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Predicting the survival time for diffuse large B-cell lymphoma using microarray data

The present study was conducted to predict survival time in patients with diffuse large B-cell lymphoma, DLBCL, based on microarray data using Cox regression model combined with seven dimension reduction methods. This historical cohort included 2042 gene expression measurements from 40 patients with...

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Autores principales: Khoshhali, Mehri, Mahjub, Hossein, Saidijam, Massoud, Poorolajal, Jalal, Soltanian, Ali Reza
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Library Publishing Media 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3410377/
https://www.ncbi.nlm.nih.gov/pubmed/23173013
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author Khoshhali, Mehri
Mahjub, Hossein
Saidijam, Massoud
Poorolajal, Jalal
Soltanian, Ali Reza
author_facet Khoshhali, Mehri
Mahjub, Hossein
Saidijam, Massoud
Poorolajal, Jalal
Soltanian, Ali Reza
author_sort Khoshhali, Mehri
collection PubMed
description The present study was conducted to predict survival time in patients with diffuse large B-cell lymphoma, DLBCL, based on microarray data using Cox regression model combined with seven dimension reduction methods. This historical cohort included 2042 gene expression measurements from 40 patients with DLBCL. In order to predict survival, a combination of Cox regression model was used with seven methods for dimension reduction or shrinkage including univariate selection, forward stepwise selection, principal component regression, supervised principal component regression, partial least squares regression, ridge regression and Losso. The capacity of predictions was examined by three different criteria including log rank test, prognostic index and deviance. MATLAB r2008a and RKWard software were used for data analysis. Based on our findings, performance of ridge regression was better than other methods. Based on ridge regression coefficients and a given cut point value, 16 genes were selected. By using forward stepwise selection method in Cox regression model, it was indicated that the expression of genes GENE3555X and GENE3807X decreased the survival time (P=0.008 and P=0.003, respectively), whereas the genes GENE3228X and GENE1551X increased survival time (P=0.002 and P<0.001, respectively). This study indicated that ridge regression method had higher capacity than other dimension reduction methods for the prediction of survival time in patients with DLBCL. Furthermore, a combination of statistical methods and microarray data could help to detect influential genes in survival.
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spelling pubmed-34103772012-11-21 Predicting the survival time for diffuse large B-cell lymphoma using microarray data Khoshhali, Mehri Mahjub, Hossein Saidijam, Massoud Poorolajal, Jalal Soltanian, Ali Reza J Mol Genet Med Research Report The present study was conducted to predict survival time in patients with diffuse large B-cell lymphoma, DLBCL, based on microarray data using Cox regression model combined with seven dimension reduction methods. This historical cohort included 2042 gene expression measurements from 40 patients with DLBCL. In order to predict survival, a combination of Cox regression model was used with seven methods for dimension reduction or shrinkage including univariate selection, forward stepwise selection, principal component regression, supervised principal component regression, partial least squares regression, ridge regression and Losso. The capacity of predictions was examined by three different criteria including log rank test, prognostic index and deviance. MATLAB r2008a and RKWard software were used for data analysis. Based on our findings, performance of ridge regression was better than other methods. Based on ridge regression coefficients and a given cut point value, 16 genes were selected. By using forward stepwise selection method in Cox regression model, it was indicated that the expression of genes GENE3555X and GENE3807X decreased the survival time (P=0.008 and P=0.003, respectively), whereas the genes GENE3228X and GENE1551X increased survival time (P=0.002 and P<0.001, respectively). This study indicated that ridge regression method had higher capacity than other dimension reduction methods for the prediction of survival time in patients with DLBCL. Furthermore, a combination of statistical methods and microarray data could help to detect influential genes in survival. Library Publishing Media 2012-05-23 /pmc/articles/PMC3410377/ /pubmed/23173013 Text en © Copyright The Author(s): http://creativecommons.org/licenses/by-nc/2.5 Published by Library Publishing Media. This is an open access article, published under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5). This license permits non-commercial use, distribution and reproduction of the article, provided the original work is appropriately acknowledged with correct citation details.
spellingShingle Research Report
Khoshhali, Mehri
Mahjub, Hossein
Saidijam, Massoud
Poorolajal, Jalal
Soltanian, Ali Reza
Predicting the survival time for diffuse large B-cell lymphoma using microarray data
title Predicting the survival time for diffuse large B-cell lymphoma using microarray data
title_full Predicting the survival time for diffuse large B-cell lymphoma using microarray data
title_fullStr Predicting the survival time for diffuse large B-cell lymphoma using microarray data
title_full_unstemmed Predicting the survival time for diffuse large B-cell lymphoma using microarray data
title_short Predicting the survival time for diffuse large B-cell lymphoma using microarray data
title_sort predicting the survival time for diffuse large b-cell lymphoma using microarray data
topic Research Report
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3410377/
https://www.ncbi.nlm.nih.gov/pubmed/23173013
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