Xu Yansheng, Ma Xin, Ai Xing, Gao Jiangping, Liang Yiming, Zhang Qin, Ma Tonghui, Mao Kaisheng, Zheng Qiaosong, Wang Sizhen, Jiao Yuchen, Zhang Xu, Li Hongzhao
Department of Urology, The First Medical Center of Chinese PLA General Hospital, Beijing, China.
Department of Urology, The Sixth Medical Center of Chinese PLA General Hospital, Beijing, China.
Front Oncol. 2021 Feb 9;10:597486. doi: 10.3389/fonc.2020.597486. eCollection 2020.
Conventional clinical detection methods such as CT, urine cytology, and ureteroscopy display low sensitivity and/or are invasive in the diagnosis of upper tract urinary carcinoma (UTUC), a factor precluding their use. Previous studies on urine biopsy have not shown satisfactory sensitivity and specificity in the application of both gene mutation or gene methylation panels. Therefore, these unfavorable factors call for an urgent need for a sensitive and non-invasive method for the diagnosis of UTUC.
In this study, a total of 161 hematuria patients were enrolled with (n = 69) or without (n = 92) UTUC. High-throughput sequencing of 17 genes and methylation analysis for CpG sites were combined as a liquid biopsy test panel. Further, a logistic regression prediction model that contained several significant features was used to evaluate the risk of UTUC in these patients.
In total, 86 UTUC- and 64 UTUC+ case samples were enrolled for the analysis. A logistic regression analysis of significant features including age, the mutation status of promoter, and methylation level resulted in an optimal model with a sensitivity of 94.0%, a specificity of 93.1%, the positive predictive value of 92.2% and a negative predictive value of 94.7%. Notably, the area under the curve (AUC) was 0.957 in the training dataset while internal validation produced an AUC of 0.962. It is worth noting that during follow-up, a patient diagnosed with ureteral inflammation at the time of diagnosis exhibiting both positive mutation and methylation test results was diagnosed with ureteral carcinoma 17 months after his enrollment.
This work utilized the epigenetic biomarker for the first time in the detection of UTUC and discovered its superior performance. To improve its sensitivity, we combined the biomarker with high-throughput sequencing of 17 genes test. It was found that the selected logistic regression model diagnosed with ureteral cancer can evaluate upper tract urinary carcinoma risk of patients with hematuria and outperform other existing panels in providing clinical recommendations for the diagnosis of UTUC. Moreover, its high negative predictive value is conducive to rule to exclude patients without UTUC.
传统的临床检测方法,如CT、尿液细胞学检查和输尿管镜检查,在上尿路尿路上皮癌(UTUC)的诊断中显示出低敏感性和/或具有侵入性,这限制了它们的应用。先前关于尿液活检的研究在基因突变或基因甲基化检测板的应用中未显示出令人满意的敏感性和特异性。因此,这些不利因素迫切需要一种敏感且非侵入性的UTUC诊断方法。
在本研究中,共纳入了161例血尿患者,其中有UTUC的患者69例,无UTUC的患者92例。将17个基因的高通量测序和CpG位点的甲基化分析结合作为一种液体活检检测板。此外,使用包含几个显著特征的逻辑回归预测模型来评估这些患者患UTUC的风险。
总共纳入了86例UTUC - 和64例UTUC + 病例样本进行分析。对包括年龄、启动子突变状态和甲基化水平等显著特征进行逻辑回归分析,得到一个最佳模型,其敏感性为94.0%,特异性为93.1%,阳性预测值为92.2%,阴性预测值为94.7%。值得注意的是,训练数据集中的曲线下面积(AUC)为0.957,内部验证产生的AUC为0.962。值得注意的是,在随访期间,一名诊断时被诊断为输尿管炎的患者,其突变和甲基化检测结果均为阳性,在入组17个月后被诊断为输尿管癌。
本研究首次利用表观遗传生物标志物检测UTUC,并发现了其卓越的性能。为提高其敏感性,我们将该生物标志物与17个基因的高通量测序检测相结合。结果发现,所选的诊断输尿管癌的逻辑回归模型可以评估血尿患者上尿路尿路上皮癌的风险,并且在为UTUC诊断提供临床建议方面优于其他现有检测板。此外,其高阴性预测值有利于排除无UTUC的患者。