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Predicting the effect of 5‐fluorouracil–based adjuvant chemotherapy on colorectal cancer recurrence: A model using gene expression profiles

It is critical to identify patients with stage II and III colorectal cancer (CRC) who will benefit from adjuvant chemotherapy (ACT) after curative surgery, while the only use of clinical factors is insufficient to predict this beneficial effect. In this study, we performed genetic algorithm (GA) to...

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Autores principales: Chen, Quan, Gao, Peng, Song, Yongxi, Huang, Xuanzhang, Xiao, Qiong, Chen, Xiaowan, Lv, Xinger, Wang, Zhenning
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7196071/
https://www.ncbi.nlm.nih.gov/pubmed/32150672
http://dx.doi.org/10.1002/cam4.2952
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author Chen, Quan
Gao, Peng
Song, Yongxi
Huang, Xuanzhang
Xiao, Qiong
Chen, Xiaowan
Lv, Xinger
Wang, Zhenning
author_facet Chen, Quan
Gao, Peng
Song, Yongxi
Huang, Xuanzhang
Xiao, Qiong
Chen, Xiaowan
Lv, Xinger
Wang, Zhenning
author_sort Chen, Quan
collection PubMed
description It is critical to identify patients with stage II and III colorectal cancer (CRC) who will benefit from adjuvant chemotherapy (ACT) after curative surgery, while the only use of clinical factors is insufficient to predict this beneficial effect. In this study, we performed genetic algorithm (GA) to select ACT candidate genes, and built a predictive model of support vector machine (SVM) using gene expression profiles from the Gene Expression Omnibus database. The model contained four ACT candidate genes (EDEM1, MVD, SEMA5B, and WWP2) and TNM stage (stage II or III). After using Subpopulation Treatment Effect Pattern Plot to determine the optimal cutoff value of predictive scores, the validated patients from The Cancer Genome Atlas database can be divided into the predictive ACT‐benefit/‐futile groups. Patients in the predictive ACT‐benefit group with 5‐fluorouracil (5‐Fu)–based ACT had significantly longer relapse‐free survival (RFS) compared to those without ACT (P = .015); However, the difference in RFS in the predictive ACT‐futile group was insignificant (P = .596). The multivariable analysis found that the predictive groups were significantly associated with the effect of ACT (P (interaction) = .011). Consequently, we developed a predictive model based on the SVM and GA algorithm which was further validated to define patients who benefit from ACT on recurrence.
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spelling pubmed-71960712020-05-04 Predicting the effect of 5‐fluorouracil–based adjuvant chemotherapy on colorectal cancer recurrence: A model using gene expression profiles Chen, Quan Gao, Peng Song, Yongxi Huang, Xuanzhang Xiao, Qiong Chen, Xiaowan Lv, Xinger Wang, Zhenning Cancer Med Clinical Cancer Research It is critical to identify patients with stage II and III colorectal cancer (CRC) who will benefit from adjuvant chemotherapy (ACT) after curative surgery, while the only use of clinical factors is insufficient to predict this beneficial effect. In this study, we performed genetic algorithm (GA) to select ACT candidate genes, and built a predictive model of support vector machine (SVM) using gene expression profiles from the Gene Expression Omnibus database. The model contained four ACT candidate genes (EDEM1, MVD, SEMA5B, and WWP2) and TNM stage (stage II or III). After using Subpopulation Treatment Effect Pattern Plot to determine the optimal cutoff value of predictive scores, the validated patients from The Cancer Genome Atlas database can be divided into the predictive ACT‐benefit/‐futile groups. Patients in the predictive ACT‐benefit group with 5‐fluorouracil (5‐Fu)–based ACT had significantly longer relapse‐free survival (RFS) compared to those without ACT (P = .015); However, the difference in RFS in the predictive ACT‐futile group was insignificant (P = .596). The multivariable analysis found that the predictive groups were significantly associated with the effect of ACT (P (interaction) = .011). Consequently, we developed a predictive model based on the SVM and GA algorithm which was further validated to define patients who benefit from ACT on recurrence. John Wiley and Sons Inc. 2020-03-09 /pmc/articles/PMC7196071/ /pubmed/32150672 http://dx.doi.org/10.1002/cam4.2952 Text en © 2020 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Clinical Cancer Research
Chen, Quan
Gao, Peng
Song, Yongxi
Huang, Xuanzhang
Xiao, Qiong
Chen, Xiaowan
Lv, Xinger
Wang, Zhenning
Predicting the effect of 5‐fluorouracil–based adjuvant chemotherapy on colorectal cancer recurrence: A model using gene expression profiles
title Predicting the effect of 5‐fluorouracil–based adjuvant chemotherapy on colorectal cancer recurrence: A model using gene expression profiles
title_full Predicting the effect of 5‐fluorouracil–based adjuvant chemotherapy on colorectal cancer recurrence: A model using gene expression profiles
title_fullStr Predicting the effect of 5‐fluorouracil–based adjuvant chemotherapy on colorectal cancer recurrence: A model using gene expression profiles
title_full_unstemmed Predicting the effect of 5‐fluorouracil–based adjuvant chemotherapy on colorectal cancer recurrence: A model using gene expression profiles
title_short Predicting the effect of 5‐fluorouracil–based adjuvant chemotherapy on colorectal cancer recurrence: A model using gene expression profiles
title_sort predicting the effect of 5‐fluorouracil–based adjuvant chemotherapy on colorectal cancer recurrence: a model using gene expression profiles
topic Clinical Cancer Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7196071/
https://www.ncbi.nlm.nih.gov/pubmed/32150672
http://dx.doi.org/10.1002/cam4.2952
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