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A risk prediction model of gene signatures in ovarian cancer through bagging of GA-XGBoost models
INTRODUCTION: Ovarian cancer (OC) is one of the most frequent gynecologic cancers among women, and high-accuracy risk prediction techniques are essential to effectively select the best intervention strategies and clinical management for OC patients at different risk levels. Current risk prediction m...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8132202/ https://www.ncbi.nlm.nih.gov/pubmed/34026291 http://dx.doi.org/10.1016/j.jare.2020.11.006 |
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author | Hsiao, Yi-Wen Tao, Chun-Liang Chuang, Eric Y. Lu, Tzu-Pin |
author_facet | Hsiao, Yi-Wen Tao, Chun-Liang Chuang, Eric Y. Lu, Tzu-Pin |
author_sort | Hsiao, Yi-Wen |
collection | PubMed |
description | INTRODUCTION: Ovarian cancer (OC) is one of the most frequent gynecologic cancers among women, and high-accuracy risk prediction techniques are essential to effectively select the best intervention strategies and clinical management for OC patients at different risk levels. Current risk prediction models used in OC have low sensitivity, and few of them are able to identify OC patients at high risk of mortality, which would both optimize the treatment of high-risk patients and prevent unnecessary medical intervention in those at low risk. OBJECTIVES: To this end, we have developed a bagging-based algorithm with GA-XGBoost models that predicts the risk of death from OC using gene expression profiles. METHODS: Four gene expression datasets from public sources were used as training (n = 1) or validation (n = 3) sets. The performance of our proposed algorithm was compared with fine-tuning and other existing methods. Moreover, the biological function of selected genetic features was further interpreted, and the response to a panel of approved drugs was predicted for different risk levels. RESULTS: The proposed algorithm showed good sensitivity (74–100%) in the validation sets, compared with two simple models whose sensitivity only reached 47% and 60%. The prognostic gene signature used in this study was highly connected to AKT, a key component of the PI3K/AKT/mTOR signaling pathway, which influences the tumorigenesis, proliferation, and progression of OC. CONCLUSION: These findings demonstrated an improvement in the sensitivity of risk classification of OC patients with our risk prediction models compared with other methods. Ongoing effort is needed to validate the outcomes of this approach for precise clinical treatment. |
format | Online Article Text |
id | pubmed-8132202 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-81322022021-05-21 A risk prediction model of gene signatures in ovarian cancer through bagging of GA-XGBoost models Hsiao, Yi-Wen Tao, Chun-Liang Chuang, Eric Y. Lu, Tzu-Pin J Adv Res Medicine INTRODUCTION: Ovarian cancer (OC) is one of the most frequent gynecologic cancers among women, and high-accuracy risk prediction techniques are essential to effectively select the best intervention strategies and clinical management for OC patients at different risk levels. Current risk prediction models used in OC have low sensitivity, and few of them are able to identify OC patients at high risk of mortality, which would both optimize the treatment of high-risk patients and prevent unnecessary medical intervention in those at low risk. OBJECTIVES: To this end, we have developed a bagging-based algorithm with GA-XGBoost models that predicts the risk of death from OC using gene expression profiles. METHODS: Four gene expression datasets from public sources were used as training (n = 1) or validation (n = 3) sets. The performance of our proposed algorithm was compared with fine-tuning and other existing methods. Moreover, the biological function of selected genetic features was further interpreted, and the response to a panel of approved drugs was predicted for different risk levels. RESULTS: The proposed algorithm showed good sensitivity (74–100%) in the validation sets, compared with two simple models whose sensitivity only reached 47% and 60%. The prognostic gene signature used in this study was highly connected to AKT, a key component of the PI3K/AKT/mTOR signaling pathway, which influences the tumorigenesis, proliferation, and progression of OC. CONCLUSION: These findings demonstrated an improvement in the sensitivity of risk classification of OC patients with our risk prediction models compared with other methods. Ongoing effort is needed to validate the outcomes of this approach for precise clinical treatment. Elsevier 2020-11-11 /pmc/articles/PMC8132202/ /pubmed/34026291 http://dx.doi.org/10.1016/j.jare.2020.11.006 Text en © 2020 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Medicine Hsiao, Yi-Wen Tao, Chun-Liang Chuang, Eric Y. Lu, Tzu-Pin A risk prediction model of gene signatures in ovarian cancer through bagging of GA-XGBoost models |
title | A risk prediction model of gene signatures in ovarian cancer through bagging of GA-XGBoost models |
title_full | A risk prediction model of gene signatures in ovarian cancer through bagging of GA-XGBoost models |
title_fullStr | A risk prediction model of gene signatures in ovarian cancer through bagging of GA-XGBoost models |
title_full_unstemmed | A risk prediction model of gene signatures in ovarian cancer through bagging of GA-XGBoost models |
title_short | A risk prediction model of gene signatures in ovarian cancer through bagging of GA-XGBoost models |
title_sort | risk prediction model of gene signatures in ovarian cancer through bagging of ga-xgboost models |
topic | Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8132202/ https://www.ncbi.nlm.nih.gov/pubmed/34026291 http://dx.doi.org/10.1016/j.jare.2020.11.006 |
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