Hamed Mahmoud Assem, Wasinger Valerie, Wang Qi, Biazik Joanna, Graham Peter, Malouf David, Bucci Joseph, Li Yong
St. George and Sutherland Clinical Campuses, School of Clinical Medicine, University of New South Wales (UNSW) Sydney, Kensington, NSW 2052, Australia.
Cancer Care Centre, St. George Hospital, Kogarah, NSW 2217, Australia.
Metabolites. 2024 Jun 28;14(7):367. doi: 10.3390/metabo14070367.
Conventional diagnostic tools for prostate cancer (PCa), such as prostate-specific antigen (PSA), transrectal ultrasound (TRUS), digital rectal examination (DRE), and tissue biopsy face, limitations in individual risk stratification due to invasiveness or reliability issues. Liquid biopsy is a less invasive and more accurate alternative. Metabolomic analysis of extracellular vesicles (EVs) holds a promise for detecting non-genetic alterations and biomarkers in PCa diagnosis and risk assessment. The current research gap in PCa lies in the lack of accurate biomarkers for early diagnosis and real-time monitoring of cancer progression or metastasis. Establishing a suitable approach for observing dynamic EV metabolic alterations that often occur earlier than being detectable by other omics technologies makes metabolomics valuable for early diagnosis and monitoring of PCa. Using four distinct metabolite extraction approaches, the metabolite cargo of PC3-derived large extracellular vesicles (lEVs) was evaluated using a combination of methanol, cell shearing using microbeads, and size exclusion filtration, as well as two fractionation chemistries (pHILIC and C18 chromatography) that are also examined. The unfiltered methanol-microbeads approach (MB-UF), followed by pHILIC LC-MS/MS for EV metabolite extraction and analysis, is effective. Identified metabolites such as L-glutamic acid, pyruvic acid, lactic acid, and methylmalonic acid have important links to PCa and are discussed. Our study, for the first time, has comprehensively evaluated the extraction and separation methods with a view to downstream sample integrity across omics platforms, and it presents an optimised protocol for EV metabolomics in PCa biomarker discovery.
前列腺癌（PCa）的传统诊断工具，如前列腺特异性抗原（PSA）、经直肠超声（TRUS）、直肠指检（DRE）和组织活检，由于存在侵入性或可靠性问题，在个体风险分层方面存在局限性。液体活检是一种侵入性较小且更准确的替代方法。细胞外囊泡（EVs）的代谢组学分析有望在PCa诊断和风险评估中检测非基因改变和生物标志物。PCa目前的研究差距在于缺乏用于早期诊断以及癌症进展或转移实时监测的准确生物标志物。建立一种合适的方法来观察动态EV代谢改变（这种改变通常比其他组学技术可检测到的时间更早出现），使得代谢组学对于PCa的早期诊断和监测具有重要价值。使用四种不同的代谢物提取方法，结合甲醇、使用微珠进行细胞剪切以及尺寸排阻过滤，对PC3来源的大型细胞外囊泡（lEVs）的代谢物含量进行了评估，同时还研究了两种分级化学方法（亲水相互作用液相色谱（pHILIC）和C18色谱）。未过滤的甲醇 - 微珠方法（MB - UF），随后采用pHILIC液相色谱 - 串联质谱（LC - MS/MS）进行EV代谢物提取和分析，是有效的。已鉴定出的代谢物如L - 谷氨酸、丙酮酸、乳酸和甲基丙二酸与PCa有重要联系，并进行了讨论。我们的研究首次全面评估了提取和分离方法，以确保跨组学平台的下游样本完整性，并提出了一种用于PCa生物标志物发现的EV代谢组学优化方案。
