Cargando…

A novel prognostic model predicts overall survival in patients with nasopharyngeal carcinoma based on clinical features and blood biomarkers

This study aims to develop and validate a novel prognostic model to estimate overall survival (OS) in nasopharyngeal carcinoma (NPC) patients based on clinical features and blood biomarkers. We assessed the model's incremental value to the TNM staging system, clinical treatment, and Epstein‐Bar...

Descripción completa

Detalles Bibliográficos
Autores principales: Lai, Changchun, Zhang, Chunning, Lv, Hualiang, Huang, Hanqing, Ke, Xia, Zhou, Chuchan, Chen, Hao, Chen, Shulin, Zhou, Lei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8178501/
https://www.ncbi.nlm.nih.gov/pubmed/33973727
http://dx.doi.org/10.1002/cam4.3839
_version_ 1783703582894194688
author Lai, Changchun
Zhang, Chunning
Lv, Hualiang
Huang, Hanqing
Ke, Xia
Zhou, Chuchan
Chen, Hao
Chen, Shulin
Zhou, Lei
author_facet Lai, Changchun
Zhang, Chunning
Lv, Hualiang
Huang, Hanqing
Ke, Xia
Zhou, Chuchan
Chen, Hao
Chen, Shulin
Zhou, Lei
author_sort Lai, Changchun
collection PubMed
description This study aims to develop and validate a novel prognostic model to estimate overall survival (OS) in nasopharyngeal carcinoma (NPC) patients based on clinical features and blood biomarkers. We assessed the model's incremental value to the TNM staging system, clinical treatment, and Epstein‐Barr virus (EBV) DNA copy number for individual OS estimation. We retrospectively analyzed 519 consecutive patients with NPC. A prognostic model was generated using the Lasso regression model in the training cohort. Then we compared the predictive accuracy of the novel prognostic model with TNM staging, clinical treatment, and EBV DNA copy number using concordance index (C‐index), time‐dependent ROC (tdROC), and decision curve analysis (DCA). Subsequently, we built a nomogram for OS incorporating the prognostic model, TNM staging, and clinical treatment. Finally, we stratified patients into high‐risk and low‐risk groups according to the model risk score, and we analyzed the survival time of these two groups using Kaplan–Meier survival plots. All results were validated in the independent validation cohort. Using the Lasso regression, we established a prognostic model consisting of 13 variables with respect to patient prognosis. The C‐index, tdROC, and DCA showed that the prognostic model had good predictive accuracy and discriminatory power in the training cohort than did TNM staging, clinical treatment, and EBV DNA copy number. Nomogram consisting of the prognostic model, TNM staging, clinical treatment, and EBV DNA copy number showed some superior net benefit. Based on the model risk score, we split the patients into two subgroups: low‐risk (risk score ≤ −1.423) and high‐risk (risk score > −1.423). There were significant differences in OS between the two subgroups of patients. Similar results were observed in the validation cohort. The proposed novel prognostic model based on clinical features and serological markers may represent a promising tool for estimating OS in NPC patients.
format Online
Article
Text
id pubmed-8178501
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher John Wiley and Sons Inc.
record_format MEDLINE/PubMed
spelling pubmed-81785012021-06-15 A novel prognostic model predicts overall survival in patients with nasopharyngeal carcinoma based on clinical features and blood biomarkers Lai, Changchun Zhang, Chunning Lv, Hualiang Huang, Hanqing Ke, Xia Zhou, Chuchan Chen, Hao Chen, Shulin Zhou, Lei Cancer Med Clinical Cancer Research This study aims to develop and validate a novel prognostic model to estimate overall survival (OS) in nasopharyngeal carcinoma (NPC) patients based on clinical features and blood biomarkers. We assessed the model's incremental value to the TNM staging system, clinical treatment, and Epstein‐Barr virus (EBV) DNA copy number for individual OS estimation. We retrospectively analyzed 519 consecutive patients with NPC. A prognostic model was generated using the Lasso regression model in the training cohort. Then we compared the predictive accuracy of the novel prognostic model with TNM staging, clinical treatment, and EBV DNA copy number using concordance index (C‐index), time‐dependent ROC (tdROC), and decision curve analysis (DCA). Subsequently, we built a nomogram for OS incorporating the prognostic model, TNM staging, and clinical treatment. Finally, we stratified patients into high‐risk and low‐risk groups according to the model risk score, and we analyzed the survival time of these two groups using Kaplan–Meier survival plots. All results were validated in the independent validation cohort. Using the Lasso regression, we established a prognostic model consisting of 13 variables with respect to patient prognosis. The C‐index, tdROC, and DCA showed that the prognostic model had good predictive accuracy and discriminatory power in the training cohort than did TNM staging, clinical treatment, and EBV DNA copy number. Nomogram consisting of the prognostic model, TNM staging, clinical treatment, and EBV DNA copy number showed some superior net benefit. Based on the model risk score, we split the patients into two subgroups: low‐risk (risk score ≤ −1.423) and high‐risk (risk score > −1.423). There were significant differences in OS between the two subgroups of patients. Similar results were observed in the validation cohort. The proposed novel prognostic model based on clinical features and serological markers may represent a promising tool for estimating OS in NPC patients. John Wiley and Sons Inc. 2021-05-11 /pmc/articles/PMC8178501/ /pubmed/33973727 http://dx.doi.org/10.1002/cam4.3839 Text en © 2021 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://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
Lai, Changchun
Zhang, Chunning
Lv, Hualiang
Huang, Hanqing
Ke, Xia
Zhou, Chuchan
Chen, Hao
Chen, Shulin
Zhou, Lei
A novel prognostic model predicts overall survival in patients with nasopharyngeal carcinoma based on clinical features and blood biomarkers
title A novel prognostic model predicts overall survival in patients with nasopharyngeal carcinoma based on clinical features and blood biomarkers
title_full A novel prognostic model predicts overall survival in patients with nasopharyngeal carcinoma based on clinical features and blood biomarkers
title_fullStr A novel prognostic model predicts overall survival in patients with nasopharyngeal carcinoma based on clinical features and blood biomarkers
title_full_unstemmed A novel prognostic model predicts overall survival in patients with nasopharyngeal carcinoma based on clinical features and blood biomarkers
title_short A novel prognostic model predicts overall survival in patients with nasopharyngeal carcinoma based on clinical features and blood biomarkers
title_sort novel prognostic model predicts overall survival in patients with nasopharyngeal carcinoma based on clinical features and blood biomarkers
topic Clinical Cancer Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8178501/
https://www.ncbi.nlm.nih.gov/pubmed/33973727
http://dx.doi.org/10.1002/cam4.3839
work_keys_str_mv AT laichangchun anovelprognosticmodelpredictsoverallsurvivalinpatientswithnasopharyngealcarcinomabasedonclinicalfeaturesandbloodbiomarkers
AT zhangchunning anovelprognosticmodelpredictsoverallsurvivalinpatientswithnasopharyngealcarcinomabasedonclinicalfeaturesandbloodbiomarkers
AT lvhualiang anovelprognosticmodelpredictsoverallsurvivalinpatientswithnasopharyngealcarcinomabasedonclinicalfeaturesandbloodbiomarkers
AT huanghanqing anovelprognosticmodelpredictsoverallsurvivalinpatientswithnasopharyngealcarcinomabasedonclinicalfeaturesandbloodbiomarkers
AT kexia anovelprognosticmodelpredictsoverallsurvivalinpatientswithnasopharyngealcarcinomabasedonclinicalfeaturesandbloodbiomarkers
AT zhouchuchan anovelprognosticmodelpredictsoverallsurvivalinpatientswithnasopharyngealcarcinomabasedonclinicalfeaturesandbloodbiomarkers
AT chenhao anovelprognosticmodelpredictsoverallsurvivalinpatientswithnasopharyngealcarcinomabasedonclinicalfeaturesandbloodbiomarkers
AT chenshulin anovelprognosticmodelpredictsoverallsurvivalinpatientswithnasopharyngealcarcinomabasedonclinicalfeaturesandbloodbiomarkers
AT zhoulei anovelprognosticmodelpredictsoverallsurvivalinpatientswithnasopharyngealcarcinomabasedonclinicalfeaturesandbloodbiomarkers
AT laichangchun novelprognosticmodelpredictsoverallsurvivalinpatientswithnasopharyngealcarcinomabasedonclinicalfeaturesandbloodbiomarkers
AT zhangchunning novelprognosticmodelpredictsoverallsurvivalinpatientswithnasopharyngealcarcinomabasedonclinicalfeaturesandbloodbiomarkers
AT lvhualiang novelprognosticmodelpredictsoverallsurvivalinpatientswithnasopharyngealcarcinomabasedonclinicalfeaturesandbloodbiomarkers
AT huanghanqing novelprognosticmodelpredictsoverallsurvivalinpatientswithnasopharyngealcarcinomabasedonclinicalfeaturesandbloodbiomarkers
AT kexia novelprognosticmodelpredictsoverallsurvivalinpatientswithnasopharyngealcarcinomabasedonclinicalfeaturesandbloodbiomarkers
AT zhouchuchan novelprognosticmodelpredictsoverallsurvivalinpatientswithnasopharyngealcarcinomabasedonclinicalfeaturesandbloodbiomarkers
AT chenhao novelprognosticmodelpredictsoverallsurvivalinpatientswithnasopharyngealcarcinomabasedonclinicalfeaturesandbloodbiomarkers
AT chenshulin novelprognosticmodelpredictsoverallsurvivalinpatientswithnasopharyngealcarcinomabasedonclinicalfeaturesandbloodbiomarkers
AT zhoulei novelprognosticmodelpredictsoverallsurvivalinpatientswithnasopharyngealcarcinomabasedonclinicalfeaturesandbloodbiomarkers