A clinicopathological and hematological biomarker-based nomogram for overall survival in early-onset colorectal cancer: development, external validation, and translational implications.

Background: Early-onset colorectal cancer (EOCRC) is increasing globally, yet prognostic tools specifically tailored to younger patients remain limited. Molecular markers such as mismatch repair (MMR) status, although biologically important, are not routinely available in many clinical settings, creating a translational gap between laboratory advances and bedside practice. This study aimed to develop and externally validate a practical nomogram for predicting overall survival (OS) in EOCRC using widely accessible clinicopathological variables and routine hematological biomarkers, thereby bridging routine laboratory data to individualized risk stratification. Methods: This retrospective study included 410 EOCRC patients who underwent radical surgery (training cohort n=246, internal validation n=164). An independent external cohort (n=91) from a second tertiary center was used for validation. Candidate predictors included T/N stage, presenting symptoms, tumor markers [carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9)], and the systemic immune-inflammation index (SII)-a composite biomarker reflecting the balance between tumor-promoting inflammation and antitumor immunity. A nomogram was constructed using the least absolute shrinkage and selection operator (LASSO) and multivariable Cox regression. Model performance was assessed by discrimination [time-dependent area under the curve (AUC)], calibration, decision curve analysis (DCA), risk stratification, and head-to-head comparison with an anatomical-stage model and a clinicopathological model without inflammatory biomarkers. Results: Six predictors were retained in the final nomogram: T stage, N stage, changes in bowel habits, CEA, CA19-9, and SII. For 3-year OS, the AUC was 0.897 (training), 0.837 (internal validation), and 0.861 (external validation); for 5-year OS, the corresponding AUCs were 0.904, 0.873, and 0.868. The nomogram significantly outperformed the anatomical-stage model in all cohorts (all P<0.001) and provided incremental value over the clinicopathological model in both validation cohorts. Calibration curves showed good agreement between predicted and observed survival probabilities. DCA confirmed clinical net benefit across a range of threshold probabilities. The nomogram successfully stratified patients into high- and low-risk groups (log-rank P<0.001 in all three cohorts). Conclusions: This externally validated nomogram translates routinely available clinicopathological and hematological data into individualized OS predictions for EOCRC. By integrating inflammatory biomarkers such as SII, the model provides a translatable tool that may support risk-stratified surveillance and management, particularly in clinical settings where routine hematological and clinicopathological assessments are available but comprehensive molecular profiling is not.