ArticleLipids in health and disease2024
Identification of novel biomarkers based on lipid metabolism-related molecular subtypes for moderately severe and severe acute pancreatitis.
Article in Lipids in health and disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 14 citations in OpenAlex.
- The membrane protein Amuc_1098 from Akkermansia muciniphila alleviates acute pancreatitis via TLR2 signaling and glycerophospholipid metabolism remodeling.Nature communications · 2026Article
- Identification of Hub Neutrophil Coexpressed Genes in Acute PancreatitisEndocrine, metabolic & immune disorders drug targets · 2026Article
- Integrating urine metabolomic biomarkers and machine learning algorithms to predict preeclampsia.European journal of medical research · 2025Article
- Roles of lactylation in lipid metabolism and related diseases.Cell death discovery · 2025Review
- Identifying Lactylation-related biomarkers and therapeutic drugs in ulcerative colitis: insights from machine learning and molecular docking.BMC pharmacology & toxicology · 2025Article
- Identification of mitophagy-related biomarkers in severe acute pancreatitis: integration of WGCNA, machine learning algorithms and scRNA-seq.Frontiers in immunology · 2025Article
- Radiomics-based prediction of recurrent acute pancreatitis in individuals with metabolic syndrome using T2WI magnetic resonance imaging data.Frontiers in medicine · 2025Article
- Identification of biomarkers associated with programmed cell death in liver ischemia-reperfusion injury: insights from machine learning frameworks and molecular docking in multiple cohorts.Frontiers in medicine · 2025Article
- Integrated Multiomics Unravels Hedgehog (HH) Signaling Characteristics in Pancreatic Cancer (PC) and DCBLD2 Regulates HH Signaling to Drive PC Progression.Human mutation · 2025Article
- Exploring the pathogenesis, biomarkers, and potential drugs for type 2 diabetes mellitus and acute pancreatitis through a comprehensive bioinformatic analysis.Frontiers in endocrinology · 2024Article
- A stumbling block in pancreatic cancer treatment: drug resistance signaling networks.Frontiers in cell and developmental biology · 2024Review
- Identification of Biomarkers Associated with Oxidative Stress and Immune Cells in Acute Pancreatitis.Journal of inflammation research · 2024Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAcute 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.
methodsThe 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.
resultsDysregulated 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.
conclusionsThis study presents a novel approach to categorizing NMAP into different clusters based on LMRGs and developing a reliable two-gene biomarker for NMAP.
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