Clin Cancer Res. 21:1688–1698. 2015. View Article : Google Scholar : PubMed/NCBI
11	Võsa U, Kolde R, Vilo J, Metspalu A and Annilo T: Comprehensive meta-analysis of microRNA expression using a robust rank aggregation approach. Methods Mol Biol. 1182:361–373. 2014. View Article : Google Scholar : PubMed/NCBI
12	Kolde R, Laur S, Adler P and Vilo J: Robust rank aggregation for gene list integration and meta-analysis. Bioinformatics. 28:573–580. 2012. View Article : Google Scholar : PubMed/NCBI
13	Shi KQ, Lin Z, Chen XJ, Song M, Wang YQ, Cai YJ, Yang NB, Zheng MH, Dong JZ, Zhang L and Chen YP: Hepatocellular carcinoma associated microRNA expression signature: Integrated bioinformatics analysis, experimental validation and clinical significance. Oncotarget. 6:25093–25108. 2015. View Article : Google Scholar : PubMed/NCBI
14	Chin L, Andersen JN and Futreal PA: Cancer genomics: From discovery science to personalized medicine. Nat Med. 17:297–303. 2011. View Article : Google Scholar : PubMed/NCBI
15	Curtis C, Shah SP, Chin SF, Turashvili G, Rueda OM, Dunning MJ, Speed D, Lynch AG, Samarajiwa S, Yuan Y, et al: The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature. 486:346–352. 2012. View Article : Google Scholar : PubMed/NCBI
16	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
17	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
18	Kalyuga M, Gallego-Ortega D, Lee HJ, Roden DL, Cowley MJ, Caldon CE, Stone A, Allerdice SL, Valdes-Mora F, Launchbury R, et al: ELF5 suppresses estrogen sensitivity and underpins the acquisition of antiestrogen resistance in luminal breast cancer. PLoS Biol. 10:e10014612012. View Article : Google Scholar : PubMed/NCBI
19	Furth PA, Nakles RE, Millman S, Diaz-Cruz ES and Cabrera MC: Signal transducer and activator of transcription 5 as a key signaling pathway in normal mammary gland developmental biology and breast cancer. Breast Cancer Res. 13:2202011. View Article : Google Scholar : PubMed/NCBI
20	Zhou L, Yan T, Jiang Y, Di G, Shen Z, Shao Z and Lu J: Prognostic and predictive value of TFF1 for adjuvant endocrine therapy in Chinese women with early ER positive breast cancer: Comparing aromatase inhibitors with tamoxifen. Breast. 20:15–20. 2011. View Article : Google Scholar : PubMed/NCBI
21	Prest SJ, May FE and Westley BR: The estrogen-regulated protein, TFF1, stimulates migration of human breast cancer cells. FASEB J. 16:592–594. 2002. View Article : Google Scholar : PubMed/NCBI
22	May FE and Westley BR: TFF3 is a valuable predictive biomarker of endocrine response in metastatic breast cancer. Endocr Relat Cancer. 22:465–479. 2015. View Article : Google Scholar : PubMed/NCBI
23	Ishibashi Y, Ohtsu H, Ikemura M, Kikuchi Y, Niwa T, Nishioka K, Uchida Y, Miura H, Aikou S, Gunji T, et al: Serum TFF1 and TFF3 but not TFF2 are higher in women with breast cancer than in women without breast cancer. Sci Rep. 7:48462017. View Article : Google Scholar : PubMed/NCBI
24	Abramson VG, Lehmann BD, Ballinger TJ and Pietenpol JA: Subtyping of triple-negative breast cancer: Implications for therapy. Cancer. 121:8–16. 2015. View Article : Google Scholar : PubMed/NCBI
25	Podo F, Buydens LM, Degani H, Hilhorst R, Klipp E, Gribbestad IS, Van Huffel S, van Laarhoven HW, Luts J, Monleon D, et al: Triple-negative breast cancer: Present challenges and new perspectives. Mol Oncol. 4:209–229. 2010. View Article : Google Scholar : PubMed/NCBI
26	Liedtke C, Mazouni C, Hess KR, André F, Tordai A, Mejia JA, Symmans WF, Gonzalez-Angulo AM, Hennessy B, Green M, et al: Response to neoadjuvant therapy and long-term survival in patients with triple-negative breast cancer. J Clin Oncol. 26:1275–1281. 2008. View Article : Google Scholar : PubMed/NCBI
27	Iwamoto T, Bianchini G, Booser D, Qi Y, Coutant C, Shiang CY, Santarpia L, Matsuoka J, Hortobagyi GN, Symmans WF, et al: Gene pathways associated with prognosis and chemotherapy sensitivity in molecular subtypes of breast cancer. J Natl Cancer Inst. 103:264–272. 2011. View Article : Google Scholar : PubMed/NCBI
28	Tan L, Song X, Sun X, Wang N, Qu Y and Sun Z: ART3 regulates triple-negative breast cancer cell function via activation of Akt and ERK pathways. Oncotarget. 7:46589–46602. 2016. View Article : Google Scholar : PubMed/NCBI
29	Liu RZ, Graham K, Glubrecht DD, Lai R, Mackey JR and Godbout R: A fatty acid-binding protein 7/RXRβ pathway enhances survival and proliferation in triple-negative breast cancer. J Pathol. 228:310–321. 2012. View Article : Google Scholar : PubMed/NCBI
30	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
31	Fritzsche FR, Dahl E, Pahl S, Burkhardt M, Luo J, Mayordomo E, Gansukh T, Dankof A, Knuechel R, Denkert C, et al: Prognostic relevance of AGR2 expression in breast cancer. Clin Cancer Res. 12:1728–1734. 2006. View Article : Google Scholar : PubMed/NCBI
32	Wright TM, Wardell SE, Jasper JS, Stice JP, Safi R, Nelson ER and McDonnell DP: Delineation of a FOXA1/ERalpha/AGR2 regulatory loop that is dysregulated in endocrine therapy-resistant breast cancer. Mol Cancer Res. 12:1829–1839. 2014. View Article : Google Scholar : PubMed/NCBI
33	Soutto M, Peng D, Katsha A, Chen Z, Piazuelo MB, Washington MK, Belkhiri A, Correa P and El-Rifai W: Activation of β-catenin signalling by TFF1 loss promotes cell proliferation and gastric tumorigenesis. Gut. 64:1028–1039. 2015. View Article : Google Scholar : PubMed/NCBI
34	Buache E, Etique N, Alpy F, Stoll I, Muckensturm M, Reina-San-Martin B, Chenard MP, Tomasetto C and Rio MC: Deficiency in trefoil factor 1 (TFF1) increases tumorigenicity of human breast cancer cells and mammary tumor development in TFF1-knockout mice. Oncogene. 30:3261–3273. 2011. View Article : Google Scholar : PubMed/NCBI