Corradi Chiara, Gentiluomo Manuel, Adsay Volkan, Sainz Juan, Camisa Paolo Riccardo, Wlodarczyk Barbara, Crippa Stefano, Tavano Francesca, Capurso Gabriele, Campa Daniele
Department of Biology, University of Pisa, Pisa, Italy.
Department of Pathology, Koç University School of Medicine and Koç University Research Center for Translational Medicine, Istanbul, Turkey.
Semin Cancer Biol. 2025 Feb;109:25-43. doi: 10.1016/j.semcancer.2024.12.005. Epub 2024 Dec 27.
Pancreatic ductal adenocarcinoma (PDAC) is the most lethal and common form of pancreatic cancer, it has no specific symptoms, and most of the patients are diagnosed when the disease is already at an advanced stage. Chemotherapy typically has only a modest effect, making surgery the most effective treatment option. However, only a small percentage of patients are amenable to surgery. One viable strategy to reduce PDAC death burden associated with the disease is to focus on precursor lesions and identify markers able to predict who will evolve into PDAC. While most PDACs are believed to be preceded by pancreatic intraepithelial neoplasms (PanINs), 5-10 % arise from Intraductal papillary mucinous neoplasms (IPMNs), which are mass-forming cystic lesions that are very common in the general population. IPMNs offer an invaluable model of pancreatic carcinogenesis for researchers to analyse, as well as a target population for PDAC early detection by clinicians. The evolution of IPMN into cancer is a complex and multistep process, therefore the identification of individual markers will not be the solution. In recent years, multiple omics technologies have been instrumental to identify possible biomarkers of IPMN progression and carcinogenesis. The only foreseeable strategy will be to integrate multi-omics data, alongside clinical and morphological features, into a progression score or signature using either standard epidemiologic tools or artificial intelligence. The aim of this manuscript is to review the current knowledge on genetic biomarkers and to briefly mention also additional omics, such as metabolomics, the exposome, the miRNome and epigenomics of IPMNs.
胰腺导管腺癌（PDAC）是胰腺癌最致命且最常见的形式，它没有特定症状，大多数患者在疾病已处于晚期时才被诊断出来。化疗通常效果有限，因此手术是最有效的治疗选择。然而，只有一小部分患者适合手术。减轻与该疾病相关的PDAC死亡负担的一个可行策略是关注癌前病变，并识别能够预测哪些人会发展为PDAC的标志物。虽然大多数PDAC被认为是由胰腺上皮内瘤变（PanINs）发展而来，但5%-10%起源于导管内乳头状黏液性肿瘤（IPMNs），这是一种形成肿块的囊性病变，在普通人群中非常常见。IPMN为研究人员提供了一个分析胰腺癌发生的宝贵模型，同时也是临床医生早期检测PDAC的目标人群。IPMN演变为癌症是一个复杂的多步骤过程，因此识别单个标志物并不能解决问题。近年来，多种组学技术有助于识别IPMN进展和癌变的可能生物标志物。唯一可预见的策略是使用标准流行病学工具或人工智能，将多组学数据与临床和形态学特征整合到一个进展评分或特征中。本文的目的是回顾关于遗传生物标志物的现有知识，并简要提及IPMN的其他组学，如代谢组学、暴露组、微小RNA组和表观基因组学。
