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Predicting the Survival Time for Bladder Cancer Using an Additive Hazards Model in Microarray Data

BACKGROUND: One substantial part of microarray studies is to predict patients’ survival based on their gene expression profile. Variable selection techniques are powerful tools to handle high dimensionality in analysis of microarray data. However, these techniques have not been investigated in compe...

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Autores principales: TAPAK, Leili, MAHJUB, Hossein, SADEGHIFAR, Majid, SAIDIJAM, Massoud, POOROLAJAL, Jalal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Tehran University of Medical Sciences 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4841879/
https://www.ncbi.nlm.nih.gov/pubmed/27114989
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author TAPAK, Leili
MAHJUB, Hossein
SADEGHIFAR, Majid
SAIDIJAM, Massoud
POOROLAJAL, Jalal
author_facet TAPAK, Leili
MAHJUB, Hossein
SADEGHIFAR, Majid
SAIDIJAM, Massoud
POOROLAJAL, Jalal
author_sort TAPAK, Leili
collection PubMed
description BACKGROUND: One substantial part of microarray studies is to predict patients’ survival based on their gene expression profile. Variable selection techniques are powerful tools to handle high dimensionality in analysis of microarray data. However, these techniques have not been investigated in competing risks setting. This study aimed to investigate the performance of four sparse variable selection methods in estimating the survival time. METHODS: The data included 1381 gene expression measurements and clinical information from 301 patients with bladder cancer operated in the years 1987 to 2000 in hospitals in Denmark, Sweden, Spain, France, and England. Four methods of the least absolute shrinkage and selection operator, smoothly clipped absolute deviation, the smooth integration of counting and absolute deviation and elastic net were utilized for simultaneous variable selection and estimation under an additive hazards model. The criteria of area under ROC curve, Brier score and c-index were used to compare the methods. RESULTS: The median follow-up time for all patients was 47 months. The elastic net approach was indicated to outperform other methods. The elastic net had the lowest integrated Brier score (0.137±0.07) and the greatest median of the over-time AUC and C-index (0.803±0.06 and 0.779±0.13, respectively). Five out of 19 selected genes by the elastic net were significant (P<0.05) under an additive hazards model. It was indicated that the expression of RTN4, SON, IGF1R and CDC20 decrease the survival time, while the expression of SMARCAD1 increase it. CONCLUSION: The elastic net had higher capability than the other methods for the prediction of survival time in patients with bladder cancer in the presence of competing risks base on additive hazards model.
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spelling pubmed-48418792016-04-25 Predicting the Survival Time for Bladder Cancer Using an Additive Hazards Model in Microarray Data TAPAK, Leili MAHJUB, Hossein SADEGHIFAR, Majid SAIDIJAM, Massoud POOROLAJAL, Jalal Iran J Public Health Original Article BACKGROUND: One substantial part of microarray studies is to predict patients’ survival based on their gene expression profile. Variable selection techniques are powerful tools to handle high dimensionality in analysis of microarray data. However, these techniques have not been investigated in competing risks setting. This study aimed to investigate the performance of four sparse variable selection methods in estimating the survival time. METHODS: The data included 1381 gene expression measurements and clinical information from 301 patients with bladder cancer operated in the years 1987 to 2000 in hospitals in Denmark, Sweden, Spain, France, and England. Four methods of the least absolute shrinkage and selection operator, smoothly clipped absolute deviation, the smooth integration of counting and absolute deviation and elastic net were utilized for simultaneous variable selection and estimation under an additive hazards model. The criteria of area under ROC curve, Brier score and c-index were used to compare the methods. RESULTS: The median follow-up time for all patients was 47 months. The elastic net approach was indicated to outperform other methods. The elastic net had the lowest integrated Brier score (0.137±0.07) and the greatest median of the over-time AUC and C-index (0.803±0.06 and 0.779±0.13, respectively). Five out of 19 selected genes by the elastic net were significant (P<0.05) under an additive hazards model. It was indicated that the expression of RTN4, SON, IGF1R and CDC20 decrease the survival time, while the expression of SMARCAD1 increase it. CONCLUSION: The elastic net had higher capability than the other methods for the prediction of survival time in patients with bladder cancer in the presence of competing risks base on additive hazards model. Tehran University of Medical Sciences 2016-02 /pmc/articles/PMC4841879/ /pubmed/27114989 Text en Copyright© Iranian Public Health Association & Tehran University of Medical Sciences This work is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License which allows users to read, copy, distribute and make derivative works for non-commercial purposes from the material, as long as the author of the original work is cited properly.
spellingShingle Original Article
TAPAK, Leili
MAHJUB, Hossein
SADEGHIFAR, Majid
SAIDIJAM, Massoud
POOROLAJAL, Jalal
Predicting the Survival Time for Bladder Cancer Using an Additive Hazards Model in Microarray Data
title Predicting the Survival Time for Bladder Cancer Using an Additive Hazards Model in Microarray Data
title_full Predicting the Survival Time for Bladder Cancer Using an Additive Hazards Model in Microarray Data
title_fullStr Predicting the Survival Time for Bladder Cancer Using an Additive Hazards Model in Microarray Data
title_full_unstemmed Predicting the Survival Time for Bladder Cancer Using an Additive Hazards Model in Microarray Data
title_short Predicting the Survival Time for Bladder Cancer Using an Additive Hazards Model in Microarray Data
title_sort predicting the survival time for bladder cancer using an additive hazards model in microarray data
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4841879/
https://www.ncbi.nlm.nih.gov/pubmed/27114989
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