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

  • Original Article
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Tumor Biology

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.

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References

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Harbron C, Chang K-M, South MC. Refplus: an r package extending the rma algorithm. Bioinformatics. 2007;23:2493–4.

    Article  CAS  PubMed  Google Scholar 

  12. Proaño A, Aragón RE, Proaño JL. Escore z: Fenton 2013. Atualizacão de dez anos. J Pediatr. 2014;90:426.

    Article  Google Scholar 

  13. Jiang H, Wong WH. Seqmap: mapping massive amount of oligonucleotides to the genome. Bioinformatics. 2008;24:2395–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. O’Quigley J, Moreau T. Cox’s regression model: computing a goodness of fit statistic. Comput Methods Prog Biomed. 1986;22:253–6.

    Article  Google Scholar 

  15. Pedersen PA, Kristensen FB. The Danish medical statistics and Danish practical research. Ugeskr Laeger. 1990;152:828–9.

    CAS  PubMed  Google Scholar 

  16. Kyle RA. Multiple myeloma: review of 869 cases. Mayo Clin Proc. 1975;50:29–40.

    CAS  PubMed  Google Scholar 

  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.

    CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  20. Heagerty PJ, Lumley T, Pepe MS. Time-dependent roc curves for censored survival data and a diagnostic marker. Biometrics. 2000;56:337–44.

    Article  CAS  PubMed  Google Scholar 

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Authors and Affiliations

  1. The Department of Orthopedic Surgery, People’s Hospital of Three Gorges University, YiChang, Hubei Province, China

    Ai-Xin Hu

  2. PuAi Institute, E Dong Healthcare Group, The Central Hospital of Huangshi, Huangshi, Hubei Province, China

    Zhi-Yong Huang

  3. Department of Pathology, Shiyan Taihe Hospital, Hubei University of Medicine, Shiyan City, Hubei Province, China

    Ping Liu

  4. Department of Clinical Laboratory Center, Central Hospital of Enshi Autonomous Prefecture, Enshi Clinical College of Wuhan University, Enshi, Hubei, China

    Tian Xiang

  5. Department of Emergency, The Affiliated Huai’an Hospital of Xuzhou Medical College and The Second People’s Hospital of Huai’an, Huai’an, China

    Shi Yan

  6. Department of Hematology, Huai’an First People’s Hospital, Nanjing Medical University, Huai’an, China

    Li Zhang

Authors
  1. Ai-Xin Hu
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  2. Zhi-Yong Huang
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  3. Ping Liu
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  4. Tian Xiang
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  5. Shi Yan
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  6. Li Zhang
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Corresponding authors

Correspondence to Shi Yan or Li Zhang.

Additional information

Ai-Xin Hu and Zhi-Yong Huang are co-first authors.

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13277_2016_5449_MOESM1_ESM.doc

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Hu, AX., Huang, ZY., Liu, P. et al. A new ten-gene risk fraction model serving as prognostic indicator for clinical outcome of multiple myeloma. Tumor Biol. 37, 15967–15975 (2016). https://doi.org/10.1007/s13277-016-5449-4

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  • DOI: https://doi.org/10.1007/s13277-016-5449-4

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