Distant Metastasis Risk Definition by Tumor Biomarkers Integrated Nomogram Approach for Locally Advanced Nasopharyngeal Carcinoma

Abstract

Identifying metastasis remains a challenge for death control and tailored therapy for nasopharyngeal carcinoma (NPC). Here, we addressed this by designing a nomogram-based Cox proportional regression model through integrating a panel of tumor biomarkers. A total of 147 locally patients with advanced NPC, derived from a randomized phase III clinical trial, were enrolled. We constructed the model by selecting the variables from 31 tumor biomarkers, including 6 pathological signaling pathway molecules and 3 Epstein-Barr virus-related serological variables. Through the least absolute shrinkage and selection operator (LASSO) Cox proportional regression analysis, a nomogram was designed to refine the metastasis risk of each NPC individuals. Using the LASSO Cox regression model, we constructed a 9 biomarkers-based prognostic nomogram: Beclin 1, Aurora-A, Cyclin D1, Ki-67, P27, Bcl-2, MMP-9, 14-3-3σ, and VCA-IgA. The time-dependence receiver operating characteristic analysis at 1, 3, and 5 years showed an appealing prognostic accuracy with the area under the curve of 0.830, 0.827, and 0.817, respectively. In the validation subset, the concordance index of this nomogram reached to 0.64 to identify the individual metastasis pattern. Supporting by this nomogram algorithm, the individual metastasis risk might be refined personally and potentially guiding the treatment decisions and target therapy against the related signaling pathways for patients with locally advanced NPC.