Numerous inflammatory indicators have been demonstrated to be strongly correlated with tumor prognosis. However, the association between inflammatory indicators and the prognosis of patients with nasopharyngeal carcinoma (NPC) receiving treatment with programmed death receptor-1 (PD-1) immunosuppressant monoclonal antibodies remains uncertain. Inflammatory indicators in peripheral blood were collected from 161 NPC patients at 3 weeks after initial PD-1 treatment. Through univariate and multivariate analyses, as well as nomogram and survival analyses, we aimed to identify independent prognostic factors related to 1-year progression-free survival (PFS). Subsequently, a prognostic nomogram was devised, and its predictive and discriminating abilities were assessed utilizing calibration curves and the concordance index. Our univariate and multivariate analyses indicated that age ( P = .012), M stage ( P < .001), and systemic immune-inflammation index (SII) during the third week following initial PD-1 treatment (SII3, P = .005) were independently correlated with the 1-year PFS of NPC patients after PD-1 treatment. Notably, we constructed a novel nomogram based on the SII3, age, and M stage. Importantly, utilizing the derived cutoff point from the nomogram, the high-risk group exhibited significantly shorter PFS than did the low-risk group ( P < .001). Furthermore, the nomogram demonstrated a greater concordance index for PFS than did the tumor node metastasis stage within the entire cohort. We successfully developed a nomogram that integrates the SII3 and clinical markers to accurately predict the 1-year PFS of NPC patients receiving PD-1 inhibitor treatment.

Nasopharyngeal carcinoma (NPC) represents a highly prevalent and aggressive malignancy endemic to Southeast Asia. Early and accurate diagnosis is critical to improving survival outcomes; however, the absence of robust, stage-specific biomarkers remains a key obstacle to clinical implementation of early screening strategies. We performed untargeted serum proteomic profiling using mass spectrometry in 15 treatment-naïve early-stage NPC patients and 15 VCA-IgA-positive healthy controls. Bioinformatics analyses were conducted to identify differentially expressed proteins (DEPs). Machine learning (random forest combined with recursive feature elimination) was employed to prioritize candidate biomarkers, which were subsequently verified using enzyme-linked immunosorbent assay (ELISA) in independent sample cohorts. In total, 1,428 serum proteins were identified, among which 1,410 were reliably quantified. We observed 31 upregulated and 189 downregulated proteins in NPC patients relative to controls. Spearman correlation analysis revealed significant associations: LTA4H (leukotriene A4 hydrolase) levels correlated with serum cell infiltration (r = 0.383, p = 0.032) and CD8 + T-cell abundance (r = 0.408, p = 0.021); both SULT1A3 (sulfotransferase family 1 A member 3) and FGL1 (fibrinogen-like protein 1) levels were positively associated with M1 macrophage infiltration (r = 0.510, p = 0.003 and r = 0.430, p = 0.015, respectively). In a preliminary validation cohort (n = 80), ELISA yielded AUC values of 0.631 (95% CI: 0.515-0.736, p = 0.04) for LTA4H, 0.787 (95% CI: 0.681-0.871, p < 0.001) for SULT1A3, and 0.688 (95% CI: 0.575-0.787, p = 0.002) for FGL1. In large-scale independent validation, SULT1A3 achieved an AUC of 0.826 (95% CI: 0.766-0.876; sensitivity = 78.89%, specificity = 75.47%) in cohort 1 (n = 196) and 0.796 (95% CI: 0.723-0.857; sensitivity = 76.67%, specificity = 76.67%) in cohort 2 (n = 150). Through an integrated workflow combining proteomic screening, machine learning prioritization, and multi-stage ELISA validation, we identified SULT1A3 as a candidate serum-based biomarker for early detection of NPC. Preliminary findings suggest that SULT1A3 may have potential utility in clinical screening, though further validation in independent, multi‑center cohorts is required.