首页|Sun Yat-sen University Reports Findings in miRNA-Based Therapy (Screening Model for Bladder Cancer Early Detection With Serum miRNAs Based on Machine Learning: A Mixed-Cohort Study Based on 16,189 Participants)
Sun Yat-sen University Reports Findings in miRNA-Based Therapy (Screening Model for Bladder Cancer Early Detection With Serum miRNAs Based on Machine Learning: A Mixed-Cohort Study Based on 16,189 Participants)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-New research on Biotechnology - miRNA- Based Therapy is the subject of a report.According to news reporting originatin g from Guangdong, People's Republic of China, by NewsRx correspondents,research stated, "Early detection of bladder cancer (BCa) can have a positive impact onpatients' prognosis. However, there is currently no widely accepted method for e arly screening of BCa."Financial supporters for this research include Natural Science Foundation of Gua ngdong Province,National Natural Science Foundation of China.Our news editors obtained a quote from the research from Sun Yat-sen University, "We aimed todevelop an efficient, clinically applicable, and noninvasive metho d for the early screening of BCa bydetecting specific serum miRNA levels. A mix ed-cohort (including BCa, 12 different other cancers, benigndisease patients, a nd health population) study was conducted using a sample size of 16,189. Five ma chinelearning algorithms were utilized to develop screening models for BCa usin g the training dataset. Theperformance of the model was evaluated using receive r operating characteristic curve and decision curveanalysis on the testing data set, and subsequently, the model with the best predictive power was selected.Fu rthermore, the selected model's screening performance was evaluated using both t he validation set andexternal set. The BCaS3miR model, utilizing only three ser um miRNAs (miR-6087, miR-1343-3p, andmiR-5100) and based on the KNN algorithm, is the superior screening model chosen for BCa. BCaS3miRconsistently performed well in both the testing, validation, and external sets, exceeding 90% sensitivityand specificity levels. The area under the curve was 0.990 (95% CI: 0.984-0.991), 0.964 (95% CI: 0.936-0.984), and 0.917 (95% CI: 0.836-0.953) in the testing, validation, and external set. The subgroup analysis revealed that the BCaS3miR model demonstrated outstanding screening accurac y in various clinicalsubgroups of BCa. In addition, we developed a BCa screenin g scoring model (BCaSS) based on thelevels of miR-1343-3p/miR-6087 and miR-5100 /miR-6087. The screening effect of BCaSS is investigatedand the findings indica te that it has predictability and distinct advantages. Using a mixed cohort withthe largest known sample size to date, we have developed effective screening mo dels for BCa, namelyBCaS3miR and BCaSS."
GuangdongPeople's Republic of ChinaA siaBiotechnologyBladder CancerCancerClinical ResearchClinical Trials a nd StudiesCyborgsDiagnostics and ScreeningDrugs and TherapiesEmerging Te chnologiesGeneticsHealth and MedicineMachine LearningOncologymiRNA-Bas ed Therapy
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