Rocio I R Macias, Hiroaki Kanzaki, Carmen Berasain, Matias A Avila, Jose J G Marin, Yujin Hoshida
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引用次数: 0
Abstract
Cholangiocarcinomas (CCAs) are a heterogeneous group of malignant tumors that originate from the biliary tract. They are usually diagnosed in advanced stages, leading to a dismal prognosis for affected patients. As CCA often arises as a sporadic cancer in individuals lacking specific risk factors or with heterogeneous backgrounds, and there are no defined high-risk groups, the implementation of effective surveillance programs for CCA is problematic. The identification and validation of new biomarkers useful for risk stratification, diagnosis, prognosis, and prediction of treatment response remains an unmet need for patients with CCA, even though numerous studies have been conducted lately to try to discover and validate CCA biomarkers. In this review, we overview the available information about the different types of biomarkers that have been investigated in recent years using minimally invasive biospecimens (blood, serum/plasma, bile, and urine) and their potential usefulness in diagnosis, prognosis, and risk stratification. It is widely accepted that early detection of CCA will impact patients' outcomes, by improving survival rates, quality of life, and the possibility of less invasive and/or curative treatments; however, challenges to its translation and clinical application for patients with CCA need to be resolved.
胆管癌(CCA)是一类起源于胆道的异质性恶性肿瘤。它们通常在晚期才被确诊,因此患者的预后很差。由于 CCA 通常以散发性癌症的形式出现在缺乏特定风险因素或具有异质性背景的个体中,而且没有明确的高危人群,因此实施有效的 CCA 监测计划很成问题。尽管近来已有大量研究试图发现和验证 CCA 生物标志物,但鉴定和验证有助于风险分层、诊断、预后和治疗反应预测的新生物标志物仍是 CCA 患者尚未满足的需求。在这篇综述中,我们将概述近年来利用微创生物样本(血液、血清/血浆、胆汁、尿液)研究的不同类型生物标志物的现有信息,以及它们在诊断、预后和风险分层方面的潜在作用。人们普遍认为,CCA 的早期检测将提高患者的生存率和生活质量,并有可能采用微创和/或根治性治疗,从而影响患者的预后。
期刊介绍:
The American Journal of Pathology, official journal of the American Society for Investigative Pathology, published by Elsevier, Inc., seeks high-quality original research reports, reviews, and commentaries related to the molecular and cellular basis of disease. The editors will consider basic, translational, and clinical investigations that directly address mechanisms of pathogenesis or provide a foundation for future mechanistic inquiries. Examples of such foundational investigations include data mining, identification of biomarkers, molecular pathology, and discovery research. Foundational studies that incorporate deep learning and artificial intelligence are also welcome. High priority is given to studies of human disease and relevant experimental models using molecular, cellular, and organismal approaches.