Cross-Platform Gene Signature to Predict Survival Outcomes for Nasopharyngeal Carcinoma.

Abstract

Purpose: Nasopharyngeal carcinoma (NPC) is a multifactorial malignancy often diagnosed at an advanced stage due to nonspecific early symptoms. Accurate prognostic stratification is essential for individualized therapy but remains challenging because of biological and clinical heterogeneity. This study aimed to develop and validate a gene expression-based prognostic signature for locally advanced NPC.

Methods: This retrospective biomarker investigation combined transcriptomic profiling and survival analyses. A prognostic model was constructed using LASSO-Cox regression in the discovery cohort (N = 99) from the multicenter, randomized phase III trial NPC-0501, and validated in independent cohorts (N = 133) from Queen Elizabeth Hospital (QEH), Hong Kong, and Sun Yat-sen University Cancer Center (SYS), Guangzhou, using different profiling methods. Mechanisms underlying the signature were explored using single-cell RNA-seq (scRNA-seq) data. The primary outcome was 3-year progression-free survival (PFS), with 5-year overall survival (OS) as a secondary end point. Performance was evaluated using hazard ratios (HRs) and survival probabilities (SP).

Results: A 5-gene signature (SPP1, IL18BP, PALMD, WDR35, and SOCS6) predicted 3-year PFS in the NPC-0501 cohort (HR, 0.046 [95% CI, 0.011 to 0.190]; P < .001), separating high-risk from low-risk patients (SP, 42.9% v 96.0%; P < .0001). It predicted survival in both validation cohorts, with associations with 5-year OS in QEH (SP, 72.7% v 100%; P < .001) and 3-year PFS in SYS (SP, 70.5% v 93.2%; P = .0059). It outperformed a published metastasis-related model. Single-cell analyses showed SPP1 enrichment in M2 macrophages, linking high risk with an immunosuppressive tumor microenvironment.

Conclusion: A robust five-gene signature was established and validated for prognostic stratification of locally advanced NPC. Reproducibility across transcriptomic platforms and biological relevance support clinical application to guide personalized treatment.