基于循环miRNA的泡型包虫病新型诊断标志物研究

Alveolar Echinococcosis, also called "worm cancer" is caused by Echinococcus multilocularis. This disease has a long incubation period and there is no obvious symptoms at the beginning. Moreover, it severely affected the health and life of the people in the Qinghai Tibet Plateau with up to 94% of mortality rate after ten year infection. At present, the major clinical diagnosis is still imaging, more lag, lack of effective way of early diagnosis. miRNA is a new type of broad-spectrum diagnostic marker. Serum circulating miRNA is widely used in noninvasive diagnosis for convenient sampling, stable and easy detection, and high incidence of disease. Our results showed infection of E. multilocularis can lead to changes of miRNA expression profile in patients’ liver, which in turn caused the change of serum miRNA. Based on the above reasons, we focused on miRNA as the object. Using high-throughput sequencing analysis, we identified some serum miRNAs specifically associated to the infection of E. multilocularis and plan to confirm them by qPCR in patients and normal populations. In addition, the correlation of the changes of miRNAs in liver tissue and serum was analyzed and the trend of serum miRNAs profile along with the degree of infection was finally obtained. Our work provides experimental basis for circulating miRNAs as molecular marker of Alveolar echinococcosis, as well as theoretical guidance for early noninvasive diagnosis, surgical treatment and prognosis monitoring and medication.

泡型包虫病由泡球蚴感染造成，有“寄生虫癌”之称。该病潜伏期长、初期无明显症状、后期易转移，未经治疗患者十年病死率高达94%，严重影响青藏高原牧区人民的健康和生活。目前临床多采用影像学诊断，存在滞后性，缺少早期诊断方法。血清循环miRNA具有取材方便、稳定易检测、与疾病高度关联等特点，是最具前景的诊断标志物。前期结果显示泡球蚴感染会导致患者肝组织miRNA表达谱变化，进而引起血清miRNA的改变。综上，本课题拟通过高通量测序分析患者血清miRNAs，找到特征miRNA作为有效标志物，并在人群中验证。一方面筛选来自泡球蚴的miRNA作为早期诊断感染的生物标志；另一方面通过筛选表达量随病情规律变化的循环miRNA，建立感染预测模型；最终在群体中验证这些miRNA作为生物标志物的检测效果。本课题研究结果将为泡球蚴感染的早期检测提供实验依据，也为临床泡型包虫病的用药及术后评估等提供理论模型与指导。

泡型包虫病又称“寄生虫癌”，10 年死亡率在 90%以上，是青藏高原最为严重的人畜共患病。为开发血清循环 miRNA 作为泡型包虫病感染的新型感染标志物，本项目通过对泡型包虫病（AE）患者血清miRNA进行Illumina高通量测序，通过数据处理与比对挖掘出20条与AE特异相关的miRNAs（特异相关指该miRNA在患者血清中的表达量与健康对照组相比显著升高或降低），计算此20条miRNAs作为标记的OR值，并构建感染预测模型，最终通过机器学习算法评估该模型的预测效力达到或接近90%（AUC=0.9）。通过与多房棘球绦虫基因组及miRNA数据库比对后，共筛选出3个可能来自多房棘球绦虫（Em）的miRNA分子，并通过序列分析得到这三个虫源miRNAs在Em基因组中的全序列。基于miRNA为高灵敏、易检测的生物分子，发现并获得感染源特异miRNA有助于对AE患者进行感染的早期发现以及治疗过程中对于感病程度的监测。