Application of a co‑expression network for the analysis of aggressive and non‑aggressive breast cancer cell lines to predict the clinical outcome of patients

Guo,Ling; Zhang,Kun; Bing,Zhitong

doi:10.3892/mmr.2017.7608

Application of a co‑expression network for the analysis of aggressive and non‑aggressive breast cancer cell lines to predict the clinical outcome of patients

Authors:
- Ling Guo
- Kun Zhang
- Zhitong Bing
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Affiliations: College of Electrical Engineering, Northwest University for Nationalities, Lanzhou, Gansu 730030, P.R. China, Evidence Based Medicine Center, School of Basic Medical Science of Lanzhou University, Lanzhou, Gansu 730000, P.R. China
Published online on: September 25, 2017 https://doi.org/10.3892/mmr.2017.7608
Pages: 7967-7978
Copyright: © Guo et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Breast cancer metastasis is a demanding problem in clinical treatment of patients with breast cancer. It is necessary to examine the mechanisms of metastasis for developing therapies. Classification of the aggressiveness of breast cancer is an important issue in biological study and for clinical decisions. Although aggressive and non‑aggressive breast cancer cells can be easily distinguished among different cell lines, it is very difficult to distinguish in clinical practice. The aim of the current study was to use the gene expression analysis from breast cancer cell lines to predict clinical outcomes of patients with breast cancer. Weighted gene co‑expression network analysis (WGCNA) is a powerful method to account for correlations between genes and extract co‑expressed modules of genes from large expression datasets. Therefore, WGCNA was applied to explore the differences in sub‑networks between aggressive and non‑aggressive breast cancer cell lines. The greatest difference topological overlap networks in both groups include potential information to understand the mechanisms of aggressiveness. The results show that the blue and red modules were significantly associated with the biological processes of aggressiveness. The sub‑network, which consisted of TMEM47, GJC1, ANXA3, TWIST1 and C19orf33 in the blue module, was associated with an aggressive phenotype. The sub‑network of LOC100653217, CXCL12, SULF1, DOK5 and DKK3 in the red module was associated with a non‑aggressive phenotype. In order to validate the hazard ratio of these genes, the prognostic index was constructed to integrate them and examined using data from the Cancer Genomic Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Patients with breast cancer from TCGA in the high‑risk group had a significantly shorter overall survival time compared with patients in the low‑risk group (hazard ratio=1.231, 95% confidence interval=1.058‑1.433, P=0.0071, by the Wald test). A similar result was produced from the GEO database. The findings may provide a novel strategy for measuring cancer aggressiveness in patients with breast cancer.

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1	Weigelt B, Peterse JL and van't Veer LJ: Breast cancer metastasis: Markers and models. Nat Rev Cancer. 5:591–602. 2005. View Article : Google Scholar : PubMed/NCBI
2	Slamon DJ, Clark GM, Wong SG, Levin WJ, Ullrich A and McGuire WL: Human breast cancer: Correlation of relapse and survival with amplification of the HER-2/neu oncogene. Science. 235:177–182. 1987. View Article : Google Scholar : PubMed/NCBI
3	Foekens JA, Peters HA, Look MP, Portengen H, Schmitt M, Kramer MD, Brünner N, Jänicke F, Meijer-van Gelder ME, Henzen-Logmans SC, et al: The urokinase system of plasminogen activation and prognosis in 2780 breast cancer patients. Cancer Res. 60:636–643. 2000.PubMed/NCBI
4	Ma XJ, Wang Z, Ryan PD, Isakoff SJ, Barmettler A, Fuller A, Muir B, Mohapatra G, Salunga R, Tuggle JT, et al: A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen. Cancer Cell. 5:607–616. 2004. View Article : Google Scholar : PubMed/NCBI
5	Wang X, Qian H and Zhang S: Discovery of significant pathways in breast cancer metastasis via module extraction and comparison. IET Syst Biol. 8:47–55. 2014. View Article : Google Scholar : PubMed/NCBI
6	Langfelder P and Horvath S: WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics. 9:5592008. View Article : Google Scholar : PubMed/NCBI
7	Langfelder P and Horvath S: Eigengene networks for studying the relationships between co-expression modules. Bmc Syst Biol. 1:542007. View Article : Google Scholar : PubMed/NCBI
8	Xiang Y, Zhang CQ and Huang K: Predicting glioblastoma prognosis networks using weighted gene co-expression network analysis on TCGA data. BMC Bioinformatics. 13 Suppl 2:S122012.PubMed/NCBI
9	Presson AP, Yoon NK, Bagryanova L, Mah V, Alavi M, Maresh EL, Rajasekaran AK, Goodglick L, Chia D and Horvath S: Protein expression based multimarker analysis of breast cancer samples. BMC Cancer. 11:2302011. View Article : Google Scholar : PubMed/NCBI
10	Wang L, Tang H, Thayanithy V, Subramanian S, Oberg AL, Cunningham JM, Cerhan JR, Steer CJ and Thibodeau SN: Gene networks and microRNAs implicated in aggressive prostate cancer. Cancer Res. 69:9490–9497. 2009. View Article : Google Scholar : PubMed/NCBI
11	Udyavar AR, Hoeksema M, Diggins K, Irish J, Massion PP and Quaranta V: Abstract B27: Phenotypic plasticity and heterogeneity in small cell lung cancer (SCLC): Novel molecular subtypes and potential for targeted therapy. Clin Cancer Res. 20:B27. 2014. View Article : Google Scholar
12	Clarke C, Madden SF, Doolan P, Aherne ST, Joyce H, O'Driscoll L, Gallagher WM, Hennessy BT, Moriarty M, Crown J, et al: Correlating transcriptional networks to breast cancer survival: A large-scale coexpression analysis. Carcinogenesis. 34:2300–2308. 2013. View Article : Google Scholar : PubMed/NCBI
13	Hua L, Zhou P, Li L, Liu H and Yang Z: Prioritizing breast cancer subtype related miRNAs using miRNA-mRNA dysregulated relationships extracted from their dual expression profiling. J Theor Biol. 331:1–11. 2013. View Article : Google Scholar : PubMed/NCBI
14	Han HJ, Russo J, Kohwi Y and Kohwi-Shigematsu T: SATB1 reprogrammes gene expression to promote breast tumour growth and metastasis. Nature. 452:187–193. 2008. View Article : Google Scholar : PubMed/NCBI
15	Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, Kim IF, Soboleva A, Tomashevsky M and Edgar R: NCBI GEO: Mining tens of millions of expression profiles-database and tools update. Nucleic Acids Res. 35(Database issue): D760–D765. 2007. View Article : Google Scholar : PubMed/NCBI
16	Zhang B and Horvath S: A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol. 4:Article172005. View Article : Google Scholar : PubMed/NCBI
17	Keller MP, Choi Y, Wang P, Davis DB, Rabaglia ME, Oler AT, Stapleton DS, Argmann C, Schueler KL, Edwards S, et al: A gene expression network model of type 2 diabetes links cell cycle regulation in islets with diabetes susceptibility. Genome Res. 18:706–716. 2008. View Article : Google Scholar : PubMed/NCBI
18	da DW Huang, Sherman BT and Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 4:44–57. 2009.PubMed/NCBI
19	da DW Huang, Sherman BT and Lempicki RA: Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37:1–13. 2009. View Article : Google Scholar : PubMed/NCBI
20	Cox DR: Regressions models and life tables. J Royal Statistical Soc. 34:187–200. 1972.
21	Xiong J, Bing Z, Su Y, Deng D and Peng X: An integrated mRNA and microRNA expression signature for glioblastoma multiforme prognosis. PLoS One. 9:e984192014. View Article : Google Scholar : PubMed/NCBI
22	Ravasz E, Somera AL, Mongru DA, Oltvai ZN and Barabási AL: Hierarchical organization of modularity in metabolic networks. Science. 297:1551–1555. 2002. View Article : Google Scholar : PubMed/NCBI
23	Hermans TM, Pilans D, Huda S, Fuller P, Kandere-Grzybowska K and Grzybowski BA: Motility efficiency and spatiotemporal synchronization in non-metastatic vs. metastatic breast cancer cells. Integr Biol (Camb). 5:1464–1473. 2013. View Article : Google Scholar : PubMed/NCBI
24	DiMilla PA, Barbee K and Lauffenburger DA: Mathematical model for the effects of adhesion and mechanics on cell migration speed. Biophys J. 60:15–37. 1991. View Article : Google Scholar : PubMed/NCBI
25	Fife CM, McCarroll JA and Kavallaris M: Movers and shakers: Cell cytoskeleton in cancer metastasis. Br J Pharmacol. 171:5507–5523. 2014. View Article : Google Scholar : PubMed/NCBI
26	Chambers AF, Groom AC and MacDonald IC: Dissemination and growth of cancer cells in metastatic sites. Nat Rev Cancer. 2:563–572. 2002. View Article : Google Scholar : PubMed/NCBI
27	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
28	Hahn MW and Kern AD: Comparative genomics of centrality and essentiality in three eukaryotic protein-interaction networks. Mol Biol Evol. 22:803–806. 2005. View Article : Google Scholar : PubMed/NCBI
29	Zotenko E, Mestre J, O'Leary DP and Przytycka TM: Why do hubs in the yeast protein interaction network tend to be essential: Reexamining the connection between the network topology and essentiality. PLoS Comput Biol. 4:e10001402008. View Article : Google Scholar : PubMed/NCBI
30	King TJ and Bertram JS: Connexins as targets for cancer chemoprevention and chemotherapy. Biochim Biophys Acta. 1719:146–160. 2005. View Article : Google Scholar : PubMed/NCBI
31	Liu YF, Xiao ZQ, Li MX, Li MY, Zhang PF, Li C, Li F, Chen YH, Yi H, Yao HX and Chen ZC: Quantitative proteome analysis reveals annexin A3 as a novel biomarker in lung adenocarcinoma. J Pathol. 217:54–64. 2009. View Article : Google Scholar : PubMed/NCBI
32	Croset M, Goehrig D, Frackowiak A, Bonnelye E, Ansieau S, Puisieux A and Clézardin P: TWIST1 expression in breast cancer cells facilitates bone metastasis formation. J Bone Miner Res. 29:1886–1899. 2014. View Article : Google Scholar : PubMed/NCBI
33	Marchiò C, Natrajan R, Shiu KK, Lambros MB, Rodriguez-Pinilla SM, Tan DS, Lord CJ, Hungermann D, Fenwick K, Tamber N, et al: The genomic profile of HER2-amplified breast cancers: The influence of ER status. J Pathol. 216:399–407. 2008. View Article : Google Scholar : PubMed/NCBI
34	Yang J, Mani SA, Donaher JL, Ramaswamy S, Itzykson RA, Come C, Savagner P, Gitelman I, Richardson A and Weinberg RA: Twist, a master regulator of morphogenesis, plays an essential role in tumor metastasis. Cell. 117:927–939. 2004. View Article : Google Scholar : PubMed/NCBI
35	Zhong J, Ogura K, Wang Z and Inuzuka H: Degradation of the transcription factor Twist, an oncoprotein that promotes cancer metastasis. Discov Med. 15:7–15. 2013.PubMed/NCBI
36	Liekens S, Schols D and Hatse S: CXCL12-CXCR4 axis in angiogenesis, metastasis and stem cell mobilization. Curr Pharm Des. 16:3903–3920. 2010. View Article : Google Scholar : PubMed/NCBI
37	Hur K, Han TS, Jung EJ, Yu J, Lee HJ, Kim WH, Goel A and Yang HK: Up-regulated expression of sulfatases (SULF1 and SULF2) as prognostic and metastasis predictive markers in human gastric cancer. J Pathol. 228:88–98. 2012.PubMed/NCBI
38	Pothlichet J, Mangeney M and Heidmann T: Mobility and integration sites of a murine C57BL/6 melanoma endogenous retrovirus involved in tumor progression in vivo. Int J Cancer. 119:1869–1877. 2006. View Article : Google Scholar : PubMed/NCBI
39	Kuphal S, Lodermeyer S, Bataille F, Schuierer M, Hoang BH and Bosserhoff AK: Expression of Dickkopf genes is strongly reduced in malignant melanoma. Oncogene. 25:5027–5036. 2006. View Article : Google Scholar : PubMed/NCBI
40	Burnett RM, Craven KE, Krishnamurthy P, Goswami CP, Badve S, Crooks P, Mathews WP, Bhat-Nakshatri P and Nakshatri H: Organ-specific adaptive signaling pathway activation in metastatic breast cancer cells. Oncotarget. 6:12682–12696. 2015. View Article : Google Scholar : PubMed/NCBI
41	Lv ZD, Kong B, Liu XP, Dong Q, Niu HT, Wang YH, Li FN and Wang HB: CXCL12 chemokine expression suppresses human breast cancer growth and metastasis in vitro and in vivo. Int J Clin Exp Pathol. 7:6671–6678. 2014.PubMed/NCBI
42	Sosseyalaoui K, Pluskota E, Davuluri G, Bialkowska K, Das M, Lindner D and Plow EF: Abstract 2088: Kindlin-3 enhances breast cancer metastasis through upregulation of Twist-mediated tumor angiogenesis. Cancer Res. 74:2088. 2014. View Article : Google Scholar
43	Handerson T, Camp R, Harigopal M, Rimm D and Pawelek J: Beta1,6-branched oligosaccharides are increased in lymph node metastases and predict poor outcome in breast carcinoma. Clin Cancer Res. 11:2969–2973. 2005. View Article : Google Scholar : PubMed/NCBI
44	Jeong H, Mason SP, Barabási AL and Oltvai ZN: Lethality and centrality in protein networks. Nature. 411:41–42. 2001. View Article : Google Scholar : PubMed/NCBI
45	Schadt EE, Lamb J, Yang X, Zhu J, Edwards S, Guhathakurta D, Sieberts SK, Monks S, Reitman M, Zhang C, et al: An integrative genomics approach to infer causal associations between gene expression and disease. Nat Genet. 37:710–717. 2005. View Article : Google Scholar : PubMed/NCBI
46	Rae JM, Creighton CJ, Meck JM, Haddad BR and Johnson MD: MDA-MB-435 cells are derived from M14 melanoma cells-a loss for breast cancer, but a boon for melanoma research. Breast Cancer Res Treat. 104:13–19. 2007. View Article : Google Scholar : PubMed/NCBI
47	Capes-Davis A, Theodosopoulos G, Atkin I, Drexler HG, Kohara A, MacLeod RA, Masters JR, Nakamura Y, Reid YA, Reddel RR and Freshney RI: Check your cultures! A list of cross-contaminated or misidentified cell lines. Int J Cancer. 127:1–8. 2010. View Article : Google Scholar : PubMed/NCBI
48	Chambers AF: MDA-MB-435 and M14 cell lines: Identical but not M14 melanoma? Cancer Res. 69:5292–5293. 2009. View Article : Google Scholar : PubMed/NCBI
49	Katase N, Gunduz M, Beder L, Gunduz E, Lefeuvre M, Hatipoglu OF, Borkosky SS, Tamamura R, Tominaga S, Yamanaka N, et al: Deletion at Dickkopf (dkk)-3 locus (11p15.2) is related with lower lymph node metastasis and better prognosis in head and neck squamous cell carcinomas. Oncol Res. 17:273–282. 2008. View Article : Google Scholar : PubMed/NCBI
50	Stracke ML and Liotta LA: Multi-step cascade of tumor cell metastasis. In Vivo. 6:309–316. 1992.PubMed/NCBI