Journal of translational medicine · 2025
Integrating anamnestic and lifestyle data with sphingolipid levels for risk-based prostate cancer screening.
СтатьяНаблюдательное исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Peraldo-Neia Caterina, Ostano Paola, Savioli Melissa, Mello-Grand Maurizia, Gregnanin Ilaria, Guana Francesca, Crivelli Francesca, Montagnani Francesco, Dei-Cas Michele, Paroni Rita, Sinopoli Antonella, Ferranti Francesco, Testino Nicolò, Oderda Marco, Zitella Andrea, Fiameni Chiara, Gagliardi Amedeo, Naccarati Alessio, Clivio Luca, Gontero Paolo, Zaramella Stefano, Chiorino Giovanna
Аннотация
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 (average ROC-AUC: 0.92). Cer-20 and CVD were consistently selected by models predicting ISUP > 1 PCa. In the SLI subgroup, PSA, age, CVD, SM-16, HexCer-20, HexCer-24.1, DHS1P, and DHCer-24 were selected in all 10 models (average ROC-AUC: 0.83).
CONCLUSIONS: Circulating sphingolipids are promising biomarkers that, when combined with PSA, anamnestic, and lifestyle data, may enhance PCa screening precision and reduce the need for invasive, costly examinations.
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 (average ROC-AUC: 0.92). Cer-20 and CVD were consistently selected by models predicting ISUP > 1 PCa. In the SLI subgroup, PSA, age, CVD, SM-16, HexCer-20, HexCer-24.1, DHS1P, and DHCer-24 were selected in all 10 models (average ROC-AUC: 0.83).
CONCLUSIONS: Circulating sphingolipids are promising biomarkers that, when combined with PSA, anamnestic, and lifestyle data, may enhance PCa screening precision and reduce the need for invasive, costly examinations.