Pilot Investigation on Markers of Bone Metabolism, Angiogenesis, and Neuroendocrine Activity as Potential Predictors of Survival of Metastatic Prostate Cancer Patients with Bone Metastases

Abstract

Prostate cancer with bone metastasis exhibits significant heterogeneity, complicating prognosis, and treatment. This study explores the potential of plasma, serum, and urine biomarkers to stratify patients and evaluate their prognostic value. Using two-step clustering, we analyzed baseline levels of Platelet-derived growth factor-BB (PDGF-BB), Insulin-like growth factor-binding protein 1 (IGFBP-1), Bone Morphogenetic Protein 2 (BMP-2), Vascular endothelial growth factor (VEGF) (plasma and urine), prostate-specific antigen (PSA), neuron-specific enolase (NSE), chromogranin A (CgA) and bone-specific alkaline phosphatase (BAP) (serum) and creatinine (Cr), and type I collagen-cross-linked N telopeptide (NTx) (urine) in 29 patients with prostate cancer and bone metastasis. Longitudinal biomarker dynamics were assessed at baseline, 6 months, and 12 months. Clinical outcomes were evaluated using Kaplan–Meier and multivariate analyses. Three distinct groups (C1, C2, and C3) were identified. C1 exhibited elevated pPDGF-BB and pVEGF levels, C3 had increased pBAP and uNTx/Cr, and C2 showed lower biomarker levels. Prior treatments influenced biomarker levels, with bisphosphonates reducing bone turnover markers and radiotherapy correlating with long-term changes in growth factors. Longitudinal analysis revealed unique biomarker dynamics within each group, with a tendency for pPDGF-BB and pVEGF levels to decrease over time in C1, and distinct trends in uNTx/Cr between groups. Despite individual biomarkers failing to predict survival, C3 patients demonstrated significantly worse survival than C1 and C2. Molecular clustering of peripheral blood and urinary biomarkers identifies distinct subgroups with metastatic castration-resistant prostate cancer patients outperforming traditional models in outcome prediction and supporting its potential for personalized treatment and prognosis.