An Individualized Prognostic Model in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma Based on Serum Metabolomic Profiling

Abstract

Purpose: This study aims to evaluate the value of a serum metabolomics-based metabolic signature for locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients, thereby assisting clinical decisions.

Methods: In this retrospective study, a total of 320 LA-NPC patients were randomly divided into a training set (ca. 70%; n = 224) and a validation set (ca. 30%; n = 96). Serum samples were analyzed using widely targeted metabolomics. Univariate and multivariate Cox regression analyses were used to identify candidate metabolites related to progression-free survival (PFS). Patients were categorized into high-risk and low-risk groups based on the median metabolic risk score (Met score), and the PFS difference between the two groups was compared using Kaplan-Meier curves. The predictive performance of the metabolic signature was evaluated using the concordance index (C-index) and the time-dependent receiver operating characteristic (ROC), and a comprehensive nomogram was constructed using the Met score and other clinical factors.

Results: Nine metabolites were screened to build the metabolic signature and generate the Met score, which effectively separated patients into low- and high-risk groups. The C-index in the training and validation sets was 0.71 and 0.73, respectively. The 5-year PFS was 53.7% (95% CI, 45.12-63.86) in the high-risk group and 83.0% (95%CI, 76.31-90.26) in the low-risk group. During the construction of the nomogram, Met score, clinical stage, pre-treatment EBV DNA level, and gender were identified as independent prognostic factors for PFS. The predictive performance of the comprehensive model was better than that of the traditional model.

Conclusion: The metabolic signature developed through serum metabolomics is a reliable prognostic indicator of PFS in LA-NPC patients and has important clinical significance.

Keywords: biomarker; locally advanced nasopharyngeal carcinoma; metabolomics; prognostic model.

Figure 1

Workflow of the study design.

Figure 2

Forest plot of the nine selected metabolites related to the 5-year PFS. Index is the code of the metabolite in the Metware database. Compounds is the name of the substance corresponding to the index. HRs (95% CI) were calculated by applying a Cox regression analysis. Abbreviations: HR, hazard ratio. * indicates p < 0.05, ** indicates p < 0.01.

Figure 3

Risk score analysis of the 9-metabolite signature. (a) Met score for each patient in the training set. The dotted line represents the cutoff (0.92) used to divide patients into the high- and low-risk groups in the training set. (b) Distributions of Met score between patients with different PFS statuses. (c) Heatmap of expression levels of the nine PFS-related metabolites from the signature between low- and high-risk patients.

Figure 4

Kaplan–Meier survival curves and ROC curves for low- and high-risk groups based on the 9-metabolite signature to predict 5-year PFS. Kaplan–Meier curves for PFS stratified by the 9-metabolite signature in the training set (a) and the validation set (b). ROC curves comparing the predictive power of the 9-metabolite signature for PFS in the training set (c) and the validation set (d). Abbreviations: PFS, progression-free survival; ROC, receiver operating characteristic.

Figure 5

(a) Nomogram for 5-year PFS in patients with locally advanced nasopharyngeal carcinoma. Calibration curve of nomogram models for predicting 5-year PFS in the training set (b) and the validation set (c). Decision curve analysis of the nomogram for the PFS in the training set (d) and the validation set (e). The nomogram incorporating sex, overall stage, EBV DNA, and Met score was developed and presented. Abbreviations: EBV DNA, Epstein–Barr virus DNA.

Figure 6

KEGG pathway enrichment of metabolites in the 9-metabolite signature. (a) Bubble plot of KEGG pathway enrichment analyses. (b) Chord diagram for KEGG pathway. Abbreviations: KEGG, Kyoto Encyclopedia of Genes and Genomes.