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用于细针穿刺取材中多种类型胰胆管肿瘤恶性诊断的联合微小RNA和信使核糖核酸微流控TaqMan阵列卡

Combined microRNA and mRNA microfluidic TaqMan array cards for the diagnosis of malignancy of multiple types of pancreatico-biliary tumors in fine-needle aspiration material.

作者信息

Gress Thomas M, Lausser Ludwig, Schirra Lyn-Rouven, Ortmüller Lisa, Diels Ramona, Kong Bo, Michalski Christoph W, Hackert Thilo, Strobel Oliver, Giese Nathalia A, Schenk Miriam, Lawlor Rita T, Scarpa Aldo, Kestler Hans A, Buchholz Malte

机构信息

Clinic for Gastroenterology, Endocrinology and Metabolism, University Hospital, Philipps-Universität Marburg, Marburg, Germany.

Institute of Medical Systems Biology, University of Ulm, Ulm, Germany.

出版信息

Oncotarget. 2017 Nov 21;8(64):108223-108237. doi: 10.18632/oncotarget.22601. eCollection 2017 Dec 8.

DOI:10.18632/oncotarget.22601
PMID:29296236
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5746138/
Abstract

Pancreatic ductal adenocarcinoma (PDAC) continues to carry the lowest survival rates among all solid tumors. A marked resistance against available therapies, late clinical presentation and insufficient means for early diagnosis contribute to the dismal prognosis. Novel biomarkers are thus required to aid treatment decisions and improve patient outcomes. We describe here a multi-omics molecular platform that allows for the first time to simultaneously analyze miRNA and mRNA expression patterns from minimal amounts of biopsy material on a single microfluidic TaqMan Array card. Expression profiles were generated from 113 prospectively collected fine needle aspiration biopsies (FNAB) from patients undergoing surgery for suspect masses in the pancreas. Molecular classifiers were constructed using support vector machines, and rigorously evaluated for diagnostic performance using 10×10fold cross validation. The final combined miRNA/mRNA classifier demonstrated a sensitivity of 91.7%, a specificity of 94.5%, and an overall diagnostic accuracy of 93.0% for the differentiation between PDAC and benign pancreatic masses, clearly outperfoming miRNA-only classifiers. The classification algorithm also performed very well in the diagnosis of other types of solid tumors (acinar cell carcinomas, ampullary cancer and distal bile duct carcinomas), but was less suited for the diagnostic analysis of cystic lesions. We thus demonstrate that simultaneous analysis of miRNA and mRNA biomarkers from FNAB samples using multi-omics TaqMan Array cards is suitable to differentiate suspect solid pancreatic masses with high precision.

摘要

胰腺导管腺癌(PDAC)在所有实体瘤中生存率持续垫底。对现有治疗方法的显著耐药性、临床症状出现较晚以及早期诊断手段不足导致了其预后不佳。因此,需要新的生物标志物来辅助治疗决策并改善患者预后。我们在此描述了一个多组学分子平台,它首次能够在一张微流控TaqMan Array卡上,从微量活检材料中同时分析miRNA和mRNA表达模式。表达谱来自113例前瞻性收集的细针穿刺活检(FNAB)样本,这些样本取自因胰腺可疑肿块而接受手术的患者。使用支持向量机构建分子分类器,并通过10×10倍交叉验证对其诊断性能进行严格评估。最终的miRNA/mRNA联合分类器在区分PDAC和胰腺良性肿块方面,敏感性为91.7%,特异性为94.5%,总体诊断准确率为93.0%,明显优于仅使用miRNA的分类器。该分类算法在诊断其他类型实体瘤(腺泡细胞癌、壶腹癌和远端胆管癌)时也表现出色,但不太适合囊性病变的诊断分析。因此,我们证明,使用多组学TaqMan Array卡同时分析FNAB样本中的miRNA和mRNA生物标志物,适用于高精度区分可疑胰腺实体肿块。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e9/5746138/65a1b2523383/oncotarget-08-108223-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e9/5746138/b6ced92fc02c/oncotarget-08-108223-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e9/5746138/fcf5f726f483/oncotarget-08-108223-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e9/5746138/59bb2c0e11be/oncotarget-08-108223-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e9/5746138/a3bbf1c2e1b3/oncotarget-08-108223-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e9/5746138/bd47ad208326/oncotarget-08-108223-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e9/5746138/94b0f0361256/oncotarget-08-108223-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e9/5746138/65a1b2523383/oncotarget-08-108223-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e9/5746138/b6ced92fc02c/oncotarget-08-108223-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e9/5746138/fcf5f726f483/oncotarget-08-108223-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e9/5746138/59bb2c0e11be/oncotarget-08-108223-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e9/5746138/a3bbf1c2e1b3/oncotarget-08-108223-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e9/5746138/bd47ad208326/oncotarget-08-108223-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e9/5746138/94b0f0361256/oncotarget-08-108223-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e9/5746138/65a1b2523383/oncotarget-08-108223-g007.jpg

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