Identification of novel biomarkers based on lipid metabolism-related molecular subtypes for moderately severe and severe acute pancreatitis

Abstract

Abstract Background Acute pancreatitis (AP) is an unpredictable and potentially fatal disorder. A derailed or unbalanced immune response may be the root of the disease’s severe course. Disorders of lipid metabolism are highly correlated with the occurrence and severity of AP. We aimed to characterize the contribution and immunological characteristics of lipid metabolism-related genes (LMRGs) in non-mild acute pancreatitis (NMAP) and identify a robust subtype and biomarker for NMAP. Methods The expression mode of LMRGs and immune characteristics in NMAP were examined. Then LMRG-derived subtypes were identified using consensus clustering. The weighted gene co-expression network analysis (WGCNA) was utilized to determine hub genes and perform functional enrichment analyses. Multiple machine learning methods were used to build the diagnostic model for NMAP patients. To validate the predictive effectiveness, nomograms, receiver operating characteristic (ROC), calibration, and decision curve analysis (DCA) were used. Using gene set variation analysis (GSVA) and single-cell analysis to study the biological roles of model genes. Results Dysregulated LMRGs and immunological responses were identified between NMAP and normal individuals. NMAP individuals were divided into two LMRG-related subtypes with significant differences in biological function. The cluster-specific genes are primarily engaged in the regulation of defense response, T cell activation, and positive regulation of cytokine production. Moreover, we constructed a two-gene prediction model with good performance. The expression of CARD16 and MSGT1 was significantly increased in NMAP samples and positively correlated with neutrophil and mast cell infiltration. GSVA results showed that they are mainly upregulated in the T cell receptor complex, immunoglobulin complex circulating, and some immune-related routes. Single-cell analysis indicated that CARD16 was mainly distributed in mixed immune cells and macrophages, and MGST1 was mainly distributed in exocrine glandular cells. Conclusions This study presents a novel approach to categorizing NMAP into different clusters based on LMRGs and developing a reliable two-gene biomarker for NMAP.

Keywords

• Acute pancreatitis
• Lipid metabolism
• Machine learning
• Biomarkers
• Molecular clusters