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1.
BMC Urol ; 24(1): 163, 2024 Aug 01.
Artículo en Inglés | MEDLINE | ID: mdl-39090720

RESUMEN

BACKGROUND: This study investigated the use of urinary exosomal mRNA as a potential biomarker for the early detection of prostate cancer (PCa). METHODS: Next-generation sequencing was utilized to analyze exosomal RNA from 10 individuals with confirmed PCa and 10 individuals without cancer. Subsequent validation through qRT-PCR in a larger sample of 43 PCa patients and 92 healthy controls revealed distinct mRNA signatures associated with PCa. RESULTS: Notably, mRNAs for RAB5B, WWP1, HIST2H2BF, ZFY, MARK2, PASK, RBM10, and NRSN2 showed promise as diagnostic markers, with AUC values between 0.799 and 0.906 and significance p values. Combining RAB5B and WWP1 in an exoRNA diagnostic model outperformed traditional PSA tests, achieving an AUC of 0.923, 81.4% sensitivity, and 89.1% specificity. CONCLUSIONS: These findings highlight the potential of urinary exosomal mRNA profiling, particularly focusing on RAB5B and WWP1, as a valuable strategy for improving the early detection of PCa.


Asunto(s)
Biomarcadores de Tumor , Detección Precoz del Cáncer , Exosomas , Neoplasias de la Próstata , ARN Mensajero , Humanos , Masculino , Neoplasias de la Próstata/orina , Neoplasias de la Próstata/genética , Neoplasias de la Próstata/diagnóstico , Exosomas/genética , ARN Mensajero/orina , Biomarcadores de Tumor/orina , Biomarcadores de Tumor/genética , Detección Precoz del Cáncer/métodos , Anciano , Persona de Mediana Edad
2.
Clin Transl Oncol ; 26(9): 2369-2379, 2024 Sep.
Artículo en Inglés | MEDLINE | ID: mdl-38602643

RESUMEN

PURPOSE: Machine learning (ML) models presented an excellent performance in the prognosis prediction. However, the black box characteristic of ML models limited the clinical applications. Here, we aimed to establish explainable and visualizable ML models to predict biochemical recurrence (BCR) of prostate cancer (PCa). MATERIALS AND METHODS: A total of 647 PCa patients were retrospectively evaluated. Clinical parameters were identified using LASSO regression. Then, cohort was split into training and validation datasets with a ratio of 0.75:0.25 and BCR-related features were included in Cox regression and five ML algorithm to construct BCR prediction models. The clinical utility of each model was evaluated by concordance index (C-index) values and decision curve analyses (DCA). Besides, Shapley Additive Explanation (SHAP) values were used to explain the features in the models. RESULTS: We identified 11 BCR-related features using LASSO regression, then establishing five ML-based models, including random survival forest (RSF), survival support vector machine (SSVM), survival Tree (sTree), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and a Cox regression model, C-index were 0.846 (95%CI 0.796-0.894), 0.774 (95%CI 0.712-0.834), 0.757 (95%CI 0.694-0.818), 0.820 (95%CI 0.765-0.869), 0.793 (95%CI 0.735-0.852), and 0.807 (95%CI 0.753-0.858), respectively. The DCA showed that RSF model had significant advantages over all models. In interpretability of ML models, the SHAP value demonstrated the tangible contribution of each feature in RSF model. CONCLUSIONS: Our score system provide reference for the identification for BCR, and the crafting of a framework for making therapeutic decisions for PCa on a personalized basis.


Asunto(s)
Aprendizaje Automático , Recurrencia Local de Neoplasia , Neoplasias de la Próstata , Humanos , Masculino , Neoplasias de la Próstata/sangre , Neoplasias de la Próstata/patología , Recurrencia Local de Neoplasia/sangre , Recurrencia Local de Neoplasia/patología , Estudios Retrospectivos , Anciano , Persona de Mediana Edad , Pronóstico , Árboles de Decisión , Modelos de Riesgos Proporcionales , Algoritmos , Máquina de Vectores de Soporte , Antígeno Prostático Específico/sangre
3.
BMC Med ; 21(1): 270, 2023 07 24.
Artículo en Inglés | MEDLINE | ID: mdl-37488510

RESUMEN

BACKGROUND: The introduction of multiparameter MRI and novel biomarkers has greatly improved the prediction of clinically significant prostate cancer (csPCa). However, decision-making regarding prostate biopsy and prebiopsy examinations is still difficult. We aimed to establish a quick and economic tool to improve the detection of csPCa based on routinely performed clinical examinations through an automated machine learning platform (AutoML). METHODS: This study included a multicenter retrospective cohort and two prospective cohorts with 4747 cases from 9 hospitals across China. The multimodal data, including demographics, clinical characteristics, laboratory tests, and ultrasound reports, of consecutive participants were retrieved using extract-transform-load tools. AutoML was applied to explore potential data processing patterns and the most suitable algorithm to build the Prostate Cancer Artificial Intelligence Diagnostic System (PCAIDS). The diagnostic performance was determined by the receiver operating characteristic curve (ROC) for discriminating csPCa from insignificant prostate cancer (PCa) and benign disease. The clinical utility was evaluated by decision curve analysis (DCA) and waterfall plots. RESULTS: The random forest algorithm was applied in the feature selection, and the AutoML algorithm was applied for model establishment. The area under the curve (AUC) value in identifying csPCa was 0.853 in the training cohort, 0.820 in the validation cohort, 0.807 in the Changhai prospective cohort, and 0.850 in the Zhongda prospective cohort. DCA showed that the PCAIDS was superior to PSA or fPSA/tPSA for diagnosing csPCa with a higher net benefit for all threshold probabilities in all cohorts. Setting a fixed sensitivity of 95%, a total of 32.2%, 17.6%, and 26.3% of unnecessary biopsies could be avoided with less than 5% of csPCa missed in the validation cohort, Changhai and Zhongda prospective cohorts, respectively. CONCLUSIONS: The PCAIDS was an effective tool to inform decision-making regarding the need for prostate biopsy and prebiopsy examinations such as mpMRI. Further prospective and international studies are warranted to validate the findings of this study. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2100048428. Registered on 06 July 2021.


Asunto(s)
Inteligencia Artificial , Neoplasias de la Próstata , Masculino , Humanos , Estudios Retrospectivos , Algoritmos , Aprendizaje Automático
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