Džubinská Daniela, Zvarík Milan, Kollárik Boris, Šikurová Libuša
Department of Nuclear Physics and Biophysics, Faculty of Mathematics, Physics and Informatics, Comenius University in Bratislava, Mlynská Dolina, 842 48 Bratislava, Slovakia.
Department of Urology, University Hospital of Bratislava, Antolská 11, 851 07 Bratislava, Slovakia.
Diagnostics (Basel). 2021 Sep 28;11(10):1793. doi: 10.3390/diagnostics11101793.
Bladder cancer (BC) is the most common type of carcinoma of the urological system. Recently, there has been an increasing interest in non-invasive diagnostic tumor markers due to the invasive attribute of cystoscopy, which is still considered the gold standard diagnostic method. However, markers published in the literature so far do not meet expectations for replacing cystoscopy due to their low specificity and excessively high false-positive results, which can be mainly caused by frequently occurring hematuria also in benign cases. No reliable non-invasive method has yet been identified that can distinguish patients with bladder cancer and non-malignant hematuria patients. Our work examined the possibilities of non-targeted biomarkers of urine to distinguish patients with malignant and non-malignant diseases of the bladder using 3D HPLC in combination with computer processing of multiple datasets. Urine samples from 47 patients, 23 patients with bladder cancer (BC) and 24 patients with non-malignant hematuria (NMHU), were enrolled in clinical trials. For the separation and subsequent analysis of a large number of urine components, 3D HPLC (high-performance liquid chromatography) with an absorption and fluorescence detector was used. The obtained dataset was further subjected to various uni- and multi-dimensional statistical analyses and mathematical modeling. We found 334 chromatographic peaks, of which 18 peaks were identified as significantly different for BC and NMHU patients. Using receiver operating characteristic (ROC) analysis, we assessed the informative ability of significant chromatographic peaks (90% sensitivity and 74% specificity). By logistic regression, we identified the optimal and simplified set of seven chromatographic peaks (5 absorptions plus 2 fluorescence) with strong classification power (100% sensitivity and 100% specificity) for distinguishing patients with bladder cancer and those with non-malignant hematuria. Partial least square discriminant analysis (PLS-DA) model and orthogonal projection to latent structure discriminant analysis (OPLS-DA) with 100% sensitivity and 96% specificity were used to distinguish BC and NMHU patients. Multivariate statistical analysis of urinary metabolomic profiles of patients revealed that BC patients can be discriminated from NMHU patients and the results can likely contribute to an early and non-invasive diagnosis of BC.
膀胱癌（BC）是泌尿系统最常见的癌症类型。近年来，由于膀胱镜检查具有侵入性，尽管其仍是金标准诊断方法，但人们对非侵入性诊断肿瘤标志物的兴趣与日俱增。然而，迄今为止文献中报道的标志物因其低特异性和过高的假阳性结果，未能达到替代膀胱镜检查的预期，这些假阳性结果主要是由良性病例中也经常出现的血尿引起的。目前尚未找到可靠的非侵入性方法来区分膀胱癌患者和非恶性血尿患者。我们的研究使用三维高效液相色谱法（3D HPLC）结合多个数据集的计算机处理，探讨了尿液非靶向生物标志物区分膀胱恶性和非恶性疾病患者的可能性。47例患者的尿液样本被纳入临床试验，其中23例为膀胱癌（BC）患者，24例为非恶性血尿（NMHU）患者。为了分离并随后分析大量尿液成分，使用了配备吸收和荧光检测器的3D高效液相色谱法（HPLC）。所得数据集进一步进行了各种单维和多维统计分析以及数学建模。我们发现了334个色谱峰，其中18个峰在BC患者和NMHU患者中存在显著差异。使用受试者工作特征（ROC）分析，我们评估了显著色谱峰的信息能力（灵敏度90%，特异性74%）。通过逻辑回归，我们确定了一组由七个色谱峰（5个吸收峰加2个荧光峰）组成的最佳简化组合，该组合具有很强的分类能力（灵敏度100%，特异性100%），可用于区分膀胱癌患者和非恶性血尿患者。使用灵敏度为100%、特异性为96%的偏最小二乘判别分析（PLS - DA）模型和正交投影到潜在结构判别分析（OPLS - DA）来区分BC患者和NMHU患者。对患者尿液代谢组学谱的多变量统计分析表明，BC患者可与NMHU患者区分开来，研究结果可能有助于膀胱癌的早期非侵入性诊断。
