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Developing a Prognostic Gene Panel of Epithelial Ovarian Cancer Patients by a Machine Learning Model

Epithelial ovarian cancer patients usually relapse after primary management. We utilized the support vector machine algorithm to develop a model for the chemo-response using the Cancer Cell Line Encyclopedia (CCLE) and validated the model in The Cancer Genome Atlas (TCGA) and the GSE9891 dataset. Fi...

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Autores principales: Lu, Tzu-Pin, Kuo, Kuan-Ting, Chen, Ching-Hsuan, Chang, Ming-Cheng, Lin, Hsiu-Ping, Hu, Yu-Hao, Chiang, Ying-Cheng, Cheng, Wen-Fang, Chen, Chi-An
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6406249/
https://www.ncbi.nlm.nih.gov/pubmed/30823599
http://dx.doi.org/10.3390/cancers11020270
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author Lu, Tzu-Pin
Kuo, Kuan-Ting
Chen, Ching-Hsuan
Chang, Ming-Cheng
Lin, Hsiu-Ping
Hu, Yu-Hao
Chiang, Ying-Cheng
Cheng, Wen-Fang
Chen, Chi-An
author_facet Lu, Tzu-Pin
Kuo, Kuan-Ting
Chen, Ching-Hsuan
Chang, Ming-Cheng
Lin, Hsiu-Ping
Hu, Yu-Hao
Chiang, Ying-Cheng
Cheng, Wen-Fang
Chen, Chi-An
author_sort Lu, Tzu-Pin
collection PubMed
description Epithelial ovarian cancer patients usually relapse after primary management. We utilized the support vector machine algorithm to develop a model for the chemo-response using the Cancer Cell Line Encyclopedia (CCLE) and validated the model in The Cancer Genome Atlas (TCGA) and the GSE9891 dataset. Finally, we evaluated the feasibility of the model using ovarian cancer patients from our institute. The 10-gene predictive model demonstrated that the high response group had a longer recurrence-free survival (RFS) (log-rank test, p = 0.015 for TCGA, p = 0.013 for GSE9891 and p = 0.039 for NTUH) and overall survival (OS) (log-rank test, p = 0.002 for TCGA and p = 0.016 for NTUH). In a multivariate Cox hazard regression model, the predictive model (HR: 0.644, 95% CI: 0.436–0.952, p = 0.027) and residual tumor size < 1 cm (HR: 0.312, 95% CI: 0.170–0.573, p < 0.001) were significant factors for recurrence. The predictive model (HR: 0.511, 95% CI: 0.334–0.783, p = 0.002) and residual tumor size < 1 cm (HR: 0.252, 95% CI: 0.128–0.496, p < 0.001) were still significant factors for death. In conclusion, the patients of high response group stratified by the model had good response and favourable prognosis, whereas for the patients of medium to low response groups, introduction of other drugs or clinical trials might be beneficial.
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spelling pubmed-64062492019-03-21 Developing a Prognostic Gene Panel of Epithelial Ovarian Cancer Patients by a Machine Learning Model Lu, Tzu-Pin Kuo, Kuan-Ting Chen, Ching-Hsuan Chang, Ming-Cheng Lin, Hsiu-Ping Hu, Yu-Hao Chiang, Ying-Cheng Cheng, Wen-Fang Chen, Chi-An Cancers (Basel) Article Epithelial ovarian cancer patients usually relapse after primary management. We utilized the support vector machine algorithm to develop a model for the chemo-response using the Cancer Cell Line Encyclopedia (CCLE) and validated the model in The Cancer Genome Atlas (TCGA) and the GSE9891 dataset. Finally, we evaluated the feasibility of the model using ovarian cancer patients from our institute. The 10-gene predictive model demonstrated that the high response group had a longer recurrence-free survival (RFS) (log-rank test, p = 0.015 for TCGA, p = 0.013 for GSE9891 and p = 0.039 for NTUH) and overall survival (OS) (log-rank test, p = 0.002 for TCGA and p = 0.016 for NTUH). In a multivariate Cox hazard regression model, the predictive model (HR: 0.644, 95% CI: 0.436–0.952, p = 0.027) and residual tumor size < 1 cm (HR: 0.312, 95% CI: 0.170–0.573, p < 0.001) were significant factors for recurrence. The predictive model (HR: 0.511, 95% CI: 0.334–0.783, p = 0.002) and residual tumor size < 1 cm (HR: 0.252, 95% CI: 0.128–0.496, p < 0.001) were still significant factors for death. In conclusion, the patients of high response group stratified by the model had good response and favourable prognosis, whereas for the patients of medium to low response groups, introduction of other drugs or clinical trials might be beneficial. MDPI 2019-02-25 /pmc/articles/PMC6406249/ /pubmed/30823599 http://dx.doi.org/10.3390/cancers11020270 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lu, Tzu-Pin
Kuo, Kuan-Ting
Chen, Ching-Hsuan
Chang, Ming-Cheng
Lin, Hsiu-Ping
Hu, Yu-Hao
Chiang, Ying-Cheng
Cheng, Wen-Fang
Chen, Chi-An
Developing a Prognostic Gene Panel of Epithelial Ovarian Cancer Patients by a Machine Learning Model
title Developing a Prognostic Gene Panel of Epithelial Ovarian Cancer Patients by a Machine Learning Model
title_full Developing a Prognostic Gene Panel of Epithelial Ovarian Cancer Patients by a Machine Learning Model
title_fullStr Developing a Prognostic Gene Panel of Epithelial Ovarian Cancer Patients by a Machine Learning Model
title_full_unstemmed Developing a Prognostic Gene Panel of Epithelial Ovarian Cancer Patients by a Machine Learning Model
title_short Developing a Prognostic Gene Panel of Epithelial Ovarian Cancer Patients by a Machine Learning Model
title_sort developing a prognostic gene panel of epithelial ovarian cancer patients by a machine learning model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6406249/
https://www.ncbi.nlm.nih.gov/pubmed/30823599
http://dx.doi.org/10.3390/cancers11020270
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