Integrative prognostic modeling and mediation analysis of recurrence risk in extremely early-stage oral squamous cell...

Despite complete surgical resection, recurrence remains a substantial challenge in patients with early-stage oral squamous cell carcinoma (OSSC). Conventional TNM staging does not fully capture the biological heterogeneity underlying relapse. This study aimed to develop an individualized recurrence-prediction model integrating clinicopathologic, immune, and spatial factors, and to evaluate the potential mediating role of perineural invasion (PNI).

We retrospectively analyzed 451 patients with stage I-II OSCC treated at Xiangya Hospital between 2018 and 2025. Twenty-three clinical, pathological, and immunohistochemical variables were initially screened using LASSO regression. Independent predictors of recurrence were subsequently identified through multivariable Cox analysis. A nomogram was constructed based on the final Cox model and internally validated using Harrell's C-index, time-dependent AUCs, and 1,000-bootstrap calibration. Risk stratification was performed using Kaplan-Meier curves, and mediation analysis was used to determine whether PNI mediated the effects of tumor grade, P53 status, and age on recurrence.

Six independent predictors were incorporated into the final model: tumor grade, PNI, postoperative lymphocyte nadir, tumor-to-midline distance, pathological stage, and P53 status. The model demonstrated good discrimination (C-index = 0.79) and strong time-dependent predictive accuracy (AUCs: 0.819, 0.825, and 0.807 at 1, 3, and 5 years, respectively). Calibration curves showed excellent agreement between predicted and observed recurrence probabilities. Risk stratification based on the nomogram clearly separated patients into low- and high-risk groups (log-rank p < 0.001). Mediation analysis showed that PNI partially mediated the effects of tumor grade (proportion mediated = 29.6%) and P53 status (7.7%) on recurrence, whereas age exhibited no significant mediation through PNI.

This integrated prognostic model combines immune recovery, spatial invasion, and molecular features to accurately predict recurrence in early-stage OSCC. The partial mediation of tumor aggressiveness through PNI highlights a biological pathway linking tumor phenotype to recurrence risk. The proposed nomogram provides a clinically applicable tool for postoperative risk stratification and may assist in tailoring surveillance strategies and individualized adjuvant treatment decisions.