Badmos Sabur, Noriega Landa Elizabeth, Holbrook Kiana L, Quaye George E, Su Xiaogang, Lee Wen-Yee
Department of Chemistry and Biochemistry, University of Texas at El Paso, El Paso, TX 79968, USA.
Division of Health Services and Outcomes Research, Children's Mercy Kansas City, Kansas City, MO 64108, USA.
Cancers (Basel). 2025 Aug 24;17(17):2756. doi: 10.3390/cancers17172756.
BACKGROUND/OBJECTIVES: Prostate cancer (PCa) is the most common cancer among males. Approximately 20-40% of patients with clinically localized PCa will present with a biochemical recurrence after a radical prostatectomy (RP), while some will present with recurrent metastasis. Monitoring the disease post-treatment is crucial for detecting a potential cancer recurrence early. Urinary volatile organic compounds (VOCs) have shown potential to detect PCa. However, their application in disease monitoring remains unexplored.
A total of 165 urine samples were collected from male adults with biopsy-designated PCa-positive results before ( = 55) and after a RP ( = 55), and with biopsy-designated PCa-negative diagnosis ( = 55). The post-RP cohort was subdivided into three groups based on their health status after surgery as recovered healthy, biochemical recurrence, and recurrent metastasis. VOCs in the urine samples were extracted by stir bar sorptive extraction and analyzed using gas chromatography and mass spectrometry. We explored the use of metabolomics and a machine learning algorithm tool to investigate the potential of using VOCs for differentiating PCa diagnoses before and after the RP procedure with different outcomes.
Over 100 potential VOCs were identified to differentiate PCa patients before and after a RP, and those with biochemical recurrence and recurrent metastasis.
Urinary VOCs are promising biomarkers that could be used to differentiate PCa patients pre- and post-RP. The findings from this research provide preliminary insights and could aid future investigations in developing tools for PCa patients after treatment. The absence of a validation cohort limits the reproducibility and translational impact of these findings; therefore, the results should be considered exploratory and require confirmation in larger, independent cohorts.
背景/目的：前列腺癌（PCa）是男性中最常见的癌症。临床上局限性PCa患者中约20 - 40%在根治性前列腺切除术（RP）后会出现生化复发，而一些患者会出现复发转移。治疗后监测疾病对于早期发现潜在的癌症复发至关重要。尿液挥发性有机化合物（VOCs）已显示出检测PCa的潜力。然而，它们在疾病监测中的应用仍未得到探索。
共收集了165份成年男性尿液样本，其中活检确诊为PCa阳性的患者术前（n = 55）、术后（n = 55）各55份，以及活检确诊为PCa阴性的患者55份。RP术后队列根据术后健康状况分为三组：恢复健康、生化复发和复发转移。尿液样本中的VOCs通过搅拌棒吸附萃取法提取，并使用气相色谱和质谱进行分析。我们探索了使用代谢组学和机器学习算法工具来研究利用VOCs区分RP手术前后不同结局的PCa诊断的潜力。
已鉴定出100多种潜在的VOCs，可区分RP手术前后的PCa患者，以及生化复发和复发转移患者。
尿液VOCs是有前景的生物标志物，可用于区分RP手术前后的PCa患者。本研究结果提供了初步见解，并有助于未来开发针对PCa患者治疗后工具的研究。缺乏验证队列限制了这些发现的可重复性和转化影响；因此，这些结果应被视为探索性的，需要在更大的独立队列中得到证实。
