PDF(Pubmed)
Abstract：
Diagnosing urothelial cancer (UCa) via invasive cystoscopy is painful, specifically in men, and can cause infection and bleeding. Because the UCa risk is higher for male patients, urinary non-invasive UCa biomarkers are highly desired to stratify men for invasive cystoscopy. We previously identified multiple DNA methylation sites in urine samples that detect UCa with a high sensitivity and specificity in men. Here, we identified the most relevant markers by employing multiple statistical approaches and machine learning (random forest, boosted trees, LASSO) using a dataset of 251 male UCa patients and 111 controls. Three CpG sites located in ALOX5, TRPS1 and an intergenic region on chromosome 16 have been concordantly selected by all approaches, and their combination in a single decision matrix for clinical use was tested based on their respective thresholds of the individual CpGs. The combination of ALOX5 and TRPS1 yielded the best overall sensitivity (61%) at a pre-set specificity of 95%. This combination exceeded both the diagnostic performance of the most sensitive bioinformatic approach and that of the best single CpG. In summary, we showed that overlap analysis of multiple statistical approaches identifies the most reliable biomarkers for UCa in a male collective. The results may assist in stratifying men for cystoscopy.
摘要：
通过侵入性膀胱镜检查诊断尿路上皮癌（UCa）是痛苦的，特别是在男人身上,会导致感染和出血.因为男性患者的UCa风险更高，泌尿非侵入性UCa生物标志物非常需要对男性进行侵入性膀胱镜检查。我们先前在尿液样品中鉴定了多个DNA甲基化位点，这些位点在男性中以高灵敏度和特异性检测UCa。这里,我们通过采用多种统计方法和机器学习(随机森林，提升的树木，LASSO)使用251名男性UCa患者和111名对照的数据集。所有方法都一致选择了位于ALOX5，TRPS1和16号染色体上的基因间区域中的三个CpG位点，并根据其各自的各个CpG阈值测试了其在临床使用的单个决策矩阵中的组合。ALOX5和TRPS1的组合在95%的预设特异性下产生最佳的总体灵敏度(61%)。这种组合超过了最敏感的生物信息学方法的诊断性能和最佳的单一CpG的诊断性能。总之,我们发现,多种统计学方法的重叠分析在男性群体中确定了UCa最可靠的生物标志物.结果可能有助于对男性进行膀胱镜检查。
