Identifying High Gleason Score Prostate Cancer by Prostate Fluid Metabolic Fingerprint‐Based Multi‐Modal Recognition

Abstract

AbstractProstate cancer (PCa) is the second most common cancer in males worldwide. The Gleason scoring system, which classifies the pathological growth pattern of cancer, is considered one of the most important prognostic factors for PCa. Compared to indolent PCa, PCa with high Gleason score (h‐GS PCa, GS ≥ 8) has greater clinical significance due to its high aggressiveness and poor prognosis. It is crucial to establish a rapid, non‐invasive diagnostic modality to decipher patients with h‐GS PCa as early as possible. In this study, ferric nanoparticle‐assisted laser desorption/ionization mass spectrometry (FeNPALDI‐MS) to extract prostate fluid metabolic fingerprint (PSF‐MF) is employed and combined with the clinical features of patients, such as prostate‐specific antigen (PSA), to establish a multi‐modal diagnosis assisted by machine learning. This approach yields an impressive area under the curve (AUC) of 0.87 to diagnose patients with h‐GS, surpassing the results of single‐modal diagnosis using only PSF‐MF or PSA, respectively. Additionally, using various screening methods, six key metabolites that exhibit greater diagnostic efficacy (AUC = 0.96) are identified. These findings also provide insights into related metabolic pathways, which may provide valuable information for further elucidation of the pathological mechanisms underlying h‐GS PCa.