Oxylipins-Omics Combine With Single-Molecule Analysis Identifies 13(S)-HODE Positive Extracellular Vesicles as a...

Efficient diagnostic biomarkers enable early detection of hepatocellular carcinoma (HCC), which improves survival. Circulating extracellular vesicles (EVs) in plasma, as a noninvasive diagnostic carrier, have caused widely concern. Here, a novel oxylipidomic profiling was used to analysis the HCC patients' tissue-derived EVs (TD-EVs) cargo and revealing differentially expressed oxylipins (vs. adjacent tissues, p<0.05), which undetected in bulk tissues. The hub oxylipins 13(S)-HODE discovered in TD-EVs was validated in plasma derived EVs (PD-EVs) by using single-molecule analysis platform, showing superior efficacy in AFP-negative HCC patients (AUC = 0.8474 vs. 0.7627 in AFP-positive). Clinically, machine learning integrating EV-derived 13(S)-HODE with routine clinical parameters of HCC patients was developed to optimize diagnostic classification. The machine learning model, LightGBM, achieved outstanding performance: AUC_mean = 0.962, Sensitivity = 0.933 (0.660-0.997), Specificity: 0.957(0.760-0.998), 95%CI: 0.958-1.000 in multi-group classification of HCC diagnostics, demonstrating the enhanced diagnostic efficiency of 13(S)-HODE positive EV subpopulation with other clinical data. This study firstly establishes EV-derived 13(S)-HODE as a concordantly biomarker across tissue and plasma sources. Furthermore, single-molecule analysis platform offers it a highly sensitive diagnostic performance for HCC, particularly valuable for AFP-negative HCC subgroup.