Bayesian Survival Analysis of High-Dimensional Microarray Data for Mantle Cell Lymphoma Patients

  • Moslemi, Azam (Department of Biostatistics & Epidemiology, Hamadan University of Medical Sciences) ;
  • Mahjub, Hossein (Research Center for Health Sciences and Department of Biostatistics & Epidemiology, School of Public Health, Hamadan University of Medical Sciences) ;
  • Saidijam, Massoud (Research Center for Molecular Medicine, Department of Molecular Medicine and Genetics, School of Medicine, Hamadan University of Medical Sciences) ;
  • Poorolajal, Jalal (Research Center for Health Sciences and Department of Biostatistics & Epidemiology, School of Public Health, Hamadan University of Medical Sciences) ;
  • Soltanian, Ali Reza (Research Center for Health Sciences and Department of Biostatistics & Epidemiology, School of Public Health, Hamadan University of Medical Sciences)
  • Published : 2016.02.05


Background: Survival time of lymphoma patients can be estimated with the help of microarray technology. In this study, with the use of iterative Bayesian Model Averaging (BMA) method, survival time of Mantle Cell Lymphoma patients (MCL) was estimated and in reference to the findings, patients were divided into two high-risk and low-risk groups. Materials and Methods: In this study, gene expression data of MCL patients were used in order to select a subset of genes for survival analysis with microarray data, using the iterative BMA method. To evaluate the performance of the method, patients were divided into high-risk and low-risk based on their scores. Performance prediction was investigated using the log-rank test. The bioconductor package "iterativeBMAsurv" was applied with R statistical software for classification and survival analysis. Results: In this study, 25 genes associated with survival for MCL patients were identified across 132 selected models. The maximum likelihood estimate coefficients of the selected genes and the posterior probabilities of the selected models were obtained from training data. Using this method, patients could be separated into high-risk and low-risk groups with high significance (p<0.001). Conclusions: The iterative BMA algorithm has high precision and ability for survival analysis. This method is capable of identifying a few predictive variables associated with survival, among many variables in a set of microarray data. Therefore, it can be used as a low-cost diagnostic tool in clinical research.


  1. Adamson P, Bray F, Costantini A, et al (2007). Time trends in the registration of Hodgkin and non- Hodgkin lymphomas in Europe. Eur J Cancer, 43, 391-401.
  2. Annest A, Bumgarner RE, Raftery AE, et al (2009). Iterative Bayesian Model Averaging: a method for the application of survival analysis to high-dimensional microarray data. BMC Bioinformatics, 10, 72.
  3. Campo E, Raffeld M, Jaffe ES (1999). Mantle-cell lymphoma. Seminars Hematol, 36, 115-27.
  4. Chang H-C, Han L, Goswami R, et al (2009). Impaired development of human Th1 cells in patients with deficient expression of STAT4.
  5. Fisher p (2005). Non-Hodgkin's lymphoma. Practice Oncol, 1948-52.
  6. Furnival G, Wilson R (1974). Regression by Leaps and Bounds. Technometrics, 16, 499-511.
  7. Hu X-T, Chen W, Wang D, et al (2008). The proteasome subunit PSMA7 located on the 20q13 amplicon is overexpressed and associated with liver metastasis in colorectal cancer. Oncology Reports, 19, 441-6.
  8. Kass RE, Raftery AE (1993). Bayes factors and model uncertainty.
  9. Li J, Duan Y, Ruan X (2007). A novel hybrid approach to selecting marker genes for cancer classification using gene expression data. Bioinformatics Biomedical Engineering, 264-7.
  10. Li W, Wu BA, Zeng YM, et al (2004). Epstein-Barr virus in hepatocellular carcinogenesis. World J Gastroenterol, 10, 3409-13.
  11. Liu Y, Zhu X, Zhu J, et al (2007). Identification of differential expression of genes in hepatocellular carcinoma by suppression subtractive hybridization combined cDNA microarray. Oncol Reports, 18, 943-51.
  12. Ma X-J, Dahiya S, Richardson E, et al (2009). Gene expression profiling of the tumor microenvironment during breast cancer progression. Breast Cancer Res, 11.
  13. Madigan D, Raftery AE (1994). Model selection and accounting for model uncertainty in graphical models using occamis Window. J American Statistical Associat, 89, 1335-46.
  14. Midorikawa Y, Tsutsumi S, Taniguchi H, et al (2002). Identification of genes associated with dedifferentiation of hepatocellular carcinoma with expression profiling analysis Jpn J Cancer Res, 93, 636-43.
  15. Muller I, Wischnewski F, Pantel K, et al (2010). RPerseoarmch aortitclee r- and cell-specific epigenetic regulation of CD44, Cyclin D2, GLIPR1 and PTEN by Methyl-CpG binding proteins and histone modifications. BMC Cancer, 10, 1-15.
  16. Park JC, Chae YK, Son CH, et al (2008). Epigenetic silencing of human T (brachyury homologue) gene in non-small-cell lung cancer. Biochemical Biophysical Res Communicat, 365, 221-6.
  17. Raftery A (1995). Bayesian model selection in Social Research. Sociological methodol, 25, 111-96.
  18. Rosenwald A, Wright G, Wiestner A, et al (2003). The proliferation gene expression signature is a quantitative integrator of oncogenic events that predicts survival in mantle cell lymphoma. Cancer Cell, 3, 185-97.
  19. Rust R, Visser L, van J, et al (2005). High expression of calcium-binding proteins, S100A10, S100A11 and CALM2 in anaplastic large cell lymphoma. British J Haematol, 131, 596-608.
  20. Swerdlow SH, Williams ME (2002). From centrocytic to mantle cell. lymphoma: a clinicopathologic and molecular review of 3 decades. Human Pathology, 33, 7-20.
  21. Wan D, He M, Wang J, et al (2004). Two variants of the human hepatocellular carcinoma-associated HCAP1 gene and their effect on the growth of the human liver cancer cell line Hep3B. Genes, Chromosomes Cancer, 39, 48-58.

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