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Abstract：
BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of RCC with high rates of metastasis. Targeted therapies such as tyrosine kinase and checkpoint inhibitors have improved treatment success, but therapy-related side effects and tumor recurrence remain a challenge. As a result, ccRCC still have a high mortality rate. Early detection before metastasis has great potential to improve outcomes, but no suitable biomarker specific for ccRCC is available so far. Therefore, molecular biomarkers derived from body fluids have been investigated over the past decade. Among them, RNAs from urine-derived extracellular vesicles (EVs) are very promising.
METHODS: RNA was extracted from urine-derived EVs from a cohort of 78 subjects (54 ccRCC patients, 24 urolithiasis controls). RNA-seq was performed on the discovery cohort, a subset of the whole cohort (47 ccRCC, 16 urolithiasis). Reads were then mapped to the genome, and expression was quantified based on 100 nt long contiguous genomic regions. Cluster analysis and differential region expression analysis were performed with adjustment for age and gender. The candidate biomarkers were validated by qPCR in the entire cohort. Receiver operating characteristic, area under the curve and odds ratios were used to evaluate the diagnostic potential of the models.
RESULTS: An initial cluster analysis of RNA-seq expression data showed separation by the subjects\' gender, but not by tumor status. Therefore, the following analyses were done, adjusting for gender and age. The regions differentially expressed between ccRCC and urolithiasis patients mainly overlapped with small nucleolar RNAs (snoRNAs). The differential expression of four snoRNAs (SNORD99, SNORD22, SNORD26, SNORA50C) was validated by quantitative PCR. Confounder-adjusted regression models were then used to classify the validation cohort into ccRCC and tumor-free subjects. Corresponding accuracies ranged from 0.654 to 0.744. Models combining multiple genes and the risk factors obesity and hypertension showed improved diagnostic performance with an accuracy of up to 0.811 for SNORD99 and SNORA50C (p = 0.0091).
CONCLUSIONS: Our study uncovered four previously unrecognized snoRNA biomarkers from urine-derived EVs, advancing the search for a robust, easy-to-use ccRCC screening method.
METHODS: RNA was extracted from urine-derived EVs from a cohort of 78 subjects (54 ccRCC patients, 24 urolithiasis controls). RNA-seq was performed on the discovery cohort, a subset of the whole cohort (47 ccRCC, 16 urolithiasis). Reads were then mapped to the genome, and expression was quantified based on 100 nt long contiguous genomic regions. Cluster analysis and differential region expression analysis were performed with adjustment for age and gender. The candidate biomarkers were validated by qPCR in the entire cohort. Receiver operating characteristic, area under the curve and odds ratios were used to evaluate the diagnostic potential of the models.
RESULTS: An initial cluster analysis of RNA-seq expression data showed separation by the subjects\' gender, but not by tumor status. Therefore, the following analyses were done, adjusting for gender and age. The regions differentially expressed between ccRCC and urolithiasis patients mainly overlapped with small nucleolar RNAs (snoRNAs). The differential expression of four snoRNAs (SNORD99, SNORD22, SNORD26, SNORA50C) was validated by quantitative PCR. Confounder-adjusted regression models were then used to classify the validation cohort into ccRCC and tumor-free subjects. Corresponding accuracies ranged from 0.654 to 0.744. Models combining multiple genes and the risk factors obesity and hypertension showed improved diagnostic performance with an accuracy of up to 0.811 for SNORD99 and SNORA50C (p = 0.0091).
CONCLUSIONS: Our study uncovered four previously unrecognized snoRNA biomarkers from urine-derived EVs, advancing the search for a robust, easy-to-use ccRCC screening method.
摘要：
背景：透明细胞肾细胞癌（ccRCC）是最常见的肾细胞癌亚型，转移率高。酪氨酸激酶和检查点抑制剂等靶向治疗提高了治疗成功率。但治疗相关的副作用和肿瘤复发仍然是一个挑战。因此,ccRCC的死亡率仍然很高。转移前早期检测具有改善预后的巨大潜力，但目前尚无ccRCC特异性合适的生物标志物。因此,在过去的十年中，已经研究了来自体液的分子生物标志物。其中,来自尿液来源的细胞外囊泡（EV）的RNA非常有前途。
方法：从78名受试者（54名ccRCC患者，24个尿石症对照)。RNA-seq在发现队列中进行，整个队列的一个子集(47ccRCC，16尿石症）。然后将读数映射到基因组，并且基于100nt长的连续基因组区域定量表达。进行聚类分析和差异区域表达分析，并根据年龄和性别进行调整。通过qPCR在整个群组中验证候选生物标志物。接收机工作特性,曲线下面积和比值比用于评估模型的诊断潜力.
结果：RNA-seq表达数据的初始聚类分析显示受试者的性别分离，但不是肿瘤状态。因此,进行了以下分析，调整性别和年龄。ccRCC和尿石症患者之间差异表达的区域主要与小核仁RNA（snoRNA）重叠。通过定量PCR验证了四种snoRNAs(SNORD99、SNORD22、SNORD26、SNORA50C)的差异表达。然后使用校正回归模型将验证队列分类为ccRCC和无肿瘤受试者。相应的准确度范围从0.654到0.744。结合多个基因和肥胖和高血压危险因素的模型显示，SNORD99和SNORA50C的诊断性能提高，准确率高达0.811（p=0.0091）。
结论：我们的研究发现了来自尿液来源的电动汽车的四种以前未被识别的snoRNA生物标志物，推进寻找一个强大的，易于使用的ccRCC筛选方法。
方法：从78名受试者（54名ccRCC患者，24个尿石症对照)。RNA-seq在发现队列中进行，整个队列的一个子集(47ccRCC，16尿石症）。然后将读数映射到基因组，并且基于100nt长的连续基因组区域定量表达。进行聚类分析和差异区域表达分析，并根据年龄和性别进行调整。通过qPCR在整个群组中验证候选生物标志物。接收机工作特性,曲线下面积和比值比用于评估模型的诊断潜力.
结果：RNA-seq表达数据的初始聚类分析显示受试者的性别分离，但不是肿瘤状态。因此,进行了以下分析，调整性别和年龄。ccRCC和尿石症患者之间差异表达的区域主要与小核仁RNA（snoRNA）重叠。通过定量PCR验证了四种snoRNAs(SNORD99、SNORD22、SNORD26、SNORA50C)的差异表达。然后使用校正回归模型将验证队列分类为ccRCC和无肿瘤受试者。相应的准确度范围从0.654到0.744。结合多个基因和肥胖和高血压危险因素的模型显示，SNORD99和SNORA50C的诊断性能提高，准确率高达0.811（p=0.0091）。
结论：我们的研究发现了来自尿液来源的电动汽车的四种以前未被识别的snoRNA生物标志物，推进寻找一个强大的，易于使用的ccRCC筛选方法。
