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用于早期检测胰腺导管腺癌的代谢物生物标志物:一项系统综述。

Metabolite Biomarkers for Early Detection of Pancreatic Ductal Adenocarcinoma: A Systematic Review.

作者信息

Eze-Odurukwe Anthony, Rehman Abdur, Ayinla Lois, Anika Nabila N, Shahid Ramsha, Ugwuoru Amarachi L, Mansoor Muzafar, Kamran Muhammad

机构信息

Surgery, Salford Royal NHS Foundation Trust, Manchester, GBR.

Surgery, Mayo Hospital, Lahore, PAK.

出版信息

Cureus. 2024 Nov 26;16(11):e74528. doi: 10.7759/cureus.74528. eCollection 2024 Nov.

Abstract

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with a poor prognosis. This poor prognosis is largely attributed to a late-stage diagnosis. Recent advancements in metabolomics have emerged as a promising avenue for biomarker discovery in PDAC. This systematic review evaluates the potential of metabolite biomarkers for early detection of PDAC. Four studies meeting the inclusion criteria were analyzed, encompassing experimental, case-control, and prospective cohort designs. Key findings include the identification of distinct metabolic subtypes in PDAC with varying sensitivities to metabolic inhibitors. A biomarker signature comprising nine metabolites plus CA19-9 showed high accuracy in distinguishing PDAC from chronic pancreatitis, outperforming CA19-9 alone. Another study identified a five-metabolite signature demonstrating high diagnostic accuracy for pancreatic cancer, differentiating it from type 2 diabetes mellitus. A two-metabolite model (isoleucine and adrenic acid) showed superior performance in detecting stage-I PDAC compared to CA19-9. These studies consistently demonstrate altered metabolic pathways in PDAC patients compared to healthy controls and those with benign pancreatic conditions. Integrating metabolomic data with other molecular profiling approaches has become a powerful strategy for improving diagnostic accuracy. However, challenges remain, including the influence of confounding factors, the need for large-scale validation studies, and the standardization of metabolomic methods. The potential of artificial intelligence in interpreting complex metabolomic data offers promising avenues for future research. This review highlights the significant potential of metabolite biomarkers in early PDAC detection while emphasizing the need for further validation and refinement of these approaches.

摘要

胰腺导管腺癌(PDAC)仍然是最致命的恶性肿瘤之一,预后很差。这种不良预后很大程度上归因于晚期诊断。代谢组学的最新进展已成为在PDAC中发现生物标志物的一个有前景的途径。本系统评价评估了代谢物生物标志物在早期检测PDAC方面的潜力。分析了四项符合纳入标准的研究,包括实验性、病例对照和前瞻性队列设计。主要发现包括在PDAC中鉴定出对代谢抑制剂敏感性不同的不同代谢亚型。一个由九种代谢物加CA19-9组成的生物标志物特征在区分PDAC和慢性胰腺炎方面具有很高的准确性,优于单独的CA19-9。另一项研究确定了一个由五种代谢物组成的特征,对胰腺癌具有很高的诊断准确性,可将其与2型糖尿病区分开来。与CA19-9相比,一个由两种代谢物组成的模型(异亮氨酸和肾上腺酸)在检测I期PDAC方面表现更优。这些研究一致表明,与健康对照者和患有胰腺良性疾病的患者相比,PDAC患者的代谢途径发生了改变。将代谢组学数据与其他分子分析方法相结合已成为提高诊断准确性的有力策略。然而,挑战仍然存在,包括混杂因素的影响、大规模验证研究的必要性以及代谢组学方法的标准化。人工智能在解释复杂代谢组学数据方面的潜力为未来研究提供了有前景的途径。本综述强调了代谢物生物标志物在早期PDAC检测中的巨大潜力,同时强调了进一步验证和完善这些方法的必要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bf4/11671176/dcf40dcb80c1/cureus-0016-00000074528-i01.jpg

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