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oai:figshare.com:article/33998988 2026-09-25T17:38:12Z category_8 category_12 category_13 category_46 category_734 category_64 category_132 category_133 category_134 portal_5 item_type_3 month_year_09_2026

<p>Core machine learning candidate biomarkers.</p> Yaobin He (25111368) Kairui Liu (2743654) Rong Chen (29176) Yipei Huang (11651614) Youxing Huang (6802448) Microbiology Cell Biology Genetics Immunology Biological Sciences not elsewhere classified Cancer Infectious Diseases Plant Biology Virology repeated stratified 5 rat ileal tissue rat ileal scrna prospective human cohorts integrative transcriptomic analysis identify candidate genes expression network analysis cohort auc values algorithm machine learning distinguish broad ap mild acute pancreatitis peripheral blood rna level transcriptomic features gsea ), ssgsea 951 ), c1qa spectrum ap group sap vs hc weighted gene co type context rather persistent organ failure seq provided supportive ap severity progression sap vs map mild ap sap group type context organ failure rfe ), gene estimates additionally co 754 ), sap relative seq data associated features supportive cell cell rna xlink "> substantial mortality related programs random forest oxidative phosphorylation independent validation healthy controls <div> <p>Background</p><p>Severe acute pancreatitis (SAP) is associated with persistent organ failure and substantial mortality, yet the transcriptomic mechanisms underlying progression from mild acute pancreatitis to severe-spectrum disease remain incompletely defined. Early identification of patients at risk of persistent organ failure remains an important unmet clinical need.</p> <p>Methods</p><p>We performed an integrative transcriptomic analysis of peripheral blood RNA-seq data from GSE194331 (healthy controls [HC], n = 32; mild acute pancreatitis [MAP], n = 57; severe-spectrum AP group [SAP group], n = 30, comprising MSAP n = 20 and severe AP n = 10; sampled within 24 hours of admission). A dual-comparison framework was established using SAP vs HC and SAP vs MAP to distinguish broad AP-associated changes from organ failure-associated features. Gene set enrichment analysis (GSEA), ssGSEA-based immune signature analysis, triple-algorithm machine learning (LASSO, random forest, and SVM-RFE), and weighted gene co-expression network analysis (WGCNA) were integrated to identify candidate genes and characterize associated biological programs. Cross-species single-cell RNA-seq data from rat ileal tissue (GSE244963) were used only as supportive cell-type context for candidate genes.</p> <p>Results</p><p>GSEA identified enrichment of NETosis, complement/coagulation, oxidative phosphorylation, and multiple inflammatory cell death-related programs in the SAP group. ssGSEA revealed reduced adaptive immune-associated signatures, with CD8 + T-cell and NK-cell signatures already reduced in MAP and CD4 + T-cell and Treg-associated signatures showing additional reduction in SAP relative to MAP. Triple-algorithm machine learning identified three core candidate genes with discovery-cohort AUC values: MRPL51 (ML-A, SAP vs HC, AUC = 0.951), C1QA (ML-B, SAP vs MAP, AUC = 0.754), and DACT1 (ML-B, SAP vs MAP, AUC = 0.758). Repeated stratified 5-fold cross-validation supported internal stability of the single-gene estimates, but no independent external validation cohort was available. A neutrophil-associated NETosis gene triad (OLFM4, LTF, and CEACAM6) was additionally co-selected by LASSO and random forest in ML-B. Rat ileal scRNA-seq provided supportive, cross-species cell-type context rather than definitive validation.</p> <p>Conclusion</p><p>Integrative transcriptomic analysis of peripheral blood identifies candidate transcriptomic markers and suggests neutrophil-associated, metabolic, and adaptive immune programs associated with AP severity progression. The stratified adaptive immune signature reduction pattern and the NETosis-associated gene triad may represent discovery-level transcriptomic features of progression from mild AP to the severe-spectrum AP group. Independent validation in prospective human cohorts is required.</p></div> 2026-09-25T17:37:49Z Dataset Dataset 10.1371/journal.pone.0349692.t003 https://figshare.com/articles/dataset/_p_Core_machine_learning_candidate_biomarkers_p_/33998988 CC BY 4.0