<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Chau TH</submitter><funding>Korea Basic Science Institute</funding><funding>Mizutani Foundation for Glycoscience</funding><funding>Macquarie University</funding><funding>Cancer Institute NSW</funding><funding>Royal Adelaide Hospital Research Fund</funding><funding>Australian Research Council</funding><pagination>101470</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12794580</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>25(1)</volume><pubmed_abstract>Septic shock, the excessive immune response to pathogen infection, accounts globally for ∼20% of all deaths. Current methods to establish disease severity are unacceptably slow, unspecific, and insensitive, hindering timely and effective treatment. Aiming to establish easy-to-measure glyco-signatures that may identify the most critically unwell patients, we applied comparative glycomics and glycoproteomics to sera longitudinally collected from septic shock survivors (n = 29) and nonsurvivors (n = 8). Glycomics of all 134 serum samples (sampled daily until recovery/death) revealed significant N-glycome dynamics across both patient groups. Unsupervised clustering of the serum N-glycome measured upon intensive care unit (ICU) admission (day 1) indicated survivorship-specific glyco-signatures. We therefore employed machine learning to train a random forest model using the serum N-glycome data. The model accurately classified survivorship outcomes of 35 of 37 patients (accuracy 94.6%) and correctly predicted 29 of 29 survivors (specificity 100%) and six of eight nonsurvivors (sensitivity 75%). Interrogation of the serum N-glycome data revealed that Lewis x (Le&lt;sup>x&lt;/sup>)-type N-glycans are elevated in nonsurvivors relative to survivors at ICU admission, a finding recapitulated by glycoproteomics. Among the 58 other Le&lt;sup>x&lt;/sup>-containing serum glycoproteins that were strongly associated with acute phase response and stress pathways, alpha-1-acid-glycoprotein (AGP-1) was identified as a principal carrier of Le&lt;sup>x&lt;/sup> glycoepitopes with a potential to stratify septic shock survivors from nonsurvivors (AUC 0.90). This study lays a foundation for risk stratification of septic shock patients by uncovering easy-to-assay AGP-1-Le&lt;sup>x&lt;/sup> glycoforms that identify individuals experiencing poor survival outcomes already upon ICU admission, with the potential to translate to early individualized clinical care at the bedside.</pubmed_abstract><journal>Molecular &amp; cellular proteomics : MCP</journal><pubmed_title>Serum AGP-1-Le&amp;lt;sup&amp;gt;x&amp;lt;/sup&amp;gt; Glycoforms Report on Survivorship of Patients with Septic Shock Upon Admission to Intensive Care Unit.</pubmed_title><pmcid>PMC12794580</pmcid><funding_grant_id>250004</funding_grant_id><funding_grant_id>2017152</funding_grant_id><funding_grant_id>ECF181259</funding_grant_id><funding_grant_id>FT210100455</funding_grant_id><funding_grant_id>C523400</funding_grant_id><funding_grant_id>20224231</funding_grant_id><pubmed_authors>Traini M</pubmed_authors><pubmed_authors>Kawahara R</pubmed_authors><pubmed_authors>Torpy DJ</pubmed_authors><pubmed_authors>Chernykh A</pubmed_authors><pubmed_authors>Hwang H</pubmed_authors><pubmed_authors>Thaysen-Andersen M</pubmed_authors><pubmed_authors>Meyer EJ</pubmed_authors><pubmed_authors>Fehring J</pubmed_authors><pubmed_authors>Chatterjee S</pubmed_authors><pubmed_authors>Chau TH</pubmed_authors><pubmed_authors>Caulfield L</pubmed_authors></additional><is_claimable>false</is_claimable><name>Serum AGP-1-Le&amp;lt;sup&amp;gt;x&amp;lt;/sup&amp;gt; Glycoforms Report on Survivorship of Patients with Septic Shock Upon Admission to Intensive Care Unit.</name><description>Septic shock, the excessive immune response to pathogen infection, accounts globally for ∼20% of all deaths. Current methods to establish disease severity are unacceptably slow, unspecific, and insensitive, hindering timely and effective treatment. Aiming to establish easy-to-measure glyco-signatures that may identify the most critically unwell patients, we applied comparative glycomics and glycoproteomics to sera longitudinally collected from septic shock survivors (n = 29) and nonsurvivors (n = 8). Glycomics of all 134 serum samples (sampled daily until recovery/death) revealed significant N-glycome dynamics across both patient groups. Unsupervised clustering of the serum N-glycome measured upon intensive care unit (ICU) admission (day 1) indicated survivorship-specific glyco-signatures. We therefore employed machine learning to train a random forest model using the serum N-glycome data. The model accurately classified survivorship outcomes of 35 of 37 patients (accuracy 94.6%) and correctly predicted 29 of 29 survivors (specificity 100%) and six of eight nonsurvivors (sensitivity 75%). Interrogation of the serum N-glycome data revealed that Lewis x (Le&lt;sup>x&lt;/sup>)-type N-glycans are elevated in nonsurvivors relative to survivors at ICU admission, a finding recapitulated by glycoproteomics. Among the 58 other Le&lt;sup>x&lt;/sup>-containing serum glycoproteins that were strongly associated with acute phase response and stress pathways, alpha-1-acid-glycoprotein (AGP-1) was identified as a principal carrier of Le&lt;sup>x&lt;/sup> glycoepitopes with a potential to stratify septic shock survivors from nonsurvivors (AUC 0.90). This study lays a foundation for risk stratification of septic shock patients by uncovering easy-to-assay AGP-1-Le&lt;sup>x&lt;/sup> glycoforms that identify individuals experiencing poor survival outcomes already upon ICU admission, with the potential to translate to early individualized clinical care at the bedside.</description><dates><release>2026-01-01T00:00:00Z</release><publication>2026 Jan</publication><modification>2026-07-04T03:19:20.173Z</modification><creation>2026-07-04T03:12:12.649Z</creation></dates><accession>S-EPMC12794580</accession><cross_references><pubmed>41260501</pubmed><doi>10.1016/j.mcpro.2025.101470</doi></cross_references></HashMap>