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DNA Repair–Related Gene Signature in Predicting Prognosis of Colorectal Cancer Patients

Background: Increasing evidence have depicted that DNA repair–related genes (DRGs) are associated with the prognosis of colorectal cancer (CRC) patients. Thus, the aim of this study was to evaluate the impact of DNA repair–related gene signature (DRGS) in predicting the prognosis of CRC patients. Me...

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Autores principales: Lv, Min-Yi, Wang, Wei, Zhong, Min-Er, Cai, Du, Fan, Dejun, Li, Cheng-Hang, Kou, Wei-Bin, Huang, Ze-Ping, Duan, Xin, Hu, Chuling, Zhu, Qiqi, He, Xiaosheng, Gao, Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9048823/
https://www.ncbi.nlm.nih.gov/pubmed/35495147
http://dx.doi.org/10.3389/fgene.2022.872238
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author Lv, Min-Yi
Wang, Wei
Zhong, Min-Er
Cai, Du
Fan, Dejun
Li, Cheng-Hang
Kou, Wei-Bin
Huang, Ze-Ping
Duan, Xin
Hu, Chuling
Zhu, Qiqi
He, Xiaosheng
Gao, Feng
author_facet Lv, Min-Yi
Wang, Wei
Zhong, Min-Er
Cai, Du
Fan, Dejun
Li, Cheng-Hang
Kou, Wei-Bin
Huang, Ze-Ping
Duan, Xin
Hu, Chuling
Zhu, Qiqi
He, Xiaosheng
Gao, Feng
author_sort Lv, Min-Yi
collection PubMed
description Background: Increasing evidence have depicted that DNA repair–related genes (DRGs) are associated with the prognosis of colorectal cancer (CRC) patients. Thus, the aim of this study was to evaluate the impact of DNA repair–related gene signature (DRGS) in predicting the prognosis of CRC patients. Method: In this study, we retrospectively analyzed the gene expression profiles from six CRC cohorts. A total of 1,768 CRC patients with complete prognostic information were divided into the training cohort (n = 566) and two validation cohorts (n = 624 and 578, respectively). The LASSO Cox model was applied to construct a prediction model. To further validate the clinical significance of the model, we also validated the model with Genomics of Drug Sensitivity in Cancer (GDSC) and an advanced clear cell renal cell carcinoma (ccRCC) immunotherapy data set. Results: We constructed a prognostic DRGS consisting of 11 different genes to stratify patients into high- and low-risk groups. Patients in the high-risk groups had significantly worse disease-free survival (DFS) than those in the low-risk groups in all cohorts [training cohort: hazard ratio (HR) = 2.40, p < 0.001, 95% confidence interval (CI) = 1.67–3.44; validation-1: HR = 2.20, p < 0.001, 95% CI = 1.38–3.49 and validation-2 cohort: HR = 2.12, p < 0.001, 95% CI = 1.40–3.21). By validating the model with GDSC, we could see that among the chemotherapeutic drugs such as oxaliplatin, 5-fluorouracil, and irinotecan, the IC50 of the cell line in the low-risk group was lower. By validating the model with the ccRCC immunotherapy data set, we can clearly see that the overall survival (OS) of the objective response rate (ORR) with complete response (CR) and partial response (PR) in the low-risk group was the best. Conclusions: DRGS is a favorable prediction model for patients with CRC, and our model can predict the response of cell lines to chemotherapeutic agents and potentially predict the response of patients to immunotherapy.
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spelling pubmed-90488232022-04-29 DNA Repair–Related Gene Signature in Predicting Prognosis of Colorectal Cancer Patients Lv, Min-Yi Wang, Wei Zhong, Min-Er Cai, Du Fan, Dejun Li, Cheng-Hang Kou, Wei-Bin Huang, Ze-Ping Duan, Xin Hu, Chuling Zhu, Qiqi He, Xiaosheng Gao, Feng Front Genet Genetics Background: Increasing evidence have depicted that DNA repair–related genes (DRGs) are associated with the prognosis of colorectal cancer (CRC) patients. Thus, the aim of this study was to evaluate the impact of DNA repair–related gene signature (DRGS) in predicting the prognosis of CRC patients. Method: In this study, we retrospectively analyzed the gene expression profiles from six CRC cohorts. A total of 1,768 CRC patients with complete prognostic information were divided into the training cohort (n = 566) and two validation cohorts (n = 624 and 578, respectively). The LASSO Cox model was applied to construct a prediction model. To further validate the clinical significance of the model, we also validated the model with Genomics of Drug Sensitivity in Cancer (GDSC) and an advanced clear cell renal cell carcinoma (ccRCC) immunotherapy data set. Results: We constructed a prognostic DRGS consisting of 11 different genes to stratify patients into high- and low-risk groups. Patients in the high-risk groups had significantly worse disease-free survival (DFS) than those in the low-risk groups in all cohorts [training cohort: hazard ratio (HR) = 2.40, p < 0.001, 95% confidence interval (CI) = 1.67–3.44; validation-1: HR = 2.20, p < 0.001, 95% CI = 1.38–3.49 and validation-2 cohort: HR = 2.12, p < 0.001, 95% CI = 1.40–3.21). By validating the model with GDSC, we could see that among the chemotherapeutic drugs such as oxaliplatin, 5-fluorouracil, and irinotecan, the IC50 of the cell line in the low-risk group was lower. By validating the model with the ccRCC immunotherapy data set, we can clearly see that the overall survival (OS) of the objective response rate (ORR) with complete response (CR) and partial response (PR) in the low-risk group was the best. Conclusions: DRGS is a favorable prediction model for patients with CRC, and our model can predict the response of cell lines to chemotherapeutic agents and potentially predict the response of patients to immunotherapy. Frontiers Media S.A. 2022-04-11 /pmc/articles/PMC9048823/ /pubmed/35495147 http://dx.doi.org/10.3389/fgene.2022.872238 Text en Copyright © 2022 Lv, Wang, Zhong, Cai, Fan, Li, Kou, Huang, Duan, Hu, Zhu, He and Gao. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Lv, Min-Yi
Wang, Wei
Zhong, Min-Er
Cai, Du
Fan, Dejun
Li, Cheng-Hang
Kou, Wei-Bin
Huang, Ze-Ping
Duan, Xin
Hu, Chuling
Zhu, Qiqi
He, Xiaosheng
Gao, Feng
DNA Repair–Related Gene Signature in Predicting Prognosis of Colorectal Cancer Patients
title DNA Repair–Related Gene Signature in Predicting Prognosis of Colorectal Cancer Patients
title_full DNA Repair–Related Gene Signature in Predicting Prognosis of Colorectal Cancer Patients
title_fullStr DNA Repair–Related Gene Signature in Predicting Prognosis of Colorectal Cancer Patients
title_full_unstemmed DNA Repair–Related Gene Signature in Predicting Prognosis of Colorectal Cancer Patients
title_short DNA Repair–Related Gene Signature in Predicting Prognosis of Colorectal Cancer Patients
title_sort dna repair–related gene signature in predicting prognosis of colorectal cancer patients
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9048823/
https://www.ncbi.nlm.nih.gov/pubmed/35495147
http://dx.doi.org/10.3389/fgene.2022.872238
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