Park Jee Soo, Lee Hyo Jung, Almujalhem Ahmad, Althubiany Hatem Hamed, A Alqahatani Ali, Jang Won Sik, Kim Jongchan, Lee Seung Hwan, Rha Koon Ho, Ham Won Sik
Department of Urology and Urological Science Institute, Yonsei University College of Medicine, Seoul 03722, Korea.
Department of Urology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia.
Cancers (Basel). 2020 May 7;12(5):1182. doi: 10.3390/cancers12051182.
A high nuclear grade is crucial to predicting tumor recurrence and metastasis in clear cell renal cell carcinomas (ccRCCs). We aimed to compare the mRNA profiles of tumor tissues and preoperative plasma in patients with localized T1 stage ccRCCs, and to evaluate the potential of the plasma mRNA profile for predicting high-grade ccRCCs. Data from a prospective cohort ( = 140) were collected between November 2018 and November 2019. Frozen tumor tissues and plasma were used to measure SET domain-containing 2 (), , and mRNA levels and correlation with the Fuhrman grade was investigated. Multivariate logistic regression analysis revealed significant association between high-grade ccRCC and and mRNA levels in tissues (odds ratio (b) = 0.021, 95% confidence interval (CI): 0.001-0.466, = 0.014; b = 6.116, 95% CI: 1.729-21.631, 0.005, respectively) and plasma (b = 0.028, 95% CI 0.007-0.119, < 0.001; b = 1.496, 95% CI: 1.187-1.885, = 0.001, respectively). High-grade ccRCC prediction models revealed areas under the curve of 0.997 and 0.971 and diagnostic accuracies of 97.86% and 92.86% for the frozen tissue and plasma, respectively. and mRNA can serve as non-invasive plasma biomarkers for predicting high-grade ccRCCs. Studies with long follow-ups are needed to validate the prognostic value of these biomarkers in ccRCCs.
高核分级对于预测透明细胞肾细胞癌(ccRCC)的肿瘤复发和转移至关重要。我们旨在比较局限性T1期ccRCC患者肿瘤组织和术前血浆的mRNA谱,并评估血浆mRNA谱预测高级别ccRCC的潜力。收集了2018年11月至2019年11月期间前瞻性队列(n = 140)的数据。使用冷冻肿瘤组织和血浆测量含SET结构域的2(SETD2)、肾母细胞瘤1(WT1)、叉头框蛋白M1(FOXM1)和锌指蛋白804a(ZNF804a)的mRNA水平,并研究其与Fuhrman分级的相关性。多因素逻辑回归分析显示,高级别ccRCC与组织中SETD2和ZNF804a的mRNA水平之间存在显著关联(优势比(OR)= 0.021,95%置信区间(CI):0.001 - 0.466,P = 0.014;OR = 6.116,95% CI:1.729 - 21.631,P < 0.005)以及与血浆中SETD (b = 0.028, 95% CI 0.007 - 0.119, P < 0.001; b = 1.496, 95% CI: 1.187 - 1.885, P = 0.001, respectively)。高级别ccRCC预测模型显示,冷冻组织和血浆的曲线下面积分别为0.997和0.971,诊断准确率分别为97.86%和92.86%。SETD2和ZNF804a的mRNA可作为预测高级别ccRCC的非侵入性血浆生物标志物。需要进行长期随访研究以验证这些生物标志物在ccRCC中的预后价值。