Abstract
BACKGROUND: This study aims to develop a novel nomogram predictive model utilizing serum bone metabolism biomarkers to accurately predict and diagnose tumor bone metastasis. The creation of this model holds significant clinical implications, supporting the development of targeted intervention strategies, providing robust laboratory data, and guiding early patient treatment. METHODS: A retrospective cohort study was conducted involving 266 patients treated at hospitals from September 2021 to January 2024. Patients were classified into three groups based on disease characteristics: tumor patients without bone metastasis, tumor patients with bone metastasis, and a control group consisting of individuals with neither tumor nor bone metabolism-related conditions. The primary serum bone metabolism biomarkers assessed included the N-terminal mid fragment of osteocalcin (NMID), the total N-terminal propeptide of type I procollagen (TPINP), and the C-terminal telopeptide of type I collagen β-special sequence (β-CTX). Multivariate statistical methods, including logistic regression and Cox regression, were employed for data analysis, while the nomogram model was rigorously evaluated using a variety of tools such as receiver operating characteristic (ROC) curves. RESULTS: The study found that the levels of NMID, TPINP, and β-CTX were significantly elevated in patients with bone metastasis compared to the other groups. These biomarkers were strongly associated with the incidence of tumor bone metastasis and identified as independent risk factors for this condition. The nomogram model demonstrated exceptional predictive performance, characterized by high area under the AUC values, robust time-dependent ROC curves, accurate calibration curves, and effective decision curve analysis. Notably, a positive correlation was observed between NMID, TPINP, β-CTX, and numeric rating scale (NRS) pain scores, providing valuable biomarkers for evaluating and managing pain associated with tumor bone metastasis. CONCLUSION: This study successfully established a nomogram predictive model based on serum bone metabolism biomarkers, with NMID, TPINP, and β-CTX emerging as critical indicators. The correlation between these biomarkers and NRS pain scores offers a novel understanding of the pain mechanisms associated with tumor bone metastasis, providing clinicians with essential reference points for diagnostic and therapeutic decision-making, thereby enhancing the practical application of the model in clinical settings.