首页> 外文期刊>Journal of proteome research >Applying random forests to identify biomarker panels in serum 2D-DIGE data for the detection and staging of prostate cancer
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Applying random forests to identify biomarker panels in serum 2D-DIGE data for the detection and staging of prostate cancer

机译:应用随机森林识别血清2D-DIGE数据中的生物标志物组以检测和分期前列腺癌

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In recent years, Prostate Specific Antigen (PSA) testing is widespread and has been associated with deceased mortality rates; however, this testing has raised concerns of overdiagnosis and overtreatment. It is clear that additional biomarkers are required. To identify these biomarkers, we have undertaken proteomics and metabolomics expression profiles of serum samples from BPH, Gleason score 5 and 7 using two-dimensional difference in gel electrophoresis (2D-DIGE) and nuclear magnetic resonance spectroscopy (NMR). Panels of serum protein biomarkers were identified by applying Random Forests to the 2D-DIGE data. The evaluation of selected biomarker panels has shown that they can provide higher prediction accuracy than the current diagnostic standard. With careful validation of these serum biomarker panels, these panels may potentially help to reduce unnecessary invasive diagnostic procedures and more accurately direct the urologist to curative surgery.
机译:近年来,前列腺特异性抗原(PSA)检测已广泛使用,并且与死亡率降低有关。但是,这种测试引起了对过度诊断和过度治疗的担忧。显然,需要其他生物标志物。为了鉴定这些生物标志物,我们使用凝胶电泳(2D-DIGE)和核磁共振波谱(NMR)的二维差异,对BPH,格里森评分5和7的血清样品进行了蛋白质组学和代谢组学表达分析。通过将随机森林应用于2D-DIGE数据来鉴定血清蛋白生物标志物。对所选生物标志物组的评估表明,它们可以提供比当前诊断标准更高的预测准确性。通过对这些血清生物标志物面板的仔细验证,这些面板可能有助于减少不必要的侵入性诊断程序,并更准确地指导泌尿科医师进行治愈性手术。

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