A Novel Prognostic Model Predicts Overall Survival in Patients With Nasopharyngeal Carcinoma Based on Clinical Features and Blood Biomarkers release_s3ehbj633razlm64lkshcnuzzq

by Changchun Lai, Chunning Zhang, Hualiang Lv, Hanqing Huang, Xia Ke, Chuchan Zhou, Hao Chen, Shulin Chen, Lei Zhou

Released as a post by Research Square.

2020  

Abstract

<jats:title>Abstract</jats:title> <jats:bold>Background: </jats:bold>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. And assess its incremental value to TNM staging system, clinical treatment, and Epstein-Barr virus DNA (EBV DNA) for individual OS estimation. <jats:bold>Methods: </jats:bold>We retrospectively analyzed 519 consecutive NPC. A prognostic model was generated by using the Lasso-Cox regression model in training cohort (n= 346). Then comparison of predictive accuracy between the novel prognostic model, TNM staging, clinical treatment, and EBV DNA using concordance index (C-index), time-dependent ROC (tdROC), and decision curve analysis (DCA). Subsequently, a nomogram for OS incorporating the prognostic model, TNM staging and clinical treatment was built. Finally, we stratified patients into high- risk and low-risk groups according to the model risk score, and the survival time of these two groups was analyzed using Kaplan–Meier survival plots. All the results were validated in the independent validation cohort (n= 173). <jats:bold>Results: </jats:bold>Using the Lasso-Cox regression, a prognostic model was established consisting of 13 variables with respect to patient prognosis. The C-index, tdROC and DCA all showed the prognostic model had good predictive accuracy and discriminatory power than TNM staging, clinical treatment and EBV DNA in training cohort. Nomogram consisting of the prognostic model, TNM staging, clinical treatment and EBV DNA shown some superior net benefit. According to the model risk score, we split the patients into two subgroups: low- risk (risk score ≤ -1.423) and high-risk (risk score &gt; -1.423). There had significant differences in OS between the two subgroups of patients. In the validation cohort, similar results were obtained. <jats:bold>Conclusions: </jats:bold>The proposed novel prognostic model based on clinical features and serological markers represents a promising signature for estimating OS in NPC patients.
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