Research Institute of Pharmaceutical Sciences and College of Pharmacy, Seoul National University, Seoul, 08826, South Korea.
School of Medicine, Vietnam National University, Ho Chi Minh City, 700000, Vietnam.
Metabolomics. 2018 Aug 10;14(8):109. doi: 10.1007/s11306-018-1404-2.
Metabolomics is an emerging approach for early detection of cancer. Along with the development of metabolomics, high-throughput technologies and statistical learning, the integration of multiple biomarkers has significantly improved clinical diagnosis and management for patients.
In this study, we conducted a systematic review to examine recent advancements in the oncometabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer.
PubMed, Scopus, and Web of Science were searched for relevant studies published before September 2017. We examined the study designs, the metabolomics approaches, and the reporting methodological quality following PRISMA statement. RESULTS AND CONCLUSION: The included 25 studies primarily focused on the identification rather than the validation of predictive capacity of potential biomarkers. The sample size ranged from 10 to 8760. External validation of the biomarker panels was observed in nine studies. The diagnostic area under the curve ranged from 0.68 to 1.00 (sensitivity: 0.43-1.00, specificity: 0.73-1.00). The effects of patients' bio-parameters on metabolome alterations in a context-dependent manner have not been thoroughly elucidated. The most reported candidates were glutamic acid and histidine in seven studies, and glutamine and isoleucine in five studies, leading to the predominant enrichment of amino acid-related pathways. Notably, 46 metabolites were estimated in at least two studies. Specific challenges and potential pitfalls to provide better insights into future research directions were thoroughly discussed. Our investigation suggests that metabolomics is a robust approach that will improve the diagnostic assessment of pancreatic cancer. Further studies are warranted to validate their validity in multi-clinical settings.
代谢组学是一种用于癌症早期检测的新兴方法。随着代谢组学、高通量技术和统计学习的发展,多种生物标志物的整合极大地改善了患者的临床诊断和管理。
本研究通过系统评价,考察了基于代谢组学的胰腺癌诊断生物标志物发现和验证的最新进展。
检索了 PubMed、Scopus 和 Web of Science 中截至 2017 年 9 月前发表的相关研究。我们按照 PRISMA 声明检查了研究设计、代谢组学方法和报告方法学质量。
纳入的 25 项研究主要集中在鉴定而不是验证潜在生物标志物的预测能力上。样本量范围为 10 至 8760。有 9 项研究观察到生物标志物组合的外部验证。诊断曲线下面积范围为 0.68-1.00(灵敏度:0.43-1.00,特异性:0.73-1.00)。患者生物参数对代谢组变化的影响还没有被充分阐明。在七种研究中报道最多的候选物是谷氨酸和组氨酸,在五种研究中报道最多的候选物是谷氨酰胺和异亮氨酸,这导致了与氨基酸相关的途径的主要富集。值得注意的是,至少有两项研究估计了 46 种代谢物。还深入讨论了为了提供对未来研究方向的更好见解而存在的具体挑战和潜在陷阱。我们的研究表明代谢组学是一种强大的方法,将改善对胰腺癌的诊断评估。需要进一步的研究来验证它们在多临床环境中的有效性。