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Tumor Biology

, Volume 37, Issue 12, pp 15967–15975 | Cite as

A new ten-gene risk fraction model serving as prognostic indicator for clinical outcome of multiple myeloma

Original Article

Abstract

Multiple myeloma (MM) is a kind of aggressive tumor prevalent with high heterogeneity. Abnormal expression of certain genes may lead to the occurrence and development of MM. Nowadays, it is not commonly seen in clinical research to predict the prognostic circumstances of patients with MM by multiple gene expression profiling method. Identification of potential genes in prognostic process could be beneficial for clinical management of MM. Therefore, we aimed to build a risk fraction model to screen out the prognostic indicator for clinical outcome of MM. Microarray data were downloaded from the Genome Expression Omnibus (GEO) datasets with accession numbers GSE24080 and GSE57317. A total of 279 samples were selected out randomly. Besides, a risk formula was constructed and verified in the dataset. Time-dependent receiver operating characteristic (ROC) curve was applied in evaluating the accurate prognostic conditions of patients. Finally, a ten genes model in the training dataset was found to be closely related to the survival condition of MM patients. Patients with MM were divided into two groups, high-risk and low-risk, by the expression of these ten genes, and significant statistical difference was found between the two groups. Furthermore, the result of multivariate cox regression and stratified analysis indicated that this model was independent of other clinical phenotypes. ROC curves also showed its feasibility to predict the survival status of MM patients. Our results demonstrated that the fraction risk model constructed by the selected ten genes could be used to predict survival status of multiple myeloma patients, which could also help in improvement of prognostic and therapeutic tool of MM.

Keywords

Microarray expression profiling Genes Multiple myeloma Survival rates Prognostic indicator 

Supplementary material

13277_2016_5449_MOESM1_ESM.doc (35 kb)
Supplement Table 1 (DOC 35 kb)

References

  1. 1.
    Munshi NC, Anderson KC, Bergsagel PL, Shaughnessy J, Palumbo A, Durie B, Fonseca R, Stewart AK, Harousseau J-L, Dimopoulos M. Consensus recommendations for risk stratification in multiple myeloma: report of the international myeloma workshop consensus panel 2. Blood. 2011;117:4696–700.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Decaux O, Lodé L, Magrangeas F, Charbonnel C, Gouraud W, Jézéquel P, Attal M, Harousseau J-L, Moreau P, Bataille R. Prediction of survival in multiple myeloma based on gene expression profiles reveals cell cycle and chromosomal instability signatures in high-risk patients and hyperdiploid signatures in low-risk patients: a study of the intergroupe francophone du myelome. J Clin Oncol. 2008;26:4798–805.CrossRefPubMedGoogle Scholar
  3. 3.
    Kumar SK, Rajkumar SV, Dispenzieri A, Lacy MQ, Hayman SR, Buadi FK, Zeldenrust SR, Dingli D, Russell SJ, Lust JA. Improved survival in multiple myeloma and the impact of novel therapies. Blood. 2008;111:2516–20.CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Tran B, Dancey JE, Kamel-Reid S, McPherson JD, Bedard PL, Brown AM, Zhang T, Shaw P, Onetto N, Stein L, Hudson TJ, Neel BG, Siu LL. Cancer genomics: technology, discovery, and translation. Cancer genomics: technology, discovery, and translation. J Clin Oncol. 2012;30(6):647–60.Google Scholar
  5. 5.
    Arpino G, Generali D, Sapino A, Del Matro L, Frassoldati A, de Laurentis M, Pronzato P, Mustacchi G, Cazzaniga M, De Placido S. Gene expression profiling in breast cancer: a clinical perspective. Breast. 2013;22:109–20.CrossRefPubMedGoogle Scholar
  6. 6.
    Gray RG, Quirke P, Handley K, Lopatin M, Magill L, Baehner FL, Beaumont C, Clark-Langone KM, Yoshizawa CN, Lee M. Validation study of a quantitative multigene reverse transcriptase–polymerase chain reaction assay for assessment of recurrence risk in patients with stage ii colon cancer. J Clin Oncol. 2011;29:4611–9.CrossRefPubMedGoogle Scholar
  7. 7.
    Choudhury AD, Eeles R, Freedland SJ, Isaacs WB, Pomerantz MM, Schalken JA, Tammela TL, Visakorpi T. The role of genetic markers in the management of prostate cancer. Eur Urol. 2012;62:577–87.CrossRefPubMedGoogle Scholar
  8. 8.
    Alizadeh AA, Gentles AJ, Alencar AJ, Liu CL, Kohrt HE, Houot R, Goldstein MJ, Zhao S, Natkunam Y, Advani RH. Prediction of survival in diffuse large b-cell lymphoma based on the expression of 2 genes reflecting tumor and microenvironment. Blood. 2011;118:1350–8.CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Consortium M. The microarray quality control (maqc)-ii study of common practices for the development and validation of microarray-based predictive models. Nat Biotechnol. 2010;28:827–38.CrossRefGoogle Scholar
  10. 10.
    Gevaert O, Xu J, Hoang CD, Leung AN, Xu Y, Quon A, Rubin DL, Napel S, Plevritis SK. Non–small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data—methods and preliminary results. Radiology. 2012;264:387–96.CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Harbron C, Chang K-M, South MC. Refplus: an r package extending the rma algorithm. Bioinformatics. 2007;23:2493–4.CrossRefPubMedGoogle Scholar
  12. 12.
    Proaño A, Aragón RE, Proaño JL. Escore z: Fenton 2013. Atualizacão de dez anos. J Pediatr. 2014;90:426.CrossRefGoogle Scholar
  13. 13.
    Jiang H, Wong WH. Seqmap: mapping massive amount of oligonucleotides to the genome. Bioinformatics. 2008;24:2395–6.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    O’Quigley J, Moreau T. Cox’s regression model: computing a goodness of fit statistic. Comput Methods Prog Biomed. 1986;22:253–6.CrossRefGoogle Scholar
  15. 15.
    Pedersen PA, Kristensen FB. The Danish medical statistics and Danish practical research. Ugeskr Laeger. 1990;152:828–9.PubMedGoogle Scholar
  16. 16.
    Kyle RA. Multiple myeloma: review of 869 cases. Mayo Clin Proc. 1975;50:29–40.PubMedGoogle Scholar
  17. 17.
    Bataille R, Boccadoro M, Klein B, Durie B, Pileri A. C-reactive protein and beta-2 microglobulin produce a simple and powerful myeloma staging system. Blood. 1992;80:733–7.PubMedGoogle Scholar
  18. 18.
    Dimopoulos MA, Barlogie B, Smith TL, Alexanian R. High serum lactate dehydrogenase level as a marker for drug resistance and short survival in multiple myeloma. Ann Intern Med. 1991;115:931–5.CrossRefPubMedGoogle Scholar
  19. 19.
    Seidel C, Hjertner Ø, Abildgaard N, Heickendorff L, Hjorth M, Westin J, Nielsen JL, Hjorth-Hansen H, Waage A, Sundan A. Serum osteoprotegerin levels are reduced in patients with multiple myeloma with lytic bone disease. Blood. 2001;98:2269–71.CrossRefPubMedGoogle Scholar
  20. 20.
    Heagerty PJ, Lumley T, Pepe MS. Time-dependent roc curves for censored survival data and a diagnostic marker. Biometrics. 2000;56:337–44.CrossRefPubMedGoogle Scholar

Copyright information

© International Society of Oncology and BioMarkers (ISOBM) 2016

Authors and Affiliations

  1. 1.The Department of Orthopedic SurgeryPeople’s Hospital of Three Gorges UniversityYiChangChina
  2. 2.PuAi Institute, E Dong Healthcare GroupThe Central Hospital of HuangshiHuangshiChina
  3. 3.Department of Pathology, Shiyan Taihe HospitalHubei University of MedicineShiyan CityChina
  4. 4.Department of Clinical Laboratory Center, Central Hospital of Enshi Autonomous PrefectureEnshi Clinical College of Wuhan UniversityEnshiChina
  5. 5.Department of EmergencyThe Affiliated Huai’an Hospital of Xuzhou Medical College and The Second People’s Hospital of Huai’anHuai’anChina
  6. 6.Department of Hematology, Huai’an First People’s HospitalNanjing Medical UniversityHuai’anChina

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