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Sequential Target and Non-target for Global (STANG) metabolomics in a Single Injection to Discover Prognostic Biomarkers of Acute Ischemic Stroke

ABSTRACT: Non-targeted metabolomics by an LC-MS system offers the advantage of wide signal coverage, enabling the discovery of novel biomarkers. However, it suffers from a low identification rate and limitations due to incomplete databases and reliable bioinformatics software to handle large data sets. On the other hand, targeted metabolomics focuses on specific biomarkers, allowing for a more directed search but potentially limiting the exploration of other potential biomarkers. To address these challenges, we developed a novel method called Sequential Target And Non-target for Global metabolomics (STANG metabolomics) using the functionality of the triple quadrupole-linear ion trap mass spectrometer (Q-Trap MS). This method combines the capabilities of a triple quadrupole for a wide linear range of MRM scans and an ion trap for fast full-scan acquisition. Our approach enables the direct analysis of known metabolites while simultaneously recording mass spectrometry signals of non-targeted substances in the full-scan spectra, facilitating comprehensive biomarker identification. Currently, 448 metabolite targets have been built, and the STANG metabolomics approach was applied to discover prognostic biomarkers of acute ischemic stroke. We identified 32 biomarkers through targeted metabolomics and 83 candidates through non-targeted metabolomics. The top four markers with the highest AUC values are lithocholic acid (AUC=0.999), ursodeoxycholic acid (AUC=0.985), 3,4-Dihydroxyphenylglycol (AUC=0.836), and 4-Hydroxybenzaldehyde (AUC=0.807). This approach shortens sample analysis time, reduces data conversion, increases metabolite coverage, and improves analytical sensitivity and accuracy. The potential of the STANG metabolomics approach was evaluated in discovering prognostic biomarkers of stroke, demonstrating its value as a comprehensive tool for biomarker discovery and disease mechanism elucidation.

INSTRUMENT(S): QTRAP 6500+

ORGANISM(S): Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Chen, chao-jung

PROVIDER: MSV000096787 | MassIVE | Tue Jan 07 01:24:00 GMT 2025

REPOSITORIES: MassIVE

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Project description:Background & Aims: Cholangiocarcinomas (CCAs), heterogeneous biliary tumors with dismal prognosis, lack accurate early-diagnostic methods, especially important for individuals at high-risk (i.e., primary sclerosing cholangitis (PSC)). Here, we searched for protein biomarkers in serum extracellular vesicles (EVs). Methods: EVs from patients with isolated PSC (n=45), concomitant PSC-CCA (n=42), PSC who developed CCA during follow-up (PSC to CCA; n=25), CCAs from non-PSC etiology (n=56), hepatocellular carcinoma (n=34) and healthy individuals (n=55) were characterized by mass-spectrometry. Diagnostic biomarkers of PSC-CCA, non-PSC CCA or CCAs regardless etiology (pan-CCAs) were defined, and their expression was evaluated in human organs/tissues and within CCA tumors at single-cell level. Prognostic EV-biomarkers for CCA were investigated. Results: High-throughput proteomics identified candidate diagnostic biomarkers for PSC-CCA, non-PSC CCA or pan-CCA, as well as and for differential diagnosis of intrahepatic CCA and HCC, that were cross-validated by ELISA using total serum. Machine learning logit modelling disclosed CRP/FRIL/Fibrinogen algorithm with diagnostic value for early-stage PSC-CCA vs isolated PSC (AUC=0.944; OR=82.0), overpowering CA19-9 (AUC=0.735; OR=9.3). An algorithm combining CRP/VWF/PIGR/ /Fibrinogen allowed the diagnosis of early-stage non-PSC CCAs compared to healthy individuals (AUC=0.999; OR=1115). Noteworthy, levels of Fibrinogen/CRP/PIGR/FRIL showed predictive capacity for CCA development in patients with PSC before clinical evidences of malignancy. Multi-organ transcriptomic analysis revealed that serum EV-biomarkers were mostly expressed in hepatobiliary tissues, and scRNA-seq and immunofluorescence analysis of CCA tumors showed their presence mainly in malignant cholangiocytes. Multivariable analysis unveiled EVprognostic biomarkers independent to clinical features, with COMP/GNAI2/CFAI and ACTN1/MYCT1/PF4V associated negatively or positively to patients’ survival, respectively. Conclusions: Serum EVs contain protein biomarkers for the prediction, early diagnosis and prognosis estimation of CCA, representing a novel tumor cell-derived liquid biopsy for personalized medicine.

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Sequential Target and Non-target for Global (STANG) metabolomics in a Single Injection to Discover Prognostic Biomarkers of Acute Ischemic Stroke

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