Integrating anamnestic and lifestyle data with sphingolipid levels for risk-based prostate cancer screening in Journal of Translational Medicine

2025
ASL Biella

Tipo pubblicazione

Article

Autori/Collaboratori (22)Vedi tutti...

Peraldo-Neia C
Genomics Lab, Fondazione Edo ed Elvo Tempia, Biella, Italy
Ostano P
Genomics Lab, Fondazione Edo ed Elvo Tempia, Biella, Italy
Savioli M
Genomics Lab, Fondazione Edo ed Elvo Tempia, Biella, Italy

et alii...

Abstract

Background: In the era of risk-based prostate cancer (PCa) screening, overcoming the limitations of prostate-specific antigen (PSA) testing and stratifying men by individual risk is crucial. Our study aims to integrate anamnestic and lifestyle data with circulating biomarkers to minimize unnecessary second-level investigations (SLIs) for patients with suspected PCa, while improving the detection of clinically significant PCa (ISUP > 1). Methods: We collected plasma samples, recent clinical history, family cancer history, PSA levels, and lifestyle information from 904 men: 421 undergoing PSA testing, 421 with suspected and 62 with confirmed PCa. Univariable logistic regression was applied to identify ananmestic and lifestyle variables mostly associated with PCa. Penalized logistic regression models predictive of PCa or ISUP > 1 PCa were built both using the 814 subjects with complete information for such variables, applying a 10-fold cross validation approach, and dividing the dataset into a training (n = 445: 132 PCa, 313 non-PCa) and a test (n = 369: 147 PCa, 222 non-PCa) set. The concentration of 50 sphingolipids was analysed on the latter set of 369 subjects by mass-spectrometry, and multivariable penalized regression with 10-fold cross-validation was applied to integrate anamnestic, lifestyle, sphingolipid data. ROC-AUCs on the test sets were compared with PSA ROC-AUCs. Results: Age, cardiovascular disease (CVD), number of medications, and sedentariness were significantly associated with PCa detection and their combination with PSA improved its performance (ROC-AUC from 0.85 to 0.89). In the SLI subgroup (n = 437), adding age improved PSA predictive power (ROC-AUC from 0.60 to 0.70), but performance was still poor. Penalized regression with 10-fold cross-validation on the sphingolipid dataset identified hypertension, CVD, PSA, age, and five sphingolipids (HexCer-20, Cer-20, HexCer-24.1, GM3-24.1, DHCer-24) as key variables for accurate PCa classification

Il documento è reperibile nella banca dati EMBASE.
Se sei accreditato in BVS-P effettua l'accesso per utilizzare i nostri servizi.

PMID : 40660231

DOI : 10.1186/s12967-025-06820-9

Keywords

3200 QTRAP; data analysis software; Dionex 3000 UPLC; R version 4.2.1; prostate specific antigen; sphingolipid; adult; aged; alcohol consumption; anamnesis; area under the curve; article; blood sampling; body mass; cancer patient; cancer risk; cancer screening; cohort analysis; controlled study; cross validation; diagnostic accuracy; diagnostic test accuracy study; human; human tissue; hypertension; least absolute shrinkage and selection operator; lifestyle; liquid chromatography-mass spectrometry; major clinical study; male; mass spectrometry; physical activity; prospective study; prostate cancer; prostatectomy; questionnaire; receiver operating characteristic; risk factor; sphingolipid metabolism; 3200 QTRAP; Dionex 3000 UPLC; R version 4.2.1;