The systemic immune-inflammation index (SII) and prognostic nutritional index (PNI) have emerged as potential prognostic markers in various malignancies, but their combined and interactive value in advanced prostate cancer (PCa) remains underexplored. This retrospective cohort study included 115 patients with advanced PCa (American Joint Committee on Cancer(AJCC) stage III–IV) diagnosed between January 2016 and December 2020. SII and PNI were calculated from pretreatment blood samples. Restricted cubic spline (RCS) analysis was used to explore nonlinear relationships. Cox regression identified independent prognostic factors. A nomogram incorporating AJCC stage, SII, and PNI was developed to predict 1-, 3-, and 5-year overall survival (OS). The model was internally validated via bootstrapping, with calibration and decision curve analysis (DCA) assessing accuracy and clinical utility. With a median follow-up of 54 months, SII and PNI predicted OS with AUCs of 0.712 and 0.803, respectively. RCS analysis revealed nonlinear, J-shaped (SII) and reverse S-shaped (PNI) relationships with OS and progression-free survival (PFS) risks ( P < 0.001). Multivariate Cox analysis confirmed AJCC stage IV (HR = 2.18, 95% CI: 1.02–4.65), SII ≥ 671.70 (HR = 2.43, 95% CI: 1.42–4.15), and PNI < 46.35 (HR = 3.85, 95% CI: 2.25–6.59) as independent risk factors. A significant synergistic interaction was observed (interaction HR = 1.85, 95% CI: 1.11–3.08, P = 0.018). The nomogram achieved a C-index of 0.861 and an AUC of 0.874 for 3-year OS prediction, outperforming individual markers ( P < 0.05). Calibration plots showed excellent agreement, and DCA demonstrated positive net clinical benefit across a wide threshold probability range. Pretreatment SII and PNI are synergistic and independent prognostic markers for advanced PCa, exhibiting nonlinear relationships with survival outcomes. The developed nomogram provides accurate, individualized survival probability estimation with favorable clinical utility, serving as a practical tool for risk stratification and personalized treatment planning.
Li et al. (Mon,) studied this question.