探索微型核糖核酸與慢性腎臟病及血液透析病人泌尿道上皮癌生物標記的相關性;MicroRNA Profile is a Biomarker Associated with Urothelial Carcinoma in Chronic Kidney Disease and Hemodialysis Patients

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Title:	探索微型核糖核酸與慢性腎臟病及血液透析病人泌尿道上皮癌生物標記的相關性;MicroRNA Profile is a Biomarker Associated with Urothelial Carcinoma in Chronic Kidney Disease and Hemodialysis Patients
Authors:	陳建隆;Chen, Chien-Lung
Contributors:	系統生物與生物資訊研究所
Keywords:	泌尿道上皮癌;微型核糖核酸;慢性腎臟病;血液透析;Urothelial carcinoma;MicroRNA;Chronic kidney disease;Hemodialysis
Date:	2019-06-19
Issue Date:	2019-09-03 14:53:33 (UTC+8)
Publisher:	國立中央大學
Abstract:	泌尿道上皮癌 (Urothelial Carcinoma, UC)為國人最常見的泌尿道腫瘤，它的發生率和復發率在慢性腎臟病和長期透析病人中更是高於一般人。目前泌尿道上皮癌的診斷以臨床症狀，影像學，細胞學及病理學為主，常因發現時間點較晚而耽誤病情，更無適當的腫瘤標記來協助診斷和治療，造成罹病率和死亡率都較高。微型核糖核酸(MicroRNAs, miRNAs) 已被證明是轉錄後基因表現的主要調節者，它可存在於組織、血液和尿液，與各種腎臟相關疾病及腫瘤有相關。自 2008 年開始便有以血液中微型核糖核酸做為生化標記的相關研究，我們實驗的目的為探索微型核糖核酸與泌尿道腫瘤生物標記相關性，並可應用於腫瘤的診斷、治療和追蹤。首先是以尿液及血液的檢體鑑別慢性腎臟病患者，罹患泌尿道上皮癌相關的 miRNAs 圖譜。利用微型 RNA 晶片 (miRNA array)篩選出 UC 相關之 miRNAs，經即時定量系統 (Quantitative real-time PCR array, qPCR)的分析，透過驗證 (validation) 與測試 (testing)，在 250 組尿液檢體中，篩選出 4個 miRNAs 表現量的組合，ROC 可達 0.8211；再以 134 位病人合併尿液及血液檢體，篩選出 9 個 miRNAs 表現量的組合，ROC 為 0.8607。其中 miR-1274a 表現量更與 UC 的預後有顯著相關。其次是以血液檢體鑑別血液透析患者，罹患泌尿道上皮癌相關的 miRNAs 圖譜。，由 8 個血漿檢體篩選出 8 個候選的 miRNAs，再以 52 個檢體驗證，發現 5 個 miRNAs 表現量的組合，對於預測泌尿道上皮癌的 AUC 值為 0.882，敏感度為 80% (95% CI,0.5191 to 0.9567%)，專一度為 83.7% (95% CI, 0.6799 to 0.9381%)，這項診斷工具對 9 個檢體進行測試實驗時，正確率更達 100%。另外也發現 miR-19b-1-5p 的表現量與 UC 的預後有相關性 (p=0.0382)。因此，我們發現 miRNAs 圖譜，具備了高於現有尿道上皮腫瘤標記的敏感度和專一度；更發現血漿 miR-19b-1-5p 及尿液 miR-1274a 與 UC 的預後具有顯著的相關性。未來本實驗結果將進一步，運用於預測、診斷、治療和追蹤尿道上皮腫瘤。;Urothelial carcinoma (UC) is the most common urinary tract tumor among patients with chronic kidney disease (CKD) in Taiwan; this tumor is particularly prevalent among individuals on hemodialysis (HD). Urinalysis, image study, cytology, and cystoscopy are conventional screening methods and diagnostic tools for UC. However, urine cytology is characterized by poor sensitivity and specificity during the early stages of UC. Therefore, a non-invasive, inexpensive, and highly sensitive and specific UC biomarker is in urgent demand for improving UC screening, diagnosis, treatment, and follow-up. MiRNA is a small, non-coding RNA that is stable in the circulation and body fluids. It regulates epigenetically gene modification that is associated with kidney diseases and cancers. Therefore, we studied miRNAs as a novel ancillary diagnosis biomarker for detecting UC in CKD and HD patients. Firstly, we used urine and plasma miRNAs from CKD patients to develop ancillary biomarkers capable of detecting UC. A UC miRNA classifier composed of four urine miRNAs from 250 patients had an Receiver Operating Characteristics (ROC) of 0.8211; an additional nine miRNAs from the combination of urine and plasma samples from 134 CKD patients were detected with a ROC of 0.8607. In particular, the expression of urine miR-1247a was significantly different between CKD and UC patients and associated with the prognosis of UC. Secondly, we identified eight candidate miRNAs using a miRNA array and quantitative, real-time PCR (qPCR) from HD patients’ plasma. After validating and testing, the expression levels of miR-150-5p, miR-155-5p/miR-150-5p, miR-378a-3p/miR-150-5p, miR-636/miR-150-5p, miR-150-5p/ miR-210-3p, and miR-19b-1-5p/miR-378a-3p were significantly different between the UC and non-UC samples (p=0.035, 0.0048, 0.016, 0.024, 0.038, and 0.048, respectively). Meanwhile, we found that low miR-19b-1-5p expression was associated with a poorer UC prognosis (p=0.0382) based on Kaplan-Meier curve analysis. We used a miRNA classifier based on five miRNA expression levels to predict UC and found that the area under the curve (AUC) was 0.882, the sensitivity was 80% (95% CI, 0.5191–0.9567%), and the specificity was 83.7% (95% CI, 0.6799–0.9381%). We have determined novel urine and plasma miRNA classifiers that have higher sensitivity and specificity than existing UC biomarkers. Additionally, we found that plasma miR-19b-1-5p and urine miR-1247a had an association with UC prognosis. In the future, we will focus on miRNA classifiers as novel biomarkers for early detection, diagnosis, and treatment of UC.
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