Bioinformatics analysis of gene expression data for the identification of critical genes in breast invasive carcinoma

Li,Yi; Wang,Yongsheng

doi:10.3892/mmr.2017.7717

Bioinformatics analysis of gene expression data for the identification of critical genes in breast invasive carcinoma

Authors:
- Yi Li
- Yongsheng Wang
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Affiliations: Department of Thoracic Oncology, Cancer Center, and State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan 610000, P.R. China
Published online on: October 4, 2017 https://doi.org/10.3892/mmr.2017.7717
Pages: 8657-8664
Copyright: © Li et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Gene expression data were analyzed in order to identify critical genes in breast invasive carcinoma (BRCA). Data from 1,073 BRCA samples and 99 normal samples were analyzed, which were obtained from The Cancer Genome Atlas. Differentially expressed genes (DEGs) were identified using the significance analysis of microarrays method and a functional enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery. Relevant microRNAs (miRNAs), transcription factors (TFs) and associated small molecule drugs were revealed by Fisher's exact test. Furthermore, protein‑protein interaction (PPI) information was downloaded from the Human Protein Reference Database. Interactions with a Pearson's correlation coefficient >0.5 were identified and PPI networks were subsequently constructed. A survival analysis was also conducted according to the Kaplan‑Meier method. Initially, the 1,073 BRCA samples were clustered into seven groups, and 5,394 DEGs that were identified in ≥4 groups were selected. These DEGs were involved in the cell cycle, ubiquitin‑mediated proteolysis, oxidative phosphorylation and human immunodeficiency virus infection. In addition, TFs, including Sp1 transcription factor, DAN domain BMP antagonist family member 5, MYCN proto‑oncogene, bHLH transcription factor and cAMP responsive element binding protein (CREB)1, were identified in the BRCA groups. Seven PPI networks were subsequently constructed and the top 10 hub genes were acquired, including RB transcriptional corepressor 1, inhibitor of nuclear factor (NF)‑κB kinase subunit γ, NF‑κB subunit 2, transporter 1, ATP binding cassette subfamily B member, CREB binding protein and proteasome subunit α3. A significant difference in survival was observed between the two combined groups (groups‑2, ‑4 and ‑5 vs. groups‑1, ‑3, ‑6 and ‑7). In conclusion, numerous critical genes were detected in BRCA, and relevant miRNAs, TFs and small molecule drugs were identified. These findings may advance understanding regarding the pathogenesis of BRCA.

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1	McGuire A, Brown JA, Malone C, McLaughlin R and Kerin MJ: Effects of age on the detection and management of breast cancer. Cancers (Basel). 7:908–929. 2015. View Article : Google Scholar : PubMed/NCBI
2	DeSantis C, Ma J, Bryan L and Jemal A: Breast cancer statistics, 2013. CA Cancer J Clin. 64:52–62. 2014. View Article : Google Scholar : PubMed/NCBI
3	McGuire S: World Cancer Report 2014. Geneva, Switzerland: World Health Organization, International agency for research on cancer, WHO press, 2015. Adv Nutr. 7:418–419. 2016. View Article : Google Scholar : PubMed/NCBI
4	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
5	Scully OJ, Bay BH, Yip G and Yu Y: Breast cancer metastasis. Cancer Genomics Proteomics. 9:311–320. 2012.PubMed/NCBI
6	Petit T, Dufour P and Tannock I: A critical evaluation of the role of aromatase inhibitors as adjuvant therapy for postmenopausal women with breast cancer. Endocr Relat Cancer. 18:R79–R89. 2011. View Article : Google Scholar : PubMed/NCBI
7	Fu M, Maresh EL, Helguera GF, Kiyohara M, Qin Y, Ashki N, Daniels-Wells TR, Aziz N, Gordon LK, Braun J, et al: Rationale and preclinical efficacy of a novel anti-EMP2 antibody for the treatment of invasive breast cancer. Mol Cancer Ther. 13:902–915. 2014. View Article : Google Scholar : PubMed/NCBI
8	Taherian-Fard A, Srihari S and Ragan MA: Breast cancer classification: Linking molecular mechanisms to disease prognosis. Brief Bioinform. 16:461–474. 2015. View Article : Google Scholar : PubMed/NCBI
9	Drabsch Y and ten Dijke P: TGF-β signaling in breast cancer cell invasion and bone metastasis. J Mammary Gland Biol Neoplasia. 16:97–108. 2011. View Article : Google Scholar : PubMed/NCBI
10	Dimeo TA, Kristen A, Pushkar P, Fan C, Perou CM, Naber S and Kuperwasser C: A novel lung metastasis signature links Wnt signaling with cancer cell self-renewal and epithelial-mesenchymal transition in basal-like breast cancer. Cancer Res. 69:5364–5373. 2009. View Article : Google Scholar : PubMed/NCBI
11	Sethi N, Dai X, Winter CG and Kang Y: Tumor-derived JAGGED1 promotes osteolytic bone metastasis of breast cancer by engaging notch signaling in bone cells. Cancer Cell. 19:192–205. 2011. View Article : Google Scholar : PubMed/NCBI
12	Du WW, Fang L, Li M, Yang X, Liang Y, Peng C, Qian W, O'Malley YQ, Askeland RW, Sugg SL, et al: MicroRNA miR-24 enhances tumor invasion and metastasis by targeting PTPN9 and PTPRF to promote EGF signaling. J Cell Sci. 126:1440–1453. 2013. View Article : Google Scholar : PubMed/NCBI
13	Washam CL, Byrum SD, Leitzel K, Ali SM, Tackett AJ, Gaddy D, Sundermann SE, Lipton A and Suva LJ: Identification of PTHrP(12–48) as a plasma biomarker associated with breast cancer bone metastasis. Cancer Epidemiol Biomarkers Prev. 22:972–983. 2013. View Article : Google Scholar : PubMed/NCBI
14	Cabioglu N, Yazici MS, Arun B, Broglio KR, Hortobagyi GN, Price JE and Sahin A: CCR7 and CXCR4 as novel biomarkers predicting axillary lymph node metastasis in T1 breast cancer. Clin Cancer Res. 11:5686–5693. 2005. View Article : Google Scholar : PubMed/NCBI
15	Valastyan S, Reinhardt F, Benaich N, Calogrias D, Szász AM, Wang ZC, Brock JE, Richardson AL and Weinberg RA: A pleiotropically acting microRNA, miR-31, inhibits breast cancer metastasis. Cell. 137:1032–1046. 2009. View Article : Google Scholar : PubMed/NCBI
16	Hurst DR, Edmonds MD, Scott GK, Benz CC, Vaidya KS and Welch DR: Breast cancer metastasis suppressor 1 up-regulates miR-146, which suppresses breast cancer metastasis. Cancer Res. 69:1279–1283. 2009. View Article : Google Scholar : PubMed/NCBI
17	Zhu Y, Qiu P and Ji Y: TCGA-assembler: Open-source software for retrieving and processing TCGA data. Nat Methods. 11:599–600. 2014. View Article : Google Scholar : PubMed/NCBI
18	Rossini PM, Barker AT, Berardelli A, Caramia MD, Caruso G, Cracco RQ, Dimitrijević MR, Hallett M, Katayama Y, Lücking CH, et al: Non-invasive electrical and magnetic stimulation of the brain, spinal cord and roots: Basic principles and procedures for routine clinical application. Report of an IFCN committee. Electroencephalogr Clin Neurophysiol. 91:79–92. 1994. View Article : Google Scholar : PubMed/NCBI
19	Farris JS: On the cophenetic correlation coefficient. Syst Biol. 18:279–285. 1969.
20	Larsson O, Wahlestedt C and Timmons JA: Considerations when using the significance analysis of microarrays (SAM) algorithm. BMC Bioinformatics. 6:1292005. View Article : Google Scholar : PubMed/NCBI
21	Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC and Lempicki RA: DAVID: Database for annotation, visualization, and integrated discovery. Genome Biol. 4:P32003. View Article : Google Scholar : PubMed/NCBI
22	Kim KI and van de Wiel MA: Effects of dependence in high-dimensional multiple testing problems. BMC Bioinformatics. 9:1142008. View Article : Google Scholar : PubMed/NCBI
23	Haw R and Stein L: Using the reactome database. Curr Protoc Bioinformatics. 8:Unit8.72012.PubMed/NCBI
24	Tu K, Yu H, Hua YJ, Li YY, Liu L, Xie L and Li YX: Combinatorial network of primary and secondary microRNA-driven regulatory mechanisms. Nucleic Acids Res. 37:5969–5980. 2009. View Article : Google Scholar : PubMed/NCBI
25	Yu H, Tu K, Wang YJ, Mao JZ, Xie L, Li YY and Li YX: Combinatorial network of transcriptional regulation and microRNA regulation in human cancer. BMC Syst Biol. 6:612012. View Article : Google Scholar : PubMed/NCBI
26	Liao YY, Lee TS and Lin YM: A Fisher exact test will be more proper. Radiology. 239:300–301. 2006. View Article : Google Scholar : PubMed/NCBI
27	Prasad TS Keshava, Goel R, Kandasamy K, Keerthikumar S, Kumar S, Mathivanan S, Telikicherla D, Raju R, Shafreen B, Venugopal A, et al: Human protein reference database-2009 update. Nucleic Acids Res. 37(Database issue): D767–D772. 2009. View Article : Google Scholar : PubMed/NCBI
28	Jang KW, Lee KH, Kim SH, Jin T, Choi EY, Jeon HJ, Kim E, Han YS and Chung JH: Ubiquitin ligase CHIP induces TRAF2 proteasomal degradation and NF-κB inactivation to regulate breast cancer cell invasion. J Cell Biochem. 112:3612–3620. 2011. View Article : Google Scholar : PubMed/NCBI
29	Sun LL, Wang J, Zhao ZJ, Liu N, Wang AL, Ren HY, Yang F, Diao KX, Fu WN, Wan EH and Mi XY: Suppressive role of miR-502-5p in breast cancer via downregulation of TRAF2. Oncol Rep. 31:2085–2092. 2014. View Article : Google Scholar : PubMed/NCBI
30	Moroni M, Soldatenkov V, Zhang L, Zhang Y, Stoica G, Gehan E, Rashidi B, Singh B, Ozdemirli M and Mueller SC: Progressive loss of Syk and abnormal proliferation in breast cancer cells. Cancer Res. 64:7346–7354. 2004. View Article : Google Scholar : PubMed/NCBI
31	Toyama T, Iwase H, Yamashita H, Hara Y, Omoto Y, Sugiura H, Zhang Z and Fujii Y: Reduced expression of the Syk gene is correlated with poor prognosis in human breast cancer. Cancer Lett. 189:97–102. 2003. View Article : Google Scholar : PubMed/NCBI
32	Zhang X, Shrikhande U, Alicie BM, Zhou Q and Geahlen RL: Role of the protein tyrosine kinase Syk in regulating cell-cell adhesion and motility in breast cancer cells. Mol Cancer Res. 7:634–644. 2009. View Article : Google Scholar : PubMed/NCBI
33	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:pp. 19472–19477. 2006; View Article : Google Scholar : PubMed/NCBI
34	He X, Arslan AD, Ho TT, Yuan C, Stampfer MR and Beck WT: Involvement of polypyrimidine tract-binding protein (PTBP1) in maintaining breast cancer cell growth and malignant properties. Oncogenesis. 3:e842014. View Article : Google Scholar : PubMed/NCBI
35	Arslan AD, Asztalos S, Stampfer M, Tonetti D, He X and Beck WT: PTBP1 stability is increased in ovarian and breast cancer cell lines compared to matched controls. Cancer Res. 73:32012013. View Article : Google Scholar
36	Elias D, Vever H, Lænkholm AV, Gjerstorff MF, Yde CW, Lykkesfeldt AE and Ditzel HJ: Gene expression profiling identifies FYN as an important molecule in tamoxifen resistance and a predictor of early recurrence in patients treated with endocrine therapy. Oncogene. 34:1919–1927. 2015. View Article : Google Scholar : PubMed/NCBI
37	Yadav V and Denning MF: Fyn is induced by Ras/PI3K/Akt signaling and is required for enhanced invasion/migration. Mol Carcinog. 50:346–352. 2011. View Article : Google Scholar : PubMed/NCBI
38	Jiang Z, Jones R, Liu JC, Deng T, Robinson T, Chung PE, Wang S, Herschkowitz JI, Egan SE, Perou CM and Zacksenhaus E: RB1 and p53 at the crossroad of EMT and triple-negative breast cancer. Cell Cycle. 10:1563–1570. 2011. View Article : Google Scholar : PubMed/NCBI
39	Robinson TJ, Liu JC, Vizeacoumar F, Sun T, Maclean N, Egan SE, Schimmer AD, Datti A and Zacksenhaus E: RB1 status in triple negative breast cancer cells dictates response to radiation treatment and selective therapeutic drugs. PLoS One. 8:e786412013. View Article : Google Scholar : PubMed/NCBI
40	Chano T, Ikebuchi K, Tomita Y, Jin Y, Inaji H, Ishitobi M, Teramoto K, Ochi Y, Tameno H, Nishimura I, et al: RB1CC1 together with RB1 and p53 predicts long-term survival in Japanese breast cancer patients. PLoS One. 5:e157372010. View Article : Google Scholar : PubMed/NCBI
41	Lerebours F, Vacher S, Andrieu C, Espie M, Marty M, Lidereau R and Bieche I: NF-kappa B genes have a major role in inflammatory breast cancer. BMC Cancer. 8:412008. View Article : Google Scholar : PubMed/NCBI
42	Pan Y, Wang R, Zhang F, Chen Y, Lv Q, Long G and Yang K: MicroRNA-130a inhibits cell proliferation, invasion and migration in human breast cancer by targeting the RAB5A. Int J Clin Exp Pathol. 8:384–393. 2015.PubMed/NCBI
43	Liu P, Tang H, Chen B, He Z, Deng M, Wu M, Liu X, Yang L, Ye F and Xie X: miR-26a suppresses tumour proliferation and metastasis by targeting metadherin in triple negative breast cancer. Cancer Lett. 357:384–392. 2015. View Article : Google Scholar : PubMed/NCBI
44	Fu J, Xu X, Kang L, Zhou L, Wang S, Lu J, Cheng L, Fan Z, Yuan B, Tian P, et al: miR-30a suppresses breast cancer cell proliferation and migration by targeting Eya2. Biochem Biophys Res Commun. 445:314–319. 2014. View Article : Google Scholar : PubMed/NCBI
45	Wu ZS, Wu Q, Wang CQ, Wang XN, Huang J, Zhao JJ, Mao SS, Zhang GH, Xu XC and Zhang N: miR-340 inhibition of breast cancer cell migration and invasion through targeting of oncoprotein c-Met. Cancer. 117:2842–2852. 2011. View Article : Google Scholar : PubMed/NCBI
46	Ma F, Zhang J, Zhong L, Wang L, Liu Y, Wang Y, Peng L and Guo B: Upregulated microRNA-301a in breast cancer promotes tumor metastasis by targeting PTEN and activating Wnt/β-catenin signaling. Gene. 535:191–197. 2014. View Article : Google Scholar : PubMed/NCBI
47	Arora H, Qureshi R and Park WY: miR-506 regulates epithelial mesenchymal transition in breast cancer cell lines. PLoS One. 8:e642732013. View Article : Google Scholar : PubMed/NCBI
48	Wang F, Lv P, Liu X, Zhu M and Qiu X: MicroRNA-214 enhances the invasion ability of breast cancer cells by targeting p53. Int J Mol Med. 35:1395–1402. 2015. View Article : Google Scholar : PubMed/NCBI
49	Inoue A, Omoto Y, Yamaguchi Y, Kiyama R and Hayashi SI: Transcription factor EGR3 is involved in the estrogen-signaling pathway in breast cancer cells. J Mol Endocrinol. 32:649–661. 2004. View Article : Google Scholar : PubMed/NCBI
50	Zhang SY, Liu SC, Al-Saleem LF, Holloran D, Babb J, Guo X and Klein-Szanto AJ: E2F-1: A proliferative marker of breast neoplasia. Cancer Epidemiol Biomarkers Prev. 9:395–401. 2000.PubMed/NCBI
51	Booy EP, Henson ES and Gibson SB: Epidermal growth factor regulates Mcl-1 expression through the MAPK-Elk-1 signalling pathway contributing to cell survival in breast cancer. Oncogene. 30:2367–2378. 2011. View Article : Google Scholar : PubMed/NCBI
52	Engel RH, Brown JA, Von Roenn JH, O'Regan RM, Bergan R, Badve S, Rademaker A and Gradishar WJ: A phase II study of single agent bortezomib in patients with metastatic breast cancer: A single institution experience. Cancer Invest. 25:733–737. 2007. View Article : Google Scholar : PubMed/NCBI
53	Schmid P, Kühnhardt D, Kiewe P, Lehenbauer-Dehm S, Schippinger W, Greil R, Lange W, Preiss J, Niederle N, Brossart P, et al: A phase I/II study of bortezomib and capecitabine in patients with metastatic breast cancer previously treated with taxanes and/or anthracyclines. Ann Oncol. 19:871–876. 2008. View Article : Google Scholar : PubMed/NCBI
54	Burstein HJ, Elias AD, Rugo HS, Cobleigh MA, Wolff AC, Eisenberg PD, Lehman M, Adams BJ, Bello CL, DePrimo SE, et al: Phase II study of sunitinib malate, an oral multitargeted tyrosine kinase inhibitor, in patients with metastatic breast cancer previously treated with an anthracycline and a taxane. J Clin Oncol. 26:1810–1816. 2008. View Article : Google Scholar : PubMed/NCBI