Integrated and Ultrafast Multiomics Sample Preparation Workflow for Screening Biomarker Panel of Platelet Biomolecules for Early Diagnosis of HCC

Early detection of hepatocellular carcinoma (HCC) remains a persistent worldwide challenge. Owing to its minimal invasiveness, liquid biopsy has emerged as a promising alternative for early screening. As key components of the tumor microenvironment (TME), platelets (PLTs) represent a rich source of biomolecular information that complements the data from conventional plasma and serum samples. Integrative multiomics analysis of such data offers a powerful strategy to deepen our understanding of hepatocarcinogenesis and accelerate the discovery of robust biomarker panels. Here, we described an integrative and ultrafast multiomics sample preparation (IAU-MOSP) strategy for the high-purity platelets. The optimized IAU-MOSP method shortened the multiomics workflow from over 24 to 6 h while yielding comparable biomolecule identifications to those of conventional methods. Then, the workflow was applied in an HCC cohort (n = 68) study. We quantified 6660 biomolecules with high reproducibility (median CVs: 0.31–0.39). The data exhibited strong cross-omics correlations, particularly between proteins and lipids (r = 0.75) as well as protein and metabolite (r = 0.68) groups. Differential analysis revealed 10 biomolecules significantly dysregulated in HCC platelets (TEK, citric acid, glycerol-3-phosphate (G3P), P2RX4, malic acid, ATP, PRG3, ITGAM, CXCR2, ITGB2) that participate in key pathways driving proliferation and metastasis. Accompanied by machine learning, the 10 biomolecules were ultimately identified as a potential biomarker panel for early diagnosis of HCC. It shows superior diagnostic efficacy (accuracy = 0.81, sensitivity = 0.74) over α-fetoprotein (AFP) (accuracy = 0.75, sensitivity = 0.45) for early HCC detection.肝细胞癌（hepatocellular carcinoma, HCC）的早期检测仍是全球范围内持续存在的临床难题。得益于微创特性，液体活检已成为早期肿瘤筛查极具前景的替代方案。作为肿瘤微环境（tumor microenvironment, TME）的关键组成部分，血小板（platelets, PLTs）是一类富含生物分子信息的重要来源，可有效补充常规血浆及血清样本的检测数据。针对此类样本开展整合多组学分析，为深化我们对肝细胞癌变机制的理解、加速稳健生物标志物组合的发现提供了强有力的研究策略。本文报道了一种针对高纯度血小板的整合超快速多组学样本制备（integrative and ultrafast multiomics sample preparation, IAU-MOSP）策略。经优化的IAU-MOSP方法将多组学工作流程从24小时以上缩短至6小时，同时获得的生物分子鉴定结果与传统方法相当。随后，该工作流程被应用于一项肝细胞癌队列（n=68）研究。研究团队以高重现性定量得到了6660个生物分子（中位数变异系数：0.31~0.39）。该数据集展现出较强的组间相关性，尤其在蛋白质与脂质（r=0.75）、蛋白质与代谢物（r=0.68）类群之间。差异表达分析揭示了10个在肝细胞癌血小板中显著失调的生物分子：TEK、柠檬酸、甘油-3-磷酸（glycerol-3-phosphate, G3P）、P2RX4、苹果酸、ATP、PRG3、ITGAM、CXCR2及ITGB2，这些分子参与驱动肿瘤增殖与转移的关键通路。结合机器学习分析，最终确定这10个生物分子可作为肝细胞癌早期诊断的潜在生物标志物组合。相较于甲胎蛋白（α-fetoprotein, AFP）（准确率0.75，灵敏度0.45），该组合在早期肝细胞癌检测中展现出更优的诊断效能（准确率0.81，灵敏度0.74）。