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Qualitative Transcriptional Signature for the Pathological Diagnosis of Pancreatic Cancer
Frontiers in Molecular Biosciences ( IF 5 ) Pub Date : 2020-08-26 , DOI: 10.3389/fmolb.2020.569842
Yu-Jie Zhou , Xiao-Fan Lu , Jia-Lin Meng , Xin-Yuan Wang , Xin-Jia Ruan , Chang-Jie Yang , Qi-Wen Wang , Hui-Min Chen , Yun-Jie Gao , Fang-Rong Yan , Xiao-Bo Li

It is currently difficult for pathologists to diagnose pancreatic cancer (PC) using biopsy specimens because samples may have been from an incorrect site or contain an insufficient amount of tissue. Thus, there is a need to develop a platform-independent molecular classifier that accurately distinguishes benign pancreatic lesions from PC. Here, we developed a robust qualitative messenger RNA signature based on within-sample relative expression orderings (REOs) of genes to discriminate both PC tissues and cancer-adjacent normal tissues from non-PC pancreatitis and healthy pancreatic tissues. A signature comprising 12 gene pairs and 17 genes was built in the training datasets and validated in microarray and RNA-sequencing datasets from biopsy samples and surgically resected samples. Analysis of 1,007 PC tissues and 257 non-tumor samples from nine databases indicated that the geometric mean of sensitivity and specificity was 96.7%, and the area under receiver operating characteristic curve was 0.978 (95% confidence interval, 0.947–0.994). For 20 specimens obtained from endoscopic biopsy, the signature had a diagnostic accuracy of 100%. The REO-based signature described here can aid in the molecular diagnosis of PC and may facilitate objective differentiation between benign and malignant pancreatic lesions.



中文翻译:

胰腺癌病理诊断的定性转录特征。

对于病理学家而言,当前难以使用活检标本诊断胰腺癌(PC),因为样品可能来自不正确的部位或组织数量不足。因此,需要开发一种与平台无关的分子分类器,其可以准确地将良性胰腺病变与PC区别开。在这里,我们基于基因的样本内相对表达顺序(REO)开发了强大的定性信使RNA签名,以区分PC组织和癌旁正常组织与非PC胰腺炎和健康的胰腺组织。在训练数据集中建立了包含12个基因对和17个基因的签名,并在活检样本和手术切除样本的微阵列和RNA测序数据集中进行了验证。分析1 来自9个数据库的007个PC组织和257个非肿瘤样本表明,敏感性和特异性的几何平均值为96.7%,接受者工作特征曲线下的面积为0.978(95%置信区间为0.947-0.994)。对于从内窥镜活检获得的20个样本,签名的诊断准确性为100%。这里描述的基于REO的签名可以帮助PC的分子诊断,并且可以促进胰腺良性和恶性病变之间的客观区分。

更新日期:2020-09-23
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