Integrative transcriptomic analysis of peripheral blood identifies NETosis-associated programs and a stratified adaptive immune suppression pattern in severe acute pancreatitis

Background 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. Methods 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. Results 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. Conclusion 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.