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Serum N-glycan fingerprint helps to discriminate intrahepatic cholangiocarcinoma from hepatocellular carcinoma
Electrophoresis ( IF 2.9 ) Pub Date : 2021-02-11 , DOI: 10.1002/elps.202000392
Chenjun Huang 1 , Xuewen Xu 1 , Mengmeng Wang 2 , Xiao Xiao 1 , Cheng Cheng 1 , Jun Ji 1 , Meng Fang 1 , Chunfang Gao 1
Affiliation  

Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) are two main types of primary liver cancer, and reliable discrimination is important for optimal treatment. Aberrant glycosylation was detected in HCC and ICC. Both cross-sectional and follow-up studies were performed to establish a differential diagnosis model using N-glycans. A total of 420 participants were enrolled, with 310 patients in training cohort and 110 patients in validation cohort. The follow-up cohort was used to assess the prognosis of ICC. As the results, the diagnostic efficacy of the model was superior to alpha-fetoprotein (AFP), carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA 19-9) when identifying ICC from HCC (AUC of the nomogram: 0.845, 95%CI: 0.788–0.902; AFP: 0.793, 95%CI: 0.732–0.854; CEA: 0.592, 95%CI: 0.496–0.687; CA 19-9: 0.674, 95%CI: 0.582–0.767) in training cohort. In validation cohort, this model (AUC: 0.810, 95% CI: 0.728–0.891) also demonstrated high efficacy in distinguishing ICC from HCC. Furthermore, the nomogram helps to stratify ICC into two subgroups with high or low risk of survival and recurrence. Therefore, a nomogram integrating six N-glycans [NGA2FB(Peak2), NG1A2F (Peak3), NA2 (Peak5), NA2F (Peak6), NA3 (Peak8) and NA4 (Peak11)] was established for ICC and HCC differentiation, and for prognosis assessment in ICC patients.

中文翻译:

血清N-聚糖指纹有助于区分肝内胆管癌和肝细胞癌

肝细胞癌 (HCC) 和肝内胆管癌 (ICC) 是原发性肝癌的两种主要类型,可靠的鉴别对于最佳治疗很重要。在 HCC 和 ICC 中检测到异常糖基化。进行横断面和后续研究以建立使用N-聚糖的鉴别诊断模型。共招募了 420 名参与者,其中 310 名患者在训练队列中,110 名患者在验证队列中。随访队列用于评估ICC的预后。结果,该模型的诊断效果优于甲胎蛋白(AFP)、癌胚抗原(CEA)和碳水化合物抗原19-9(CA 19-9),在从HCC中鉴别ICC时(列线图的AUC:0.845, 95%CI:0.788–0.902;AFP:0.793,95%CI:0.732–0.854;CEA:0.592,95%CI:0.496–0.687;CA 19-9:0.674,95%CI:0.582–0.767)在训练队列中。在验证队列中,该模型 (AUC: 0.810, 95% CI: 0.728–0.891) 在区分 ICC 和 HCC 方面也表现出高效。此外,列线图有助于将 ICC 分为生存和复发风险高或低的两个亚组。因此,建立了整合六种 N-聚糖 [NGA2FB(Peak2)、NG1A2F (Peak3)、NA2 (Peak5)、NA2F (Peak6)、NA3 (Peak8) 和 NA4 (Peak11)] 的列线图,用于 ICC 和 HCC 分化,以及ICC患者的预后评估。
更新日期:2021-02-11
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