A novel prognostic model predicts overall survival in patients with nasopharyngeal carcinoma based on clinical features and blood biomarkers.
Biomarkers, Tumor
/ blood
Cohort Studies
DNA Copy Number Variations
DNA, Viral
/ genetics
Decision Support Techniques
Female
Herpesvirus 4, Human
/ genetics
Humans
Kaplan-Meier Estimate
Male
Middle Aged
Models, Statistical
Nasopharyngeal Carcinoma
/ blood
Nasopharyngeal Neoplasms
/ blood
Neoplasm Staging
Nomograms
Prognosis
Regression Analysis
Retrospective Studies
lasso regression
model
nasopharyngeal carcinoma
nomogram
prognostic
Journal
Cancer medicine
ISSN: 2045-7634
Titre abrégé: Cancer Med
Pays: United States
ID NLM: 101595310
Informations de publication
Date de publication:
06 2021
06 2021
Historique:
revised:
25
02
2021
received:
27
10
2020
accepted:
26
02
2021
pubmed:
12
5
2021
medline:
18
12
2021
entrez:
11
5
2021
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 33973727
doi: 10.1002/cam4.3839
pmc: PMC8178501
doi:
Substances chimiques
Biomarkers, Tumor
0
DNA, Viral
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
3511-3523Informations de copyright
© 2021 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.
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