Urine is a preferred object for noninvasive diagnostic strategies. Urinary metabolic analysis is speculatively regarded as an ideal tool for screening diseases closely related to the genitourinary system in view of the intimate relationship between metabolomics and phenotype. Herein, we propose a urinary metabolic fingerprint-based noninvasive diagnostic strategy by designing hollow core-shell metal oxide heterojunctions (denoted as MOHs). With outstanding light absorption and electron-hole separation ability, MOHs aid in the extraction of high-performance urine metabolic fingerprints. Coupled with optimized machine learning algorithms, we establish a metabolic marker panel for accurate diagnosis of prostate cancer (PCa), which is the most common malignant tumor of the male genitourinary system, achieving accuracies of 84.72 and 83.33% in the discovery and validation sets, respectively. Furthermore, metabolite variations and related pathway analyses confirm the credibility and change correlation of key metabolic features in PCa. This work tends to advance the noninvasive diagnostic strategy toward clinical realities.