Prognostic impact of nutritional indicators based on Lasso-Cox regression for non-muscle-invasive bladder cancer

Background There is a lack of prognostic models to predict the outcomes of non-muscle-invasive bladder cancer (NMIBC) patients receiving Bacillus Calmette-Guérin (BCG) immunotherapy. Existing nutritional risk indicators, such as the prognostic nutritional index (PNI), geriatric nutritional risk index (GNRI) and Naples prognostic score (NPS), have demonstrated prognostic value in various malignancies. This study aimed to construct novel nutritional risk indexes (NRIs) using peripheral blood markers via Lasso-Cox regression and validate their prognostic value. Methods The electric medical records in our institution were searched and data of 525 NMIBC patients were collected. The Lasso-Cox regression was employed to screen preoperative blood biomarkers correlated with recurrence-free survival (RFS), time to BCG-treatment failure (TTF), and progression-free survival (PFS). NRIs were developed based on selected markers and validated against GNRI, PNI, NPS, and the EAU2021 risk model using Kaplan–Meier analysis, Cox regression, receiver-operating characteristic (ROC) curves, Concordance index (C-index) and Decision Curve analysis. Results Lasso-Cox regression identified distinct blood biomarkers: gamma-glutamyl transpeptidase (GGT), serum total protein (TP), albumin and cholesterol were predictive of tumor recurrence and BCG failure, while GGT, TP, and coefficient variation of red blood cell volume distribution width were linked to tumor progression. Three NRIs—NRITR (RFS), NRIBF (TTF) and NRITP (PFS)—were constructed. The NRIs exhibited prognostic value through Kaplan–Meier analysis. Multivariate Cox analysis confirmed NRITR (HR = 0.38, 95%CI:0.28–0.53), NRIBF (HR = 0.45, 95%CI: 0.30–0.67), and NRITP (HR = 0.38, 95%CI: 0.21–0.69) as independent predictors. Nomograms incorporating NRIs demonstrated superior discriminative performance in predicting RFS (AUC = 0.739, C-index = 0.673), TTF (AUC = 0.795, C-index = 0.767), and PFS (AUC = 0.796, C-index = 0.788), and could bring more net benefit for NMIBC patients. Conclusion The Lasso-Cox regression may offer superior value in selecting prognostic biomarkers for NMIBC. The Lasso-Cox regression based NRIs enhance prognostic stratification for BCG-treated NMIBC, outperforming existing blood-based nutritional risk indicators and the EAU2021 model. Incorporation of blood-based nutritional indicators into clinical practice could optimization of personalized NMIBC treatment strategies and clinical decision-making. Further validation is warranted.