Medical Oncology

, 35:67 | Cite as

Bioinformatic analysis reveals the key pathways and genes in early-onset breast cancer

  • Chuanlong Cui
  • Lun Li
  • Jing Zhen
Original Paper


Early-onset breast cancer is the most prevalent cancer in the female. To identify the differentially expressed genes and the key signaling pathways in early-onset breast cancer, we have carried out the bioinformatic analysis of an RNA array dataset in the GEO database, GSE109169, which was acquired from early-onset breast cancer patient. A total of 118 differentially expressed genes in early-onset breast cancer were significantly changed compared with that in adjacent normal tissues. Most of these genes are classified into three categories: signaling molecule, enzyme modulator, and hydrolase. Gene ontology terms reveal that most of these genes are involved in cellular and metabolic processes, biological regulation, binding and catalytic activities, and receptor regulation. Protein–protein interaction network was constructed and has two highly enriched modules: one with up-regulated genes and the other with down-regulated genes. The singling pathways are mainly enriched in the cellular immune system, lipid metabolism and other types of metabolic pathways. Finally, we have plotted the Kaplan–Meier curves of two up-regulated and two down-regulated genes for the overall survival prediction in breast cancer. These results greatly expand the current view of early-onset breast cancer and shed light on the discovery of drug candidates and the improvement for the prognosis.


Early-onset breast cancer Bioinformatic analysis Protein–protein interactions Gene ontology and pathway analysis 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.


  1. 1.
    Siegel R, Naishadham D, Jemal A. Cancer statistics, 2013. CA Cancer J Clin. 2013;63(1):11–30. Scholar
  2. 2.
    Carroll JC, Cremin C, Allanson J, Blaine SM, Dorman H, Gibbons CA, et al. Hereditary breast and ovarian cancers. Can Fam Phys. 2008;54(12):1691–2.Google Scholar
  3. 3.
    Anders CK, Johnson R, Litton J, Phillips M, Bleyer A. Breast cancer before age 40 years. Semin Oncol. 2009;36(3):237–49. Scholar
  4. 4.
    Sundquist M, Thorstenson S, Brudin L, Wingren S, Nordenskjold B. Incidence and prognosis in early onset breast cancer. Breast. 2002;11(1):30–5. Scholar
  5. 5.
    Peto J, Collins N, Barfoot R, Seal S, Warren W, Rahman N, et al. Prevalence of BRCA1 and BRCA2 gene mutations in patients with early-onset breast cancer. J Natl Cancer Inst. 1999;91(11):943–9.CrossRefPubMedGoogle Scholar
  6. 6.
    Easton DF, Bishop DT, Ford D, Crockford GP. Genetic linkage analysis in familial breast and ovarian cancer: results from 214 families. The Breast Cancer Linkage Consortium. Am J Hum Genet. 1993;52(4):678–701.PubMedPubMedCentralGoogle Scholar
  7. 7.
    Loman N, Johannsson O, Kristoffersson U, Olsson H, Borg A. Family history of breast and ovarian cancers and BRCA1 and BRCA2 mutations in a population-based series of early-onset breast cancer. J Natl Cancer Inst. 2001;93(16):1215–23.CrossRefPubMedGoogle Scholar
  8. 8.
    Birch JM, Blair V, Kelsey AM, Evans DG, Harris M, Tricker KJ, et al. Cancer phenotype correlates with constitutional TP53 genotype in families with the Li-Fraumeni syndrome. Oncogene. 1998;17(9):1061–8. Scholar
  9. 9.
    Nichols KE, Malkin D, Garber JE, Fraumeni JF Jr, Li FP. Germ-line p53 mutations predispose to a wide spectrum of early-onset cancers. Cancer Epidemiol Biomark Prev. 2001;10(2):83–7.Google Scholar
  10. 10.
    Walsh T, Casadei S, Coats KH, Swisher E, Stray SM, Higgins J, et al. Spectrum of mutations in BRCA1, BRCA2, CHEK2, and TP53 in families at high risk of breast cancer. JAMA. 2006;295(12):1379–88. Scholar
  11. 11.
    Olivier M, Goldgar DE, Sodha N, Ohgaki H, Kleihues P, Hainaut P, et al. Li-Fraumeni and related syndromes: correlation between tumor type, family structure, and TP53 genotype. Cancer Res. 2003;63(20):6643–50.PubMedGoogle Scholar
  12. 12.
    McCuaig JM, Armel SR, Novokmet A, Ginsburg OM, Demsky R, Narod SA, et al. Routine TP53 testing for breast cancer under age 30: ready for prime time? Fam Cancer. 2012;11(4):607–13. Scholar
  13. 13.
    Lalloo F, Varley J, Ellis D, Moran A, O’Dair L, Pharoah P, et al. Prediction of pathogenic mutations in patients with early-onset breast cancer by family history. Lancet. 2003;361(9363):1101–2. Scholar
  14. 14.
    Masciari S, Dillon DA, Rath M, Robson M, Weitzel JN, Balmana J, et al. Breast cancer phenotype in women with TP53 germline mutations: a Li-Fraumeni syndrome consortium effort. Breast Cancer Res Treat. 2012;133(3):1125–30. Scholar
  15. 15.
    Armes JE, Trute L, White D, Southey MC, Hammet F, Tesoriero A, et al. Distinct molecular pathogeneses of early-onset breast cancers in BRCA1 and BRCA2 mutation carriers: a population-based study. Cancer Res. 1999;59(8):2011–7.PubMedGoogle Scholar
  16. 16.
    Starink TM, van der Veen JP, Arwert F, de Waal LP, de Lange GG, Gille JJ, et al. The Cowden syndrome: a clinical and genetic study in 21 patients. Clin Genet. 1986;29(3):222–33.CrossRefPubMedGoogle Scholar
  17. 17.
    Brownstein MH, Wolf M, Bikowski JB. Cowden’s disease: a cutaneous marker of breast cancer. Cancer. 1978;41(6):2393–8.CrossRefPubMedGoogle Scholar
  18. 18.
    Dite GS, Whittemore AS, Knight JA, John EM, Milne RL, Andrulis IL, et al. Increased cancer risks for relatives of very early-onset breast cancer cases with and without BRCA1 and BRCA2 mutations. Br J Cancer. 2010;103(7):1103–8. Scholar
  19. 19.
    Varga D, Koenig J, Kuhr K, Strunz K, Geyer V, Kurzeder C, et al. Comparison of early onset breast cancer patients to older premenopausal breast cancer patients. Arch Gynecol Obstet. 2010;282(4):427–32. Scholar
  20. 20.
    Nilsson MP, Hartman L, Idvall I, Kristoffersson U, Johannsson OT, Loman N. Long-term prognosis of early-onset breast cancer in a population-based cohort with a known BRCA1/2 mutation status. Breast Cancer Res Treat. 2014;144(1):133–42. Scholar
  21. 21.
    Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucl Acids Res. 2002;30(1):207–10.CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Mi H, Muruganujan A, Casagrande JT, Thomas PD. Large-scale gene function analysis with the PANTHER classification system. Nat Protoc. 2013;8(8):1551–66. Scholar
  23. 23.
    da Huang W, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucl Acids Res. 2009;37(1):1–13. Scholar
  24. 24.
    Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucl Acids Res. 2000;28(1):27–30.CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Snel B, Lehmann G, Bork P, Huynen MA. STRING: a web-server to retrieve and display the repeatedly occurring neighbourhood of a gene. Nucl Acids Res. 2000;28(18):3442–4.CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504. Scholar
  27. 27.
    Lanczky A, Nagy A, Bottai G, Munkacsy G, Szabo A, Santarpia L, et al. miRpower: a web-tool to validate survival-associated miRNAs utilizing expression data from 2178 breast cancer patients. Breast Cancer Res Treat. 2016;160(3):439–46. Scholar
  28. 28.
    Assi HA, Khoury KE, Dbouk H, Khalil LE, Mouhieddine TH, El Saghir NS. Epidemiology and prognosis of breast cancer in young women. J Thorac Dis. 2013;5(Suppl 1):S2–8. Scholar
  29. 29.
    Johnson RW, Finger EC, Olcina MM, Vilalta M, Aguilera T, Miao Y, et al. Induction of LIFR confers a dormancy phenotype in breast cancer cells disseminated to the bone marrow. Nat Cell Biol. 2016;18(10):1078–89. Scholar
  30. 30.
    Gudas JM, Payton M, Thukral S, Chen E, Bass M, Robinson MO, et al. Cyclin E2, a novel G1 cyclin that binds Cdk2 and is aberrantly expressed in human cancers. Mol Cell Biol. 1999;19(1):612–22.CrossRefPubMedGoogle Scholar
  31. 31.
    Gao CL, Wang GW, Yang GQ, Yang H, Zhuang L. Karyopherin subunit-alpha 2 expression accelerates cell cycle progression by upregulating CCNB2 and CDK1 in hepatocellular carcinoma. Oncol Lett. 2018;15(3):2815–20. Scholar
  32. 32.
    Zhou W, Wang Z, Shen N, Pi W, Jiang W, Huang J, et al. Knockdown of ANLN by lentivirus inhibits cell growth and migration in human breast cancer. Mol Cell Biochem. 2015;398(1–2):11–9. Scholar
  33. 33.
    Magnusson K, Gremel G, Ryden L, Ponten V, Uhlen M, Dimberg A, et al. ANLN is a prognostic biomarker independent of Ki-67 and essential for cell cycle progression in primary breast cancer. BMC Cancer. 2016;16(1):904. Scholar
  34. 34.
    Wang Z, Chen J, Zhong MZ, Huang J, Hu YP, Feng DY, et al. Overexpression of ANLN contributed to poor prognosis of anthracycline-based chemotherapy in breast cancer patients. Cancer Chemother Pharmacol. 2017;79(3):535–43. Scholar
  35. 35.
    Zhou C, Wang M, Zhou L, Zhang Y, Liu W, Qin W, et al. Prognostic significance of PLIN1 expression in human breast cancer. Oncotarget. 2016;7(34):54488–502. Scholar
  36. 36.
    Paul D, Ghorai S, Dinesh US, Shetty P, Chattopadhyay S, Santra MK. Cdc20 directs proteasome-mediated degradation of the tumor suppressor SMAR1 in higher grades of cancer through the anaphase promoting complex. Cell Death Dis. 2017;8(6):e2882. Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Graduate StudiesRutgers, The State University of New JerseyNewarkUSA
  2. 2.Department of Medicinal ChemistryRutgers, The State University of New JerseyNew BrunswickUSA
  3. 3.Teva Pharmaceutical IndustriesWest ChesterUSA

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