Role of NNMT and NM23A for diagnosis of RCC: Renal Cell Carcinoma

Abstract

Objective:

This study investigates the diagnostic potential of nicotinamide N-methyltransferase (NNMT) and NM23A as biomarkers for renal cell carcinoma (RCC), focusing on their ability to facilitate early detection and improve diagnostic precision.

Methods:

A prospective observational study was conducted over 24 months, enrolling patients with RCC, benign renal tumors, and healthy controls. Biomarker levels were measured using enzyme-linked immunosorbent assays (ELISA). Diagnostic performance was evaluated through statistical analyses, including ROC curve analysis and Mann-Whitney U tests. Additionally, machine learning models such as Random Forest and Gradient Boosting were employed to identify key predictors of RCC.

Results:

NNMT and NM23A demonstrated significant diagnostic accuracy, with area under the curve (AUC) values of 0.933 and 0.915, respectively. Both biomarkers showed substantial differences across RCC, benign, and control groups (p < 0.001). Machine learning analyses highlighted NNMT as the most influential predictor for RCC diagnosis, further supporting its clinical relevance.

Conclusions:

NNMT and NM23A emerge as promising non-invasive biomarkers for RCC, offering substantial diagnostic accuracy and reducing reliance on invasive procedures. Their integration into clinical workflows, supported by advanced machine learning methodologies, could transform RCC diagnostics. Further research with diverse populations is recommended to validate these findings and expand their clinical applicability.