Identification of breast cancer prognostic modules based on weighted protein‑protein interaction networks

Li,Wan; Bai,Xue; Hu,Erqiang; Huang,Hao; Li,Yiran; He,Yuehan; Lv,Junjie; Chen,Lina; He,Weiming

doi:10.3892/ol.2017.5917

Identification of breast cancer prognostic modules based on weighted protein‑protein interaction networks

Authors:
- Wan Li
- Xue Bai
- Erqiang Hu
- Hao Huang
- Yiran Li
- Yuehan He
- Junjie Lv
- Lina Chen
- Weiming He
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Affiliations: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, P.R. China, Institute of Opto‑electronics, Harbin Institute of Technology, Harbin, Heilongjiang 150001, P.R. China
Published online on: March 27, 2017 https://doi.org/10.3892/ol.2017.5917
Pages: 3935-3941

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Abstract

Breast cancer is one of the leading causes of mortality in females. A number of prognostic markers have been identified, including single genes, multi‑gene signatures and network modules; however, the robustness of these prognostic markers is insufficient. Thus, the present study proposed a more robust method to identify breast cancer prognostic modules based on weighted protein‑protein interaction networks, by integrating four sets of disease‑associated expression profiles. Three identified prognostic modules were closely associated with prognosis‑associated functions and survival time, as determined by Cox regression and Kaplan‑Meier survival analyses. The robustness of these modules was verified with an independent profile from another platform. Genes from these modules may be useful as breast cancer prognostic markers. The prognostic modules could be used to determine the prognoses of patients with breast cancer and characterize patient recovery.

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1	Fitzgerald SP: Breast-cancer screening. N Engl J Med. 366:191author reply 191. –192. 2012.PubMed/NCBI
2	Hanahan D and Weinberg RA: Hallmarks of cancer: The next generation. Cell. 144:646–674. 2011. View Article : Google Scholar : PubMed/NCBI
3	Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J and Jemal A: Global cancer statistics, 2012. CA Cancer J Clin. 65:87–108. 2015. View Article : Google Scholar : PubMed/NCBI
4	Colzani E, Liljegren A, Johansson AL, Adolfsson J, Hellborg H, Hall PF and Czene K: Prognosis of patients with breast cancer: Causes of death and effects of time since diagnosis, age, and tumor characteristics. J Clin Oncol. 29:4014–4021. 2011. View Article : Google Scholar : PubMed/NCBI
5	Staiger C, Cadot S, Györffy B, Wessels LF and Klau GW: Current composite-feature classification methods do not outperform simple single-genes classifiers in breast cancer prognosis. Front Genet. 4:2892013. View Article : Google Scholar : PubMed/NCBI
6	Goswami CP and Nakshatri H: PROGgene: Gene expression based survival analysis web application for multiple cancers. J Clin Bioinforma. 3:222013. View Article : Google Scholar : PubMed/NCBI
7	van de Vijver MJ, He YD, van't Veer LJ, Dai H, Hart AA, Voskuil DW, Schreiber GJ, Peterse JL, Roberts C, Marton MJ, et al: A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med. 347:1999–2009. 2002. View Article : Google Scholar : PubMed/NCBI
8	Pawitan Y, Bjöhle J, Amler L, Borg AL, Egyhazi S, Hall P, Han X, Holmberg L, Huang F, Klaar S, et al: Gene expression profiling spares early breast cancer patients from adjuvant therapy: Derived and validated in two population-based cohorts. Breast Cancer Res. 7:R953–R964. 2005. View Article : Google Scholar : PubMed/NCBI
9	Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M, Baehner FL, Walker MG, Watson D, Park T, et al: A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 351:2817–2826. 2004. View Article : Google Scholar : PubMed/NCBI
10	Wang J, Webb-Robertson BJ, Matzke MM, Varnum SM, Brown JN, Riensche RM, Adkins JN, Jacobs JM, Hoidal JR, Scholand MB, et al: A semiautomated framework for integrating expert knowledge into disease marker identification. Dis Markers. 35:513–523. 2013. View Article : Google Scholar : PubMed/NCBI
11	Lee G, Singanamalli A, Wang H, Feldman MD, Master SR, Shih NN, Spangler E, Rebbeck T, Tomaszewski JE and Madabhushi A: Supervised multi-view canonical correlation analysis (sMVCCA): Integrating histologic and proteomic features for predicting recurrent prostate cancer. IEEE Trans Med Imaging. 34:284–297. 2015. View Article : Google Scholar : PubMed/NCBI
12	Davis S and Meltzer PS: GEOquery: A bridge between the gene expression omnibus (GEO) and BioConductor. Bioinformatics. 23:1846–1847. 2007. View Article : Google Scholar : PubMed/NCBI
13	Wang Y, Klijn JG, Zhang Y, Sieuwerts AM, Look MP, Yang F, Talantov D, Timmermans M, Meijer-van Gelder ME, Yu J, et al: Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet. 365:671–679. 2005. View Article : Google Scholar : PubMed/NCBI
14	Miller LD, Smeds J, George J, Vega VB, Vergara L, Ploner A, Pawitan Y, Hall P, Klaar S, Liu ET and Bergh J: An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects and patient survival. Proc Natl Acad Sci USA. 102:13550–13555. 2005. View Article : Google Scholar : PubMed/NCBI
15	Ivshina AV, George J, Senko O, Mow B, Putti TC, Smeds J, Lindahl T, Pawitan Y, Hall P, Nordgren H, et al: Genetic reclassification of histologic grade delineates new clinical subtypes of breast cancer. Cancer Res. 66:10292–10301. 2006. View Article : Google Scholar : PubMed/NCBI
16	Chanrion M, Negre V, Fontaine H, Salvetat N, Bibeau F, MacGrogan G, Mauriac L, Katsaros D, Molina F, Theillet C and Darbon JM: A gene expression signature that can predict the recurrence of tamoxifen-treated primary breast cancer. Clin Cancer Res. 14:1744–1752. 2008. View Article : Google Scholar : PubMed/NCBI
17	Keshava Prasad TS, 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
18	Nepusz T, Yu H and Paccanaro A: Detecting overlapping protein complexes in protein-protein interaction networks. Nat Methods. 9:471–472. 2012. View Article : Google Scholar : PubMed/NCBI
19	Friston KJ, Frith CD, Liddle PF and Frackowiak RS: Functional connectivity: The principal-component analysis of large (PET) data sets. J Cereb Blood Flow Metab. 13:5–14. 1993. View Article : Google Scholar : PubMed/NCBI
20	Gene Ontology Consortium: Gene Ontology Consortium: Going forward. Nucleic Acids Res. 43:(Database issue). D1049–D1056. 2015. View Article : Google Scholar : PubMed/NCBI
21	Kanehisa M, Goto S, Sato Y, Kawashima M, Furumichi M and Tanabe M: Data, information, knowledge and principle: Back to metabolism in KEGG. Nucleic Acids Res. 42:(Database issue). D199–D205. 2014. View Article : Google Scholar : PubMed/NCBI
22	Parmar MB, Aliabadi HM, Mahdipoor P, Kucharski C, Maranchuk R, Hugh JC and Uludağ H: Targeting cell cycle proteins in breast cancer cells with siRNA by using lipid-substituted polyethylenimines. Front Bioeng Biotechnol. 3:142015. View Article : Google Scholar : PubMed/NCBI
23	Weinberg RA: The retinoblastoma protein and cell cycle control. Cell. 81:323–330. 1995. View Article : Google Scholar : PubMed/NCBI
24	Yu Z, Baserga R, Chen L, Wang C, Lisanti MP and Pestell RG: microRNA, cell cycle, and human breast cancer. Am J Pathol. 176:1058–1064. 2010. View Article : Google Scholar : PubMed/NCBI
25	Tenga MJ and Lazar IM: Proteomic snapshot of breast cancer cell cycle: G1/S transition point. Proteomics. 13:48–60. 2013. View Article : Google Scholar : PubMed/NCBI
26	Achari C, Winslow S, Ceder Y and Larsson C: Expression of miR-34c induces G2/M cell cycle arrest in breast cancer cells. BMC Cancer. 14:5382014. View Article : Google Scholar : PubMed/NCBI
27	Kato T, Kameoka S, Kimura T, Tanaka S, Nishikawa T and Kobayashi M: p53, mitosis, apoptosis and necrosis as prognostic indicators of long-term survival in breast cancer. Anticancer Res. 22:1105–1112. 2002.PubMed/NCBI
28	van Diest PJ, van der Wall E and Baak JP: Prognostic value of proliferation in invasive breast cancer: A review. J Clin Pathol. 57:675–681. 2004. View Article : Google Scholar : PubMed/NCBI
29	Königsberg R, Rögelsperger O, Jäger W, Thalhammer T, Klimpfinger M, De Santis M, Hudec M and Dittrich C: Cell cycle dysregulation influences survival in high risk breast cancer patients. Cancer Invest. 26:734–740. 2008. View Article : Google Scholar : PubMed/NCBI
30	Louie MC, McClellan A, Siewit C and Kawabata L: Estrogen receptor regulates E2F1 expression to mediate tamoxifen resistance. Mol Cancer Res. 8:343–352. 2010. View Article : Google Scholar : PubMed/NCBI
31	Kamel A, Mokhtar N, Elshakankiry N, Yassin D, Elnahass Y, Zakarya O, Elbasmy A and Elmetenawy W: The prognostic impact of some cell cycle regulatory proteins in Egyptian breast cancer patients. J Egypt Natl Canc Inst. 18:93–102. 2006.PubMed/NCBI
32	Simon NE and Schwacha A: The Mcm2-7 replicative helicase: A promising chemotherapeutic target. Biomed Res Int. 2014:5497192014. View Article : Google Scholar : PubMed/NCBI
33	Coster G, Frigola J, Beuron F, Morris EP and Diffley JF: Origin licensing requires ATP binding and hydrolysis by the MCM replicative helicase. Mol Cell. 55:666–677. 2014. View Article : Google Scholar : PubMed/NCBI
34	Labib K, Tercero JA and Diffley JF: Uninterrupted MCM2-7 function required for DNA replication fork progression. Science. 288:1643–1647. 2000. View Article : Google Scholar : PubMed/NCBI
35	Wan G, Hu X, Liu Y, Han C, Sood AK, Calin GA, Zhang X and Lu X: A novel non-coding RNA lncRNA-JADE connects DNA damage signalling to histone H4 acetylation. EMBO J. 32:2833–2847. 2013. View Article : Google Scholar : PubMed/NCBI
36	de Munnik SA, Bicknell LS, Aftimos S, Al-Aama JY, van Bever Y, Bober MB, Clayton-Smith J, Edrees AY, Feingold M, Fryer A, et al: Meier-Gorlin syndrome genotype-phenotype studies: 35 individuals with pre-replication complex gene mutations and 10 without molecular diagnosis. Eur J Hum Genet. 20:598–606. 2012. View Article : Google Scholar : PubMed/NCBI
37	MacDermed DM, Khodarev NN, Pitroda SP, Edwards DC, Pelizzari CA, Huang L, Kufe DW and Weichselbaum RR: MUC1-associated proliferation signature predicts outcomes in lung adenocarcinoma patients. BMC Med Genomics. 3:162010. View Article : Google Scholar : PubMed/NCBI
38	Wu G and Stein L: A network module-based method for identifying cancer prognostic signatures. Genome Biol. 13:R1122012. View Article : Google Scholar : PubMed/NCBI
39	Cronin M, Sangli C, Liu ML, Pho M, Dutta D, Nguyen A, Jeong J, Wu J, Langone KC and Watson D: Analytical validation of the Oncotype DX genomic diagnostic test for recurrence prognosis and therapeutic response prediction in node-negative, estrogen receptor-positive breast cancer. Clin Chem. 53:1084–1091. 2007. View Article : Google Scholar : PubMed/NCBI