Gene Expression Profiling: Identification of Novel Pathways and Potential Biomarkers in Severe Acute Pancreatitis

Abstract

BACKGROUND: Determining the risk of developing severe acute pancreatitis (AP) on presentation to hospital is difficult but vital to enable early management decisions that reduce morbidity and mortality. The objective of this study was to determine global gene expression profiles of patients with different acute pancreatitis severity to identify genes and molecular mechanisms involved in the pathogenesis of severe AP. STUDY DESIGN: AP patients (n = 87) were recruited within 24 hours of admission to the Emergency Department and were confirmed to exhibit at least 2 of the following features: (1) abdominal pain characteristic of AP, (2) serum amylase and/or lipase more than 3-fold the upper laboratory limit considered normal, and/or (3) radiographically demonstrated AP on CT scan. Severity was defined according to the Revised Atlanta classification. Thirty-two healthy volunteers were also recruited and peripheral venous blood was collected for performing RNA-Seq. RESULTS: In severe AP, 422 genes (185 upregulated, 237 downregulated) were significantly differentially expressed when compared with moderately severe and mild cases. Pathway analysis revealed changes in specific innate and adaptive immune, sepsis-related, and surface modification pathways in severe AP. Data-driven approaches revealed distinct gene expression groups (endotypes), which were not entirely overlapping with the clinical Atlanta classification. Importantly, severe and moderately severe AP patients clustered away from healthy controls, whereas mild AP patients did not exhibit any clear separation, suggesting distinct underlying mechanisms that may influence severity of AP. CONCLUSION: There were significant differences in gene expression affecting the severity of AP, revealing a central role of specific immunological pathways. Despite the existence of patient endotypes, a 4-gene transcriptomic signature (S100A8, S100A9, MMP25, and MT-ND4L) was determined that can predict severe AP with an accuracy of 64%.

Identification of novel immune-related targets mediating disease progression in acute pancreatitis

“…These inconsistent results suggest that SIRS and CARS are activated simultaneously during the progression of AP, and balancing the intensity and order of induction of the immune response might be an effective therapeutic approach for AP. Many studies have shown that the course and severity of AP are largely determined by the crosstalk between innate and adaptive immune responses (Sendler et al, 2020;Xu et al, 2020;Nesvaderani et al, 2022), yet the essential genes remain largely unclear. In the current study, by conducting an integrative analysis of samples from AP patients and injured tissues from an AP animal model, we intended to explore the susceptibility genes and biological pathways involved in the pathogenesis of AP.…”

“…The count-based gene expression profiles of peripheral blood and associated clinical information for the three AP severity levels were acquired from the Gene Expression Omnibus (GEO) database (accession number: GSE194331), including 32 healthy donors, 57 mild acute pancreatitis (MAP) patients, 20 moderately severe acute pancreatitis (M-SAP) patients and 10 severe acute pancreatitis (SAP) patients (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE194331). This dataset was deposited by Maryam N et al from the Nepean Hospital (Nesvaderani et al, 2022). The severity levels of AP were evaluated according to the Revised Atlanta classification.…”

Novel insight on marker genes and pathogenic peripheral neutrophil subtypes in acute pancreatitis

“…High-throughput bulk sequencing datasets related to peripheral blood of AP and COVID-19 infection were screened in the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/). The GSE194331 dataset, which includes peripheral blood gene expression data from 87 patients with AP of varying severity (mild = 57, moderate to severe = 20, and severe = 10) within 24 h of presentation to the hospital and peripheral blood gene expression data from 32 healthy controls, was used in this study (18). Additionally, to explore the common biological mechanism between AP and COVID-19, the GSE152418 dataset was used in the following analysis as the COVID group, including 17 COVID-19 subjects and 17 healthy controls (20).…”

“…Bioinformatic analysis of high-throughput sequencing data plays an important role in medical research. In fact, there are some studies based on bioinformatic analysis in AP (15)(16)(17)(18). However, all of these analyses are based on the identification of differentially expressed genes (DEGs), and this method could result in the omission of large-scale information from unrecognized genes.…”

Fighting Fire with Fire: Exosomes and Acute Pancreatitis-Associated Acute Lung Injury

“…These data show a link between the severity of AP and the pro-inflammatory activity of exosome-specific S100A8/S100A9. Nesvaderan et al developed a four-gene signature including S100A8, S100A9, matrix metalloproteinase (MMP)25, and MT-ND4L that predicts severe AP with 4% accuracy [ 134 ]. According to their findings, this gene panel might be used to detect severe AP at an early stage.…”