Download PDF
Horm Metab Res 2023; 55(10): 711-721
DOI: 10.1055/a-2105-6152
Original Article: Endocrine Research

Immune-Related Genes can Serve as Potential Biomarkers for Predicting Severe Acute Pancreatitis

Weijuan Zhao
1   Emergency, Affiliated Wuxi Fifth Hospital of Jiangnan University (Infectious Diseases Hospital of Wuxi), Wuxi, China
› Author Affiliations
› Further Information
Also available at
    Permissions and Reprints

Abstract

We aimed to investigate immune-related candidate genes for predicting the severity of acute pancreatitis (AP). RNA sequencing profile GSE194331 was downloaded, and differentially expressed genes (DEGs) were investigated. Meanwhile, the infiltration of immune cells in AP were assessed using CIBERSORT. Genes related with the infiltration of immune cells were investigated using weighted gene co-expression network analysis (WGCNA). Furthermore, immune subtypes, micro-environment, and DEGs between immune subtypes were explored. Immune-related genes, protein-protein interaction (PPI) network, and functional enrichment analysis were further performed. Overall, 2533 DEGs between AP and healthy controls were obtained. After trend cluster analysis, 411 upregulated and 604 downregulated genes were identified. Genes involved in two modules were significantly positively related to neutrophils and negatively associated with T cells CD4 memory resting, with correlation coefficient more than 0.7. Then, 39 common immune-related genes were obtained, and 56 GO BP were enriched these genes, including inflammatory response, immune response, and innate immune response; 10 KEGG pathways were enriched, including cytokine-cytokine receptor interaction, Th1 and Th2 cell differentiation, and IL-17 signaling pathway. Genes, including S100A12, MMP9, IL18, S100A8, HCK, S100A9, RETN, OSM, FGR, CAMP, were selected as genes with top 10 degree in PPI, and the expression levels of these genes increased gradually in subjects of healthy, mild, moderately severe, and severe AP. Our findings indicate a central role of immune-related genes in predicting the severity of AP, and the hub genes involved in PPI represent logical candidates for further study.

Key words

acute pancreatitis - differentially expressed genes - immune-related genes


Publication History

Received: 25 February 2023

Accepted after revision: 31 May 2023

Article published online:
30 June 2023

© 2023. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Petrov MS, Yadav D. Global epidemiology and holistic prevention of pancreatitis. Nat Rev Gastroenterol Hepatol 2019; 16: 175-184
    CrossrefPubMedGoogle Scholar
  • 2 Collaboration NCDRF. Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128.9 million children, adolescents, and adults. Lancet 2017; 390: 2627-2642
    CrossrefPubMedGoogle Scholar
  • 3 Krishna SG, Amrit K. et al. The changing epidemiology of acute pancreatitis hospitalizations: a decade of trends and the impact of chronic pancreatitis. Pancreas 2017; 46: 482-488
    CrossrefPubMedGoogle Scholar
  • 4 Petrov MS, Shanbhag S, Chakraborty M. et al. Organ failure and infection of pancreatic necrosis as determinants of mortality in patients with acute pancreatitis. Gastroenterology 2010; 139: 813-820
    CrossrefPubMedGoogle Scholar
  • 5 Wu BU, Bakker OJ, Papachristou GI. et al. Blood urea nitrogen in the early assessment of acute pancreatitis: an international validation study. Archiv Intern Med 2011; 171: 669-676
    PubMedGoogle Scholar
  • 6 Papachristou GI. Prediction of severe acute pancreatitis: current knowledge and novel insights. World J Gastroenterol 2008; 14: 6273-6275
    CrossrefPubMedGoogle Scholar
  • 7 Shen Y, Cui N, Miao B. et al. Immune dysregulation in patients with severe acute pancreatitis. Inflammation 2011; 34: 36-42
    PubMedGoogle Scholar
  • 8 Watanabe T, Kudo M, Strober W. Immunopathogenesis of pancreatitis. Mucosal Immunol 2017; 10: 283-298
    CrossrefPubMedGoogle Scholar
  • 9 Mounzer R, Langmead CJ, Wu BU. et al. Comparison of existing clinical scoring systems to predict persistent organ failure in patients with acute pancreatitis. Gastroenterology 2012; 142: 1476-1482
    CrossrefPubMedGoogle Scholar
  • 10 Robinson MD, McCarthy DJ, Smyth GK. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010; 26: 139-140
    CrossrefPubMedGoogle Scholar
  • 11 Shen Y, Cui N, Miao B. et al. Immune dysregulation in patients with severe acute pancreatitis. Inflammation 2011; 34: 36-42
    CrossrefPubMedGoogle Scholar
  • 12 Wilkerson MD, Hayes DN. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 2010; 26: 1572-1573
    CrossrefPubMedGoogle Scholar
  • 13 Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008; 9: 559
    PubMedGoogle Scholar
  • 14 Hanzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 2013; 14: 7
    CrossrefPubMedGoogle Scholar
  • 15 Kawada JI, Takeuchi S, Imai H. et al. Immune cell infiltration landscapes in pediatric acute myocarditis analyzed by CIBERSORT. J Cardiol 2020; 77: 174-178
    CrossrefPubMedGoogle Scholar
  • 16 Liberzon A, Subramanian A, Pinchback R. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 2011; 27: 1739-1740
    CrossrefPubMedGoogle Scholar
  • 17 Bhattacharya S, Andorf S, Gomes L. et al. ImmPort: disseminating data to the public for the future of immunology. Immunol Res 2014; 58: 234-239
    CrossrefPubMedGoogle Scholar
  • 18 Langfelder P, Horvath S, Langfelder P. et al. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2009; 9: 559
    CrossrefPubMedGoogle Scholar
  • 19 Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000; 28: 27-30
    CrossrefPubMedGoogle Scholar
  • 20 Morris JS, Kopetz S. Tumor microenvironment in gene signatures: critical biology or confounding noise?. Clin Cancer Res 2016; 3989-3991
    CrossrefPubMedGoogle Scholar
  • 21 Shannon P, Markiel A, Ozier O. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003; 13: 2498-2504
    CrossrefPubMedGoogle Scholar
  • 22 Tang Y, Li M, Wang J. et al. CytoNCA: a cytoscape plugin for centrality analysis and evaluation of protein interaction networks. Biosystems 2015; 127: 67-72
    CrossrefPubMedGoogle Scholar
  • 23 Geering B, Simon HU. Peculiarities of cell death mechanisms in neutrophils. Cell Death Differ 2011; 18: 1457-1469
    CrossrefPubMedGoogle Scholar
  • 24 Mantovani A, Cassatella MA, Costantini C. et al. Neutrophils in the activation and regulation of innate and adaptive immunity. Nat Rev Immunol 2011; 11: 519-531
    CrossrefPubMedGoogle Scholar
  • 25 Damian S, Morris JH, Helen C. et al. The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic Acids Res 2017; D362-D368
    PubMedGoogle Scholar
  • 26 Soehnlein O, Lindbom L. Phagocyte partnership during the onset and resolution of inflammation. Nat Rev Immunol 2010; 10: 427-439
    CrossrefPubMedGoogle Scholar
  • 27 McKinstry KK, Strutt TM, Swain SL. The potential of CD4 T-cell memory. Immunology 2010; 130: 1-9
    CrossrefPubMedGoogle Scholar
  • 28 Feng Z, Yinchu Z, Yinsheng S. et al. Potential effects of calcium binding protein S100A12 on severity evaluation and curative effect of severe acute pancreatitis. Inflammation 2015; 38: 290-297
    CrossrefPubMedGoogle Scholar
  • 29 Zhao B, Chen Y, Sun WW. et al. Effect of S100A12 and soluble receptor for advanced glycation end products on the occurrence of severe acute pancreatitis. J Digest Dis 2016; 17: 475-482
    CrossrefPubMedGoogle Scholar
  • 30 Schnekenburger J, Schick V, Krüger B. et al. The calcium binding protein S100A9 is essential for pancreatic leukocyte infiltration and induces disruption of cell-cell contacts. J Cell Physiol 2010; 216: 558-567
    CrossrefPubMedGoogle Scholar
  • 31 Vandooren J, Van den Steen PE, Opdenakker G. Biochemistry and molecular biology of gelatinase B or matrix metalloproteinase-9 (MMP-9): the next decade. Crit Rev Biochem Mol Biol 2013; 48: 222-272
    CrossrefPubMedGoogle Scholar
  • 32 Al Mofleh I-A. Severe acute pancreatitis: pathogenetic aspects and prognostic factors. World J Gastroenterol 2008; 14: 675-684
    CrossrefPubMedGoogle Scholar
  • 33 Chen P, Yuan Y, Wang S. et al. Serum matrix metalloproteinase 9 as a marker for the assessment of severe acute pancreatitis. Tohoku J Exp Med 2006; 208: 261-266
    CrossrefPubMedGoogle Scholar
  • 34 Wawrocki S, Druszczynska M, Kowalewicz-Kulbat M. et al. Interleukin 18 (IL-18) as a target for immune intervention. Acta Biochim Pol 2016; 63: 59-63
    CrossrefPubMedGoogle Scholar
  • 35 Pastor CM, Morel DR, Vonlaufen A. et al. Delayed production of IL-18 in lungs and pancreas of rats with acute pancreatitis. Pancreatology 2010; 10: 752-757
    CrossrefPubMedGoogle Scholar
  • 36 Carbone A, Vizio B, Novarino A. et al. IL-18 paradox in pancreatic carcinoma: elevated serum levels of free IL-18 are correlated with poor survival. J Immunother 2009; 32: 920-931
    CrossrefPubMedGoogle Scholar
  • 37 Li Z, Yu X, Werner J. et al. The role of interleukin-18 in pancreatitis and pancreatic cancer. Cytokine Growth Factor Rev 2019; 50: 1-12
    CrossrefPubMedGoogle Scholar
  • 38 Zhou Q, Chen B, Ji T. et al. Association of genetic variants in RETN, NAMPT and ADIPOQ gene with glycemic, metabolic traits and diabetes risk in a Chinese population. Gene 2018; 642: 439-446
    CrossrefPubMedGoogle Scholar
  • 39 Wos-Wroniewicz E, Caban M, Malecka-Panas E. Role of adipokines in the assessment of severity and predicting the clinical course of acute pancreatitis. J Physiol Pharmacol 2020; 71 DOI: 10.26402/jpp.2020.5.01.
    CrossrefPubMedGoogle Scholar
  • 40 Bosch M, Sánchez-Álvarez M, Fajardo A. et al. Mammalian lipid droplets are innate immune hubs integrating cell metabolism and host defense. Science 2020; 370: eaay8085
    CrossrefPubMedGoogle Scholar
  • 41 Liu Q, Li L, Xu D. et al. Identification of novel immune-related targets mediating disease progression in acute pancreatitis. Front Cell Infect Microbiol 2022; 12: 1052466
    CrossrefPubMedGoogle Scholar
  • 42 Adam J, Bartosz L, Anna J. et al. Interleukin 18 as an early marker or prognostic factor in acute pancreatitis. Przeglad Gastroenterol 2015; 10: 203-207
    PubMedGoogle Scholar
  • 43 Du M, Wen G, Jin J. et al. Mangiferin prevents the growth of gastric carcinoma by blocking the PI3K-Akt signalling pathway. Anti-Cancer Drugs 2018; 29: 167-175
    CrossrefPubMedGoogle Scholar
  • 44 Rodriguez-Nicolas A, Martinez-Chamorro A, Jimenez P. et al. TH1 and TH2 cytokine profiles as predictors of severity in acute pancreatitis. Pancreas 2018; 47: 400-405
    CrossrefPubMedGoogle Scholar
  • 45 Inman KS, Francis AA, Murray NR. Complex role for the immune system in initiation and progression of pancreatic cancer. World J Gastroenterol 2014; 20: 11160-11181
    CrossrefPubMedGoogle Scholar
  • 46 Lee DW, Cho CM. Predicting severity of acute pancreatitis. Medicina (Kaunas) 2022; 58: 787
    CrossrefPubMedGoogle Scholar
  • 47 Zhang D, Wang T, Dong X. et al. Systemic immune-inflammation index for predicting the prognosis of critically ill patients with acute pancreatitis. Int J Gen Med 2021; 14: 4491-4498
    CrossrefPubMedGoogle Scholar