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.