BACKGROUND: Multiple Myeloma (MM) is a plasma cell malignancy associated with systemic and renal complications. This study evaluates the prognostic and diagnostic significance of poliovirus receptor (PVR) gene expression and protein levels, serum amylase, and urinary biomarkers (IGFBP-7, TIMP-2) in MM patients. METHODS: In a prospective case-control study, 50 MM patients and 50 healthy controls were assessed. PVR gene expression (qPCR), serum PVR and amylase (ELISA/chemistry analyzer), and urinary IGFBP-7 and TIMP-2 (ELISA) were analyzed. Statistical analyses included correlation tests, Kaplan-Meier survival analysis, Cox regression, stratified quartile analysis, and receiver operating characteristic (ROC) curve evaluation. Multiple testing corrections (Bonferroni and FDR) were applied. RESULTS: MM patients showed significantly elevated PVR expression and protein levels, serum amylase, and urinary biomarkers compared to controls (p<0.001). High PVR expression was associated with advanced disease stage, TP53 mutations, and reduced overall survival (OS: 44.84 vs. 48.0 months; p=0.044). High serum amylase and urinary IGFBP-7 were linked to significantly poorer OS and progression-free survival (PFS). Multivariate Cox regression confirmed PVR expression (HR=12.2), serum amylase (HR=11.5), and IGFBP-7 (HR=11.9) as independent predictors of poor OS, findings that remained robust in bootstrapped and penalized regression models. Stratified analysis revealed that patients in the highest biomarker quartiles had significantly worse outcomes and higher TP53 mutation rates. ROC analysis showed excellent diagnostic performance for the combined panel (PVR + amylase + IGFBP-7; AUC=0.97, sensitivity =90%, specificity = 88%), outperforming individual markers. Significant associations remained after multiple testing correction. CONCLUSION: PVR gene expression, serum amylase, and urinary IGFBP-7 are independent and robust prognostic biomarkers in MM. Their combined use enhances diagnostic accuracy and risk stratification, supporting their integration into clinical decision-making. Validation in larger, multi-center studies is recommended. Limitations include the single-center design, modest sample size, absence of disease comparator groups, and the cross-sectional nature of biomarker evaluation. These findings warrant validation in larger, multi-institutional, and longitudinal studies.