IL18 in acute pancreatitis: Machine learning and two-sample Mendelian randomization study

IL18 in acute pancreatitis: Machine learning and two-sample Mendelian randomization study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article IL18 in acute pancreatitis: Machine learning and two-sample Mendelian randomization study Kena Zhou, Leheng Liu, Jingpiao Bao, Chuanyang Wang, Xingpeng Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3965868/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective Acute pancreatitis is a common disease whose treatment is limited to symptomatic support, thus finding effective biomarkers is of great significance for early diagnosis and therapy. Methods Bioinformatics and machine learning were applied to evaluate the expression, clinical features, biological function and immunological effects of the characteristic genes in AP. Meanwhile, AP mice models were constructed to verify the results in vivo. Finally, Mendelian randomization studies were performed to determine the causal relationship between IL-18 and AP through genome-wide association studies. Results A total of 100 core genes were obtained via differential analysis and PPI interaction network. IL18 was identified as the characteristic gene for AP by machine learning through three algorithmic. The expression of IL18 was increased significantly in AP (P < 0.001). The AUC value of IL18 in the diagnosis of AP was 0.917, exhibiting high clinical value. Moreover, IL18 was associated with various immune cells involved in the progression of AP. Through inverse variance weighting (IVW), we found that the OR for IL18 and AP was 0.908 (95%CI = 0.843–0.978, p = 0.011). Conclusions IL18 is a pivotal biomarker predicting the clinical prognosis and immune response in AP, which is proved to serve as a protective factor. IL18 acute pancreatitis machine learning Mendelian randomization immune responses Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Acute pancreatitis (AP) is a clinically common inflammatory disease that is mild in most patients [ 1 ]. About 35% of AP patients will progress to moderate severe acute pancreatitis (MSAP) or severe acute pancreatitis (SAP), conditions with poor prognosis and life-threatening [ 2 ]. AP is a sophisticated disease that varies in severity and course. Early diagnosis and stratification of severity contribute to timely treatment and better prognosis [ 3 ]. With the development of genomics in the last decade, our understanding of the pathophysiological mechanism of AP has gradually shifted from traditional clinical indicators to the gene era [ 4 – 6 ]. High-throughput sequencing accelerates basic research and makes deep molecular characterization of patient samples routine [ 7 ]. Machine learning is superior to traditional statistical models, and can develop predictive models for associations between high-throughput sequencing results and features [ 8 – 10 ]. It is becoming an integral part of modern data mining and clinical diagnosis [ 11 – 13 ]. Interleukin-18 (IL-18) is a cytokine that shares structural features with the interleukin-1 (IL-1) family of proteinsIL-1 family [ 14 ]. IL-18, as a unique cytokine, is involved in the activation and differentiation of multiple T cell groups [ 15 ]. IL-18 expression was increased in AP at an early stage, and was proved to be correlated with disease severity [ 16 – 18 ]. At present, the mainstream view is that IL-18 is involved in the deterioration of AP [ 16 , 19 ]. However, some scholars believe that IL-18 seems to have a protective effect on AP [ 20 ]. Mendelian randomization (MR) uses genetic variation “single nucleotide polymorphisms (SNPs)” as an instrumental variable (IV) to determine whether observed associations between risk factors and outcomes are consistent with causal effects [ 21 ]. Since these genetic variants are not usually associated with confounders, differences in outcomes between those who carry the variant and those who do not can be attributed to differences in risk factors. In this study, we used high-throughput sequencing results via machine learning to find the characteristic genes of AP. Then the expression levels, clinical correlation, biological effect and immune infiltration of IL18 in AP were analyzed comprehensively. We focused on using two-sample MR to explore the risk value of IL18 in AP. In addition, we established mice models to validate the relative mRNA and protein expression of IL18 in experimental AP. Materials and Methods Datasets and mice models The series matrix file GSE194331 of AP patients was obtained from GEO database, downloaded from https://www.ncbi.nlm.nih.gov/geo/ (September 14, 2023). The dataset was derived from whole blood samples, including 87 AP patients and 32 healthy controls (total = 119). GTEx data is downloaded from UCSC ( https://xena.ucsc.edu/ ). The GEO and GTEx databases are publicly available, which does not require institutional review board approval and informed consent. Male C57BL/6J mice weighing 20.76-22.12g were purchased from SLAC Animal Corporation (Shanghai, China). The mice were raised in an animal laboratory with suitable humidity and temperature, adequate water resources and feed. This project was approved by the Experimental Animal Ethics Committee of Shanghai First Hospital Affiliated to Shanghai Jiao Tong University (IACUC: 2023AWS208). Ten male C57BL/6J mice were randomly divided into two groups, the wild type group and the caerulein-induced AP group (5 mice per group). The AP induction protocol was 100ug/kg of caerulein injected intraperitoneally every 1 hour, repeated for 10 times. The control group was given the same amount of normal saline intraperitoneal injection for the same time period. Mice were sacrificed after3 hours of the last injection, and pancreas were collected for subsequent analysis. Differential genes and protein-protein-interaction (PPI) R-package of "edgeR" was used to analyze differentially expressed genes in whole blood between normal controls and AP patients. We took mRNA expression LogFC absolute value > 1 and false discovery rate (FDR) < 0.05 as the threshold points of differential genes. PPI analysis was performed in the STRING database ( https://www.string-db.org/ ). The connectivity diagram is drawn in R language, and the core gene is defined according to the connectivity > 5. Cytoscape presented the core genes and their interactions. Biological role and disease analysis The R packages of "clusterProfiler", "enrichplot", "org.Hs.eg.db", "ggplot2", "GSEABase" and "DOSE" was applied to analyze the function, pathway and disease of core genes. Gene Ontology (GO) functional enrichment was used to analyze the biological significance of core genes, including Biological Process (BP), Cellular Components (CC) and Molecular Function (MF). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment was used to analyze the pathways of core genes. Disease Ontology (DO) enrichment analyzed main diseases of core gene [ 22 ] [ 23 ]. The above results was considered statistically significant with P < 0.05 and P < 0.05 after adjustment. The visualization of DO is achieved through the R package of "GOplot" [ 24 ]. Machine learning In order to reduce bias, we use three different machine learning algorithms to screen potential characteristic genes. The R packages of "glmnet", "e1071" and "randomForest" completed the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression, Support Vector Machine Recursive Feature Elimination (SVM_RFE) and Random Forest (RF) respectively. Finally, the venn diagram shows the overlapping characteristic gene in AP after performing three algorithms. Expression of the characteristic gene in human tissues The expression levels of the characteristic gene in human organs were extracted from GTEx database. Then R packages of "dplyr" and "ggpubr" were used to graph the expression of the characteristic gene in various tissues of human body. Moreover, the mRNA expression of the characteristic gene in AP tissues and normal controls was also mapped. Clinical association and ROC curve We explored the clinical relevance and diagnostic sensitivity of the characteristic gene. The R-package of "ggpubr" compared the expression level of the characteristic gene under different clinical condition. The R package of "pROC" generated the receiver operating characteristic curve (ROC) and calculated the area under the curve (AUC) value to assess the specificity and sensitivity of the characteristic gene to predict AP. Function and pathway analysis of co-expressed genes We look for correlated genes that are associated with the characteristic gene in R software. The threshold value of correlated genes was set as |Pearson correlation coefficient|>0.60 and P < 0.001. The R packages of "igraph" and "reshape2" were used to visualize all co-expressed results. R packages of "clusterProfiler", "org.Hs.eg.db", "DOSE", "ggplot2", "GOplot", "R.Utls" and "pathview" were used for GO and KEGG analyses. Immune infiltration and principal component analysis (PCA) The CIBERSORT algorithm can show the relationship between the expression level of the characteristic gene and 22 types of immune cells. Boxplot exhibited the situation of immune cells in normal controls and AP tissues. PCA maps were used for dimensionality reduction to evaluate the feasibility of distinguishing AP among immune cells. Bar charts visualized the correlation between characteristic gene and immune cells. The ssGSEA algorithm is based on 29 immune gene sets (infiltration scores of 16 immune cells and activity of 13 immune-related pathways) to comprehensively quantify the relative abundance of immune cell types, pathways, functions, and checkpoints in each patient. GSVA R package was used to analyze the differences in immune function between AP patients with low- and high-expression of the characteristic gene. The R packages of “limma”, “preprocessCore”, “GSVA”, “GSEABase”, “reshape2”, “corrplot”, “ggpubr”, and “ggplot2” were used to complete the above tasks. Two-sample Mendelian randomization (MR) analysis Two-sample MR was used to investigate the causal relationship between the characteristic gene and the risk of AP, and SNP was defined as IVs. The characteristic gene information was acquired from the Genome-Wide Association Study (GWAS). We performed MR analysis of IL18 and AP. The GWAS ID was ebi-a-GCST90010141 for IL-18, and finn-b-K11_ACUTPAN for AP. MR Analysis was performed based on the R package of "TwoSampleMR" and the relationship between IL18 and AP was evaluated using inverse variance weighting (IVW). Additional sensitivity analysis was performed by MR-Egger [ 25 ]. Biological experiments Total RNA and protein from mouse pancreas was extracted by TRIzol according to the instructions from manufactory. Reverse transcription kit and SYBR kit were used for RT-qPCR (EnzyArtisan). Regarding Rplp0 as the reference gene, CT values (2 −ΔΔCT ) were calculated to exhibit the relative expression level of IL18. Primer sequences are as follows: IL18, forward: 5'-AACTTTGGCCGACTTCACTGTA-3', reverse: 5' - TATCAGTCATATCCTCGAACACAGG-3'; mouse Rplp0, forward: 5'-TTATAACCCTGAAGTGCTCGAC-3', reverse: 5'-CGCTTGTACCCATTGATGATG-3'. Besides, western blotting was performed using IL18 (Proteintech) and β-actin (Servicebio) primary antibody (1:2000 dilution) incubated at 4℃ overnight. And the second antibody of the corresponding species (mouse for IL18 and rabbit for β-actin) were incubated for 90minutes the next day at the dilutions of 1:5000 (Proteintech) and 1:10000 (Servicebio) respectively. ECL photograph showed WB results. The pancreas tissue was fixed and embedded in paraffin wax, and was sliced by 4mm. Sections were dewaxed and hydrated regularly, and then incubated with a citrate antigen retrieval solution (Beyotime Biotechnology, Shanghai, China) for 1 h. The slides were then incubated overnight with primary antibodies of IL18 (Proteintech). On one hand, fluorescent secondary antibody (Servicebio, 1:200 dilution) combined with DAPI was added the next day for tissue immunofluorescence analysis. On the other hand, half of the slides were incubated for 1 hour by biotinylated secondary antibody (Servicebio, 1:200 dilution) for IHC staining graphed by the microscope (DFC550; Leica, IL, USA). Statistics The T-test is used to analyze differences between groups for variables with a normal distribution. Otherwise, the Mann-Whitney U test is applied. Chi-square tests are used to compare quantities. The Pearson correlation method was used to analyze the correlation between two different genes. All statistical analyses were performed by R software version 4.3.1 ( https://www.r-project.org/ ). P < 0.05 was considered statistically significant. We have marked * in the results, where * means p < 0.05, ** means p < 0.01, and *** means p < 0.001. Results Differential genes and function of core gene The transcriptome of whole blood samples from patients with AP and normal controls was analyzed. A total of 1356 differential genes were obtained according to the threshold value (Supplementary Table 1). The PPI interaction network of 1356 differential genes was constructed via STRING online database (http-string-db.org) to better understand the interactions between these differential genes. And number of interactions ≥ 5 is considered as the threshold for core genes (Fig. 1 A). The cytoHubba module of Cytoscape software computed and visualized these 100 core genes (Fig. 1 B). To explore the role of these core genes in the AP process, we focused on function, pathway and disease analysis of them. The results of GO analysis are mainly positive regulation of cytokine production, defense response to bacterium and regulation of inflammatory response. This suggests that a variety of cytokines and inflammatory responses take part in the development of AP (Fig. 1 C). The pathways of core genes are mainly enriched in Tuberculosis, Inflammatory bowel disease and Staphylococcus aureus infection (Fig. 1 D). DO analysis showed that the onset of AP may be associated with diseases such as periodontal disease, arteriosclerosis, bacterial infectious disease, etc. The circle graph presented the six diseases corresponding to core genes (Fig. 1 E). The role of 100 core genes in AP was consistent with the law of previous studies and clinical evidence. They can be further screened out by machine learning. Machine learning Three machine learning methods, namely LASSO regression, SVM-RFE and RF, were used to learn core genes in pancreatitis. LASSO regression obtained 17 AP gene subsets (Fig. 2 A). The SVM-REF algorithm got 13 AP gene subsets (Fig. 2 B). The RF algorithm gained 8 AP gene subsets (Supplementary Table 2). A Venn diagram was drawn to indicate the intersection of subsets from three machine learning algorithms. Finally IL18 was identified as a characteristic gene for pancreatitis (Fig. 2 D). Expression levels and clinical value of IL18 Expression level of IL18 in all organs was depicted based on the GTEx database. The red box suggested the expression of IL18 in the pancreas (Fig. 3 A). According toGSE194331, we compared the expression level of IL18 in whole blood samples in AP and control group (Fig. 3 B). The ROC curve indicated that IL18could diagnose AP with good ability, with AUC value of 0.917 (95%CI 0.859–0.961) (Fig. 3 C). The whole blood expression of IL18 in all pathological types of AP was higher than that in normal controls (P < 0.001). The expression of IL18 increased with the severity of pancreatitis, and the expression of IL18 was higher in SAP than in mild pancreatitis (P < 0.01, Fig. 3 D). We conducted AP model in mice, HE staining showed that AP animal models were established successfully (Fig. 4 A). Furthermore, the relative mRNA expression level of IL18 in AP was significantly increased than that in wild type controls (Fig. 4 D, n = 5, P = 0.0032). At the same time, the quantitative and qualitative results of pancreatic tissue protein extracted from pancreas also showed that the expression of IL18 protein in AP group was higher than that in wild type controls (Fig. 4 B, C, E). Correlation and function of IL18 Genes with a correlation coefficient over 0.6 are considered as IL18 related genes. A total of 964 associated genes are listed in Supplementary Table 3. The corNetwork showed the correlation among the 12 genes with the largest correlation coefficient with IL18 (Fig. 5 A). GO analysis suggested that BP mainly involves activation of immune response, immune response-regulating signaling pathway and immune response-activating signaling pathway. CC is mainly secretory granule membrane, secretory granule lumen and cytoplasmic vesicle lumen. MF is mainly immune receptor activity, phosphortyrosine residue binding and oxidoreductase activity, acting on a sulfur group of donors (Fig. 5 C, details in Supplementary Table 4).KEGG showed that IL18 was mainly associated with Salmonella infection, Osteoclast differentiation, NF-kappa B signaling pathway, Th1 and Th2 cell differentiation and T cell receptor signaling pathway (Fig. 5 E, details in Supplementary Table 4). All these indicated that IL18 was closely related to immune response and status in AP. The pathway map exhibited the correlated genes with altered expression in the pathways of NF-kappa B, Th1 and Th2 cell differentiation, and T cell receptor (Fig. 5 B, D, F). Immune infiltration We used the CIBERSORT method to measure the status of 22 types of immune cells in normal controls and AP (Fig. 6 A). Differences existed with regard to 12 kinds of immune cells such as B cells naive, Macrophages, T cells CD8 and T cells CD4 memory resting. PCA results showed that AP and controls could be well distinguished based on immune cells (Fig. 6 B). IL18 was positively associated with 6 types of immune cells and negatively correlated to 4 types of immune cells (Fig. 6 C). Except Macrophages, Neutrophils and Treg cells, the other immune pathways were downregulated when the expression of IL18 was high, which were assessed via ssGSEA. Overall, most of immune cells and signaling pathways were inhibited under the condition of high expression of IL18 (Fig. 6 D). Hence we propose that IL18 works like a coin which has two sides, affecting immune cells and immune pathways in different ways. Mendelian randomization result Last but not least, we estimated the causal relationship between IL-18level and AP. Scatter plots showed the causal effect of SNP on AP: the higher IL18level was, the lower risk for AP became (Fig. 7 A).The forest map reflects the results of every SNP via wald ratio method, while the bottom red line reflects the IVW result (Fig. 7 B). We found that IL18level was associated with the risk of AP by IVW approach, with an OR of 0.908 (95% CI = 0.843–0.978, P = 0.011). The IVW heterogeneity test indicated that heterogeneity did not exist (P = 0.777). The causal effects of the funnel plot are roughly symmetrical (Fig. 7 C). Horizontal pleiotropy showed no confounding factors (P = 0.514), suggesting that IL18level was reliable to predict the risk of AP. Leave-one-out sensitivity analysis showed that there was no dominant SNP of IL18level in AP (Fig. 7 D), demonstrating significant causal association between all the calculated results of SNPs. These results elucidated that genetically determined higher IL18 levels are causally associated with lower AP incidence. Discussions AP is an inflammatory disease caused by the activation of intracellular trypsinogen due to various reasons [ 26 , 27 ]. In 2022, Maryam Nesvaderani provided the first high-throughput sequencing data of blood samples in AP patients[ 28 ]. This raw dataset is remarkable for researchers to probe for machine learning of novel markers in AP [ 29 , 30 ]. With the help of this dataset, we found there’s associations between AP and oral problems, as well as atherosclerosis. Then three machine learning algorithms were used to dig deeply into AP. And IL18 was considered to be the characteristic gene of AP. The expression of IL18 increased significantly in AP compared to normal controls, and mice model confirmed the result. ROC curve illustrated that IL18 was a good indicator for clinical diagnosis of AP. Furthermore, IL18 was involved in a variety of immune activities and immune responses. Intriguingly, MR analysis suggested that high serum levels of IL18 could be causally associated with a reduced risk of AP. Although the triggers for AP may vary, the immune response to cell damage is similar[ 31 ]. Pro-inflammatory mediators released in AP were found to be associated with the severity of inflammation[ 32 ]. IL18 was a member of the cytokine IL-1 family and was recognized as an important regulator of inflammation, immune response, and tissue damage [ 33 , 34 ]. Mechanistically, IL-18 is involved in Th1 and Th2 immune responses and activation of M2 macrophages [ 15 , 35 ]. IL-18 has been shown to induce the production of cytokines and chemokines by neutrophils [ 36 ]. Neutrophil infiltration in the SAP-damaged pancreas was significantly reduced when IL-18 was knocked out, along with reduced T cell activation [ 37 ]. Our ssGSEA showed that neutrophil and macrophage scores increased and T cell scores decreased in patients with high expression of IL18. Further lollipop chart showed that IL18 was positively correlated with T cells CD4 memory activated, and negatively correlated with T cells CD8. In line with our study, Takashi Ueda et al. proposed that IL-18 was positively correlated with CD4-positive lymphocytes [ 34 ].GO analysis also suggested that IL18 was involved in the activation and regulation of immune response. Activation of NF-κB is a classic signaling pathway in AP [ 38 , 39 ]. Early inhibition of NF-κB activation may significantly reduce the severity and lethal outcome of AP [ 40 , 41 ]. Some studies reported that IL18 co-operated with other cytokines in the activation of NF-κB [ 42 – 44 ]. In addition, IL18 was involved in Th1 and Th2 immune responses [ 15 , 45 ]. Our results showed that IL18 not only affected the NF − kappa B signaling pathway and Th1 and Th2 cell differentiation, but also influenced T cell receptor signaling pathway and B cell receptor signaling pathway. IL18 is the characteristic gene via three machine learning algorithms, indicating that it plays an important role in AP. Previous studies have shown that serum IL-18 concentration corresponded to the severity of AP [ 16 , 19 , 34 , 46 , 47 ]. Compared with healthy controls, AP patients had significantly higher serum IL-18 levels within 24 hours of symptom onset [ 16 , 17 , 34 ]. Moreover, the expression of serum IL18 was much higher in patients with SAP than that in mild AP [ 16 , 17 ]. Interestingly, IL-18 will return to normal levels if no complications occur [ 16 , 17 ]. Meanwhile, levels of circulating IL-18 would further elevated in patients with pancreatic necrosis or systemic damage on lung, kidney, heart, liver and multiple organ dysfunction syndromes [ 16 , 48 ]. Therefore, serum IL-18 concentration can be used to reflect the severity of AP. This is consistent with our clinical results. At the same time, we plotted the ROC curve with an AUC value of 0.917, suggesting that IL18 could serve as a good biomarker for AP in clinical diagnosis [ 49 ]. However, the above studies were based on serum levels, little was known about the expression of IL18 in tissues. We further established AP animal models in mice induced by caerulein, and found that mRNA and protein relative expression levels of IL18 were increased. These results indicated that the level of serum IL-18 is elevated in AP. Therefore, it is generally believed that IL18 can reflect the severity of AP. Nevertheless, whether it is destructive or protective to AP is still unified. Sendler et al. observed that IL-18 deficiency reduced the severity of AP [ 37 ]. In addition, Rau et al. found that serum IL-18 concentration usually increased before and after the occurrence of AP-related complications, suggesting that IL-18 may be involved in the aggravation of AP [ 16 ]. Ueno et al. reported that compared with AP wild-type mice, serum amylase, lipase and the numbers of acinar cells with parenchyma vacuolization were significantly increased in AP mice with IL18 knockout, and the above parameters could be improved after pretreatment with recombinant mouse IL-18. Therefore, they believe that IL-18 has a protective effect in AP [ 20 ]. Our study suggests that IL18 promoted multiple immune cells at the same time suppressed many more immune cells and pathways. But there are too many confounding factors. MR can use genetic variation as an instrumental variable to determine causality between risk factors and outcomes. Since genetic variation is not associated with confounding factors, MR Analysis has an obvious advantage in addressing the above issue. We conducted a two-sample MR Analysis of GWAS data to explore the association between IL18 and the risk of AP. The results showed that there was a causal relationship between increased IL18 level and reduced risk of AP. In conclusion, IL-18 is like a coin with two sides: it increases corresponding to disease severity meanwhile revealing as a protective factor against injury. It is convincing that we used the mice experiments and MR to validate our findings, but there are still some shortcomings. First, we used STRING database for differential genes to mine the core regulatory genes. And there might be possibilities to miss some unusual genes although STRING covered the most species and had the largest interaction information. Second, the microarray data for AP we used was based on peripheral blood sequencing, and some differences could exist when we applied mice pancreatic tissue for validation. Finally, because of the complex and dynamic regulation of the immune response of AP, there should be more evidence from different induction methods to prove our results since we only used caerulein to induce AP. Conclusion IL18 is regarded as the characteristic gene for AP after machine learning. Besides, the higher expression of IL18 is, the more severe AP becomes. In addition, IL18 is associated with a variety of immune pathways and immune cells. There is a causal relationship between IL18 and AP. MR showed that elevated expression level ofIL18 could lessen the risk of pancreatitis. In summary, we strongly believe that IL-18 is a unique target for the therapy of AP. Declarations Data Availability: The datasets generated and/or analyzed during the current study are available in the GEO (https://www.ncbi.nlm.nih.gov/geo/) and GTEx (https://xena.ucsc.edu/) online repository. Funding: This work was supported by National Natural Science Foundation of China [grant number 82370656] and National Natural Science Foundation of Shanghai [grant number 23ZR1450900]. Acknowledgments: We are very grateful to the GEO and GTEx databases for providing gene expression information for our study. Moreover, we appreciate all the mice sacrificed in the experiment. Author contributions to manuscript: Kena Zhou and Leheng Liu: Formal analysis, investigation, methodology, software, writing – original draft. Jingpiao Bao and Chuanyang Wang: Data curation, methodology, validation. Xingpeng Wang, Weiliang Jiang, Rong Wan: Conceptualization, funding acquisition, supervision, writing – review & editing. Conflict of interest: The authors declare no conflict of interest. References Gardner TB. Acute Pancreatitis. Ann Intern Med 2021; 174:ITC17-ITC32. Sternby H, Bolado F, Canaval-Zuleta HJ, Marra-Lopez C, Hernando-Alonso AI, Del-Val-Antonana A, et al. 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Interleukin-18 Amplifies Macrophage Polarization and Morphological Alteration, Leading to Excessive Angiogenesis. Front Immunol 2018; 9:334. Leung BP, Culshaw S, Gracie JA, Hunter D, Canetti CA, Campbell C, et al. A role for IL-18 in neutrophil activation. J Immunol 2001; 167:2879–86. Sendler M, van den Brandt C, Glaubitz J, Wilden A, Golchert J, Weiss FU, et al. NLRP3 Inflammasome Regulates Development of Systemic Inflammatory Response and Compensatory Anti-Inflammatory Response Syndromes in Mice With Acute Pancreatitis. Gastroenterology 2020; 158:253–269 e14. Chen X, Ji B, Han B, Ernst SA, Simeone D, Logsdon CD. NF-kappaB activation in pancreas induces pancreatic and systemic inflammatory response. Gastroenterology 2002; 122:448–57. Masamune A, Sakai Y, Yoshida M, Satoh A, Satoh K, Shimosegawa T. Lysophosphatidylcholine activates transcription factor NF-kappaB and AP-1 in AR42J cells. Dig Dis Sci 2001; 46:1871–81. Ethridge RT, Hashimoto K, Chung DH, Ehlers RA, Rajaraman S, Evers BM. Selective inhibition of NF-kappaB attenuates the severity of cerulein-induced acute pancreatitis. J Am Coll Surg 2002; 195:497–505. Satoh A, Shimosegawa T, Fujita M, Kimura K, Masamune A, Koizumi M, et al. Inhibition of nuclear factor-kappaB activation improves the survival of rats with taurocholate pancreatitis. Gut 1999; 44:253–8. Matsumoto S, Tsuji-Takayama K, Aizawa Y, Koide K, Takeuchi M, Ohta T, et al. Interleukin-18 activates NF-kappaB in murine T helper type 1 cells. Biochem Biophys Res Commun 1997; 234:454–7. Adachi O, Kawai T, Takeda K, Matsumoto M, Tsutsui H, Sakagami M, et al. Targeted disruption of the MyD88 gene results in loss of IL-1- and IL-18-mediated function. Immunity 1998; 9:143–50. Novick D, Kim SH, Fantuzzi G, Reznikov LL, Dinarello CA, Rubinstein M. Interleukin-18 binding protein: a novel modulator of the Th1 cytokine response. Immunity 1999; 10:127–36. Nakanishi K, Yoshimoto T, Tsutsui H, Okamura H. Interleukin-18 is a unique cytokine that stimulates both Th1 and Th2 responses depending on its cytokine milieu. Cytokine Growth Factor Rev 2001; 12:53–72. Endo S, Inoue Y, Fujino Y, Wakabayashi G, Inada K, Sato S. Interleukin 18 levels reflect the severity of acute pancreatitis. Res Commun Mol Pathol Pharmacol 2001; 110:285–91. Janiak A, Lesniowski B, Jasinska A, Pietruczuk M, Malecka-Panas E. Interleukin 18 as an early marker or prognostic factor in acute pancreatitis. Prz Gastroenterol 2015; 10:203–7. Perejaslov A, Chooklin S, Bihalskyy I. Implication of interleukin 18 and intercellular adhesion molecule (ICAM)-1 in acute pancreatitis. Hepatogastroenterology 2008; 55:1806–13. Kusnierz-Cabala B, Galicka-Latala D, Naskalski JW, Sieradzki J. [Usefulness of interleukin 18 determination in clinical diagnostics]. Przegl Lek 2006; 63:789–91. Supplementary Tables Supplementary Tables 1 to 4 are not available with this version. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3965868","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":274041282,"identity":"97741e10-d1cb-45af-bb48-7acfdde9f508","order_by":0,"name":"Kena Zhou","email":"","orcid":"","institution":"Shanghai First People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kena","middleName":"","lastName":"Zhou","suffix":""},{"id":274041283,"identity":"d271722f-58f0-4611-a66c-0ca678866739","order_by":1,"name":"Leheng Liu","email":"","orcid":"","institution":"Shanghai First People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Leheng","middleName":"","lastName":"Liu","suffix":""},{"id":274041284,"identity":"7ed06ef6-ce11-4636-8d21-1c47998f4bf5","order_by":2,"name":"Jingpiao Bao","email":"","orcid":"","institution":"Shanghai First People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jingpiao","middleName":"","lastName":"Bao","suffix":""},{"id":274041285,"identity":"cb038831-4792-41c9-ac8d-0a8eb91c7672","order_by":3,"name":"Chuanyang Wang","email":"","orcid":"","institution":"Shanghai First People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chuanyang","middleName":"","lastName":"Wang","suffix":""},{"id":274041286,"identity":"7fe434f5-b1c9-4aea-96a0-1530543f58a9","order_by":4,"name":"Xingpeng Wang","email":"","orcid":"","institution":"Shanghai First People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xingpeng","middleName":"","lastName":"Wang","suffix":""},{"id":274041287,"identity":"9a2c2117-3cc8-49d9-903a-7f9ecad77438","order_by":5,"name":"Weiliang Jiang","email":"","orcid":"","institution":"Shanghai First People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Weiliang","middleName":"","lastName":"Jiang","suffix":""},{"id":274041288,"identity":"3f558808-940c-4834-98c2-a1928849ac8e","order_by":6,"name":"Rong Wan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYDCCAyg8A5v6NvbGxgcf8GthbEDwCtIY+3kONxvOIF7Lh8OMM2ekt0lz4NHBd7z5+YOPew7Lm0sfPvbwi8FhZoObDxukGRjs5HQbsGuRPHPMsHHGs8OGO/vS0o1lDNLZDG4nNhgXMCQbmx3ArsXgRg5jM8+B24wbzvCYSUsYWPOAtCTPYDiQuI2AFnuoFmYJg5sHGw7zEKElEaRF8oOBs4HkDMbGZnxaQH6ZOePA/+QNZ9jSpBkM0hL4eRKbGWcY4PYLMMQefPhwIM12wxnmY5I//tgksLEff/7jQ4WdHC4tKICZB+FgIpSDAOMPIhWOglEwCkbByAIAZ6BobIyBMMkAAAAASUVORK5CYII=","orcid":"","institution":"Shanghai First People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Rong","middleName":"","lastName":"Wan","suffix":""}],"badges":[],"createdAt":"2024-02-18 04:29:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3965868/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3965868/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51559499,"identity":"2749631b-a642-4012-b22a-45a66ba71197","added_by":"auto","created_at":"2024-02-23 17:32:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1023688,"visible":true,"origin":"","legend":"\u003cp\u003eCore genes and related functions, pathways and diseases in AP.\u003c/p\u003e\n\u003cp\u003eA. Bar chart of core genes in the PPI network. The horizontal axis represents the number of regulatory channels. The vertical axis represents the core genes.\u003c/p\u003e\n\u003cp\u003eB. Visualize the core genes using Cytohubba plugin in Cytoscape software.\u003c/p\u003e\n\u003cp\u003eC. The circular scatter plot of GO enrichment to exhibit the biological process.\u003c/p\u003e\n\u003cp\u003eD. The circular scatter plot of KEGG enrichment.\u003c/p\u003e\n\u003cp\u003eE. Circos illustrates the relationship between core genes and the top 6 kinds of diseases with DO enrichment. Genes are involved in connection with the DO term through colored connecting lines. The outer ring color represents logFC.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3965868/v1/e5f1e98afcb0a05e60e062e2.png"},{"id":51559498,"identity":"ab823ea8-cfe7-4dbe-b367-77bced0a4ce0","added_by":"auto","created_at":"2024-02-23 17:32:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":160453,"visible":true,"origin":"","legend":"\u003cp\u003eIdentify potential biomarkers of AP based on machine learning algorithms.\u003c/p\u003e\n\u003cp\u003eA. The best Lambda value in LASSO algorithm corresponds to 17 genes.\u003c/p\u003e\n\u003cp\u003eB. Root mean square error (RSME) curve of 100 core genes in SVM-RFE algorithm. The blue dots represent the lowest error rate and correspond to 13 genes.\u003c/p\u003e\n\u003cp\u003eC. Screening diagnostic genes for AP by random forest algorithm.\u003c/p\u003e\n\u003cp\u003eD. Venn diagram intersects the diagnostic genes for AP obtained by LASSO, SVM-REF and RF algorithms.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3965868/v1/60bdb86df5c4436d57a11901.png"},{"id":51559500,"identity":"baebf003-7a58-42d7-b144-61c98eb31f16","added_by":"auto","created_at":"2024-02-23 17:32:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":248225,"visible":true,"origin":"","legend":"\u003cp\u003eExpression and clinical significance of IL18.\u003c/p\u003e\n\u003cp\u003eA. Expression of IL18 in various organs of normal human body. The red box illustrated the pancreas.\u003c/p\u003e\n\u003cp\u003eB. Violin plot of IL18 mRNA expression in the GEO dataset (GSE194331).\u003c/p\u003e\n\u003cp\u003eC. ROC curve to evaluate the diagnostic value of IL18 in GEO cohort.\u003c/p\u003e\n\u003cp\u003eD. Expression of IL18 in different pathological states of AP.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3965868/v1/2f59d835edc3d802f5315d69.png"},{"id":51560194,"identity":"5a15f228-3026-4082-88a8-1ffc5a829be0","added_by":"auto","created_at":"2024-02-23 17:40:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":371212,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental results indicating the expression of IL18 in normal wild type and AP.\u003c/p\u003e\n\u003cp\u003eA. HE staining of pancreas in mice model.\u003c/p\u003e\n\u003cp\u003eB. WB result of relative expression of IL18 in normal controls (n=5) and AP mice (n=5).\u003c/p\u003e\n\u003cp\u003eC. IHC staining result of expression of IL18 in normal controls and AP mice.\u003c/p\u003e\n\u003cp\u003eD. PCR result of relative expression of IL18 in normal controls (n=5) and AP mice (n=5).\u003c/p\u003e\n\u003cp\u003eE. IF result of expression of IL18 in normal controls and AP mice (IL18 in red).\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3965868/v1/b524606b4c1c4a1a7a7c3dd0.png"},{"id":51560195,"identity":"48e53abc-b920-46dc-b7e3-5870acbd5f47","added_by":"auto","created_at":"2024-02-23 17:40:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":610284,"visible":true,"origin":"","legend":"\u003cp\u003eFunction and pathway of co-expressed genes of IL18.\u003c/p\u003e\n\u003cp\u003eA. The corNetwork diagram of IL18.\u003c/p\u003e\n\u003cp\u003eB. Expression of IL18 and co-expressed genes in NF−kappa B signaling pathway.\u003c/p\u003e\n\u003cp\u003eC. Bubble map of GO enrichment analysis for IL18 co-expression genes.\u003c/p\u003e\n\u003cp\u003eD. Expression of IL18 and co-expressed genes in Th1 and Th2 cell differentiation signaling pathway.\u003c/p\u003e\n\u003cp\u003eE. Bubble map of KEGG enrichment analysis for IL18 co-expression genes.\u003c/p\u003e\n\u003cp\u003eF. Expression of IL18 and co-expressed genes in T cell receptor signaling pathway.\u003c/p\u003e\n\u003cp\u003eRed represents positive regulation, while green represents negative regulation.\u003c/p\u003e","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3965868/v1/2bae091e88d645696dac7e9b.png"},{"id":51559504,"identity":"73e4c464-f0c7-4777-a15a-cbd4a34d7543","added_by":"auto","created_at":"2024-02-23 17:32:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":250945,"visible":true,"origin":"","legend":"\u003cp\u003eAnalyses of the association between immune cells and IL18 in normal controls and AP.\u003c/p\u003e\n\u003cp\u003eA. 22 kinds of immune cells status in normal controls and AP (Blue represents controls, while red represents AP samples).\u003c/p\u003e\n\u003cp\u003eB. PCA of 22 types of immune cells, revealing differences of immune-phenotype in normal controls and AP.\u003c/p\u003e\n\u003cp\u003eC. Lollipop diagram of correlation between IL18 and 22 kinds of immune cells.\u003c/p\u003e\n\u003cp\u003eD. Comparison of ssGSEA scores between low- and high-expression of IL18.\u003c/p\u003e","description":"","filename":"OnlineFigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-3965868/v1/da301ce86ed8d923dfbf974c.png"},{"id":51559502,"identity":"4b80c594-ea46-42d0-97b0-dbb4b667b5b3","added_by":"auto","created_at":"2024-02-23 17:32:40","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":122585,"visible":true,"origin":"","legend":"\u003cp\u003eMendelian randomization study on IL18 and AP.\u003c/p\u003e\n\u003cp\u003eA. Scatter plot showing the causal effect of IL18 on the risk of AP.\u003c/p\u003e\n\u003cp\u003eB. Forest plot showing the causal effect of each SNP on the risk of AP.\u003c/p\u003e\n\u003cp\u003eC. Funnel plots to visualize overall heterogeneity of MR estimates for the effect of IL18 on AP.\u003c/p\u003e\n\u003cp\u003eD. Leave-one-out plot to visualize causal effect of IL18 on AP when leaving one SNP out.\u003c/p\u003e","description":"","filename":"OnlineFigure7.png","url":"https://assets-eu.researchsquare.com/files/rs-3965868/v1/a25b9f9043c6aac9e49a5913.png"},{"id":51829058,"identity":"f049a483-134a-42c0-93bc-ad3b2635c66b","added_by":"auto","created_at":"2024-02-29 17:40:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3273281,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3965868/v1/31f5c2ce-2701-4b24-9823-591280ff7f40.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"IL18 in acute pancreatitis: Machine learning and two-sample Mendelian randomization study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute pancreatitis (AP) is a clinically common inflammatory disease that is mild in most patients [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. About 35% of AP patients will progress to moderate severe acute pancreatitis (MSAP) or severe acute pancreatitis (SAP), conditions with poor prognosis and life-threatening [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. AP is a sophisticated disease that varies in severity and course. Early diagnosis and stratification of severity contribute to timely treatment and better prognosis [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. With the development of genomics in the last decade, our understanding of the pathophysiological mechanism of AP has gradually shifted from traditional clinical indicators to the gene era [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHigh-throughput sequencing accelerates basic research and makes deep molecular characterization of patient samples routine [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Machine learning is superior to traditional statistical models, and can develop predictive models for associations between high-throughput sequencing results and features [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. It is becoming an integral part of modern data mining and clinical diagnosis [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInterleukin-18 (IL-18) is a cytokine that shares structural features with the interleukin-1 (IL-1) family of proteinsIL-1 family [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. IL-18, as a unique cytokine, is involved in the activation and differentiation of multiple T cell groups [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. IL-18 expression was increased in AP at an early stage, and was proved to be correlated with disease severity [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. At present, the mainstream view is that IL-18 is involved in the deterioration of AP [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, some scholars believe that IL-18 seems to have a protective effect on AP [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) uses genetic variation \u0026ldquo;single nucleotide polymorphisms (SNPs)\u0026rdquo; as an instrumental variable (IV) to determine whether observed associations between risk factors and outcomes are consistent with causal effects [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Since these genetic variants are not usually associated with confounders, differences in outcomes between those who carry the variant and those who do not can be attributed to differences in risk factors.\u003c/p\u003e \u003cp\u003eIn this study, we used high-throughput sequencing results via machine learning to find the characteristic genes of AP. Then the expression levels, clinical correlation, biological effect and immune infiltration of IL18 in AP were analyzed comprehensively. We focused on using two-sample MR to explore the risk value of IL18 in AP. In addition, we established mice models to validate the relative mRNA and protein expression of IL18 in experimental AP.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDatasets and mice models\u003c/h2\u003e \u003cp\u003eThe series matrix file GSE194331 of AP patients was obtained from GEO database, downloaded from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (September 14, 2023). The dataset was derived from whole blood samples, including 87 AP patients and 32 healthy controls (total\u0026thinsp;=\u0026thinsp;119). GTEx data is downloaded from UCSC (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xena.ucsc.edu/\u003c/span\u003e\u003cspan address=\"https://xena.ucsc.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The GEO and GTEx databases are publicly available, which does not require institutional review board approval and informed consent.\u003c/p\u003e \u003cp\u003eMale C57BL/6J mice weighing 20.76-22.12g were purchased from SLAC Animal Corporation (Shanghai, China). The mice were raised in an animal laboratory with suitable humidity and temperature, adequate water resources and feed. This project was approved by the Experimental Animal Ethics Committee of Shanghai First Hospital Affiliated to Shanghai Jiao Tong University (IACUC: 2023AWS208). Ten male C57BL/6J mice were randomly divided into two groups, the wild type group and the caerulein-induced AP group (5 mice per group). The AP induction protocol was 100ug/kg of caerulein injected intraperitoneally every 1 hour, repeated for 10 times. The control group was given the same amount of normal saline intraperitoneal injection for the same time period. Mice were sacrificed after3 hours of the last injection, and pancreas were collected for subsequent analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDifferential genes and protein-protein-interaction (PPI)\u003c/h2\u003e \u003cp\u003eR-package of \"edgeR\" was used to analyze differentially expressed genes in whole blood between normal controls and AP patients. We took mRNA expression LogFC absolute value\u0026thinsp;\u0026gt;\u0026thinsp;1 and false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as the threshold points of differential genes. PPI analysis was performed in the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.string-db.org/\u003c/span\u003e\u003cspan address=\"https://www.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The connectivity diagram is drawn in R language, and the core gene is defined according to the connectivity\u0026thinsp;\u0026gt;\u0026thinsp;5. Cytoscape presented the core genes and their interactions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eBiological role and disease analysis\u003c/h2\u003e \u003cp\u003eThe R packages of \"clusterProfiler\", \"enrichplot\", \"org.Hs.eg.db\", \"ggplot2\", \"GSEABase\" and \"DOSE\" was applied to analyze the function, pathway and disease of core genes. Gene Ontology (GO) functional enrichment was used to analyze the biological significance of core genes, including Biological Process (BP), Cellular Components (CC) and Molecular Function (MF). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment was used to analyze the pathways of core genes. Disease Ontology (DO) enrichment analyzed main diseases of core gene [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The above results was considered statistically significant with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 after adjustment. The visualization of DO is achieved through the R package of \"GOplot\" [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMachine learning\u003c/h2\u003e \u003cp\u003eIn order to reduce bias, we use three different machine learning algorithms to screen potential characteristic genes. The R packages of \"glmnet\", \"e1071\" and \"randomForest\" completed the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression, Support Vector Machine Recursive Feature Elimination (SVM_RFE) and Random Forest (RF) respectively. Finally, the venn diagram shows the overlapping characteristic gene in AP after performing three algorithms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eExpression of the characteristic gene in human tissues\u003c/h2\u003e \u003cp\u003eThe expression levels of the characteristic gene in human organs were extracted from GTEx database. Then R packages of \"dplyr\" and \"ggpubr\" were used to graph the expression of the characteristic gene in various tissues of human body. Moreover, the mRNA expression of the characteristic gene in AP tissues and normal controls was also mapped.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eClinical association and ROC curve\u003c/h2\u003e \u003cp\u003eWe explored the clinical relevance and diagnostic sensitivity of the characteristic gene. The R-package of \"ggpubr\" compared the expression level of the characteristic gene under different clinical condition. The R package of \"pROC\" generated the receiver operating characteristic curve (ROC) and calculated the area under the curve (AUC) value to assess the specificity and sensitivity of the characteristic gene to predict AP.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eFunction and pathway analysis of co-expressed genes\u003c/h2\u003e \u003cp\u003eWe look for correlated genes that are associated with the characteristic gene in R software. The threshold value of correlated genes was set as |Pearson correlation coefficient|\u0026gt;0.60 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.001. The R packages of \"igraph\" and \"reshape2\" were used to visualize all co-expressed results. R packages of \"clusterProfiler\", \"org.Hs.eg.db\", \"DOSE\", \"ggplot2\", \"GOplot\", \"R.Utls\" and \"pathview\" were used for GO and KEGG analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eImmune infiltration and principal component analysis (PCA)\u003c/h2\u003e \u003cp\u003eThe CIBERSORT algorithm can show the relationship between the expression level of the characteristic gene and 22 types of immune cells. Boxplot exhibited the situation of immune cells in normal controls and AP tissues. PCA maps were used for dimensionality reduction to evaluate the feasibility of distinguishing AP among immune cells. Bar charts visualized the correlation between characteristic gene and immune cells. The ssGSEA algorithm is based on 29 immune gene sets (infiltration scores of 16 immune cells and activity of 13 immune-related pathways) to comprehensively quantify the relative abundance of immune cell types, pathways, functions, and checkpoints in each patient. GSVA R package was used to analyze the differences in immune function between AP patients with low- and high-expression of the characteristic gene. The R packages of \u0026ldquo;limma\u0026rdquo;, \u0026ldquo;preprocessCore\u0026rdquo;, \u0026ldquo;GSVA\u0026rdquo;, \u0026ldquo;GSEABase\u0026rdquo;, \u0026ldquo;reshape2\u0026rdquo;, \u0026ldquo;corrplot\u0026rdquo;, \u0026ldquo;ggpubr\u0026rdquo;, and \u0026ldquo;ggplot2\u0026rdquo; were used to complete the above tasks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eTwo-sample Mendelian randomization (MR) analysis\u003c/h2\u003e \u003cp\u003eTwo-sample MR was used to investigate the causal relationship between the characteristic gene and the risk of AP, and SNP was defined as IVs. The characteristic gene information was acquired from the Genome-Wide Association Study (GWAS). We performed MR analysis of IL18 and AP. The GWAS ID was ebi-a-GCST90010141 for IL-18, and finn-b-K11_ACUTPAN for AP. MR Analysis was performed based on the R package of \"TwoSampleMR\" and the relationship between IL18 and AP was evaluated using inverse variance weighting (IVW). Additional sensitivity analysis was performed by MR-Egger [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBiological experiments\u003c/h2\u003e \u003cp\u003e Total RNA and protein from mouse pancreas was extracted by TRIzol according to the instructions from manufactory. Reverse transcription kit and SYBR kit were used for RT-qPCR (EnzyArtisan). Regarding Rplp0 as the reference gene, CT values (2\u003csup\u003e\u0026minus;ΔΔCT\u003c/sup\u003e) were calculated to exhibit the relative expression level of IL18. Primer sequences are as follows: IL18, forward: 5'-AACTTTGGCCGACTTCACTGTA-3', reverse: 5' - TATCAGTCATATCCTCGAACACAGG-3'; mouse Rplp0, forward: 5'-TTATAACCCTGAAGTGCTCGAC-3', reverse: 5'-CGCTTGTACCCATTGATGATG-3'. Besides, western blotting was performed using IL18 (Proteintech) and β-actin (Servicebio) primary antibody (1:2000 dilution) incubated at 4℃ overnight. And the second antibody of the corresponding species (mouse for IL18 and rabbit for β-actin) were incubated for 90minutes the next day at the dilutions of 1:5000 (Proteintech) and 1:10000 (Servicebio) respectively. ECL photograph showed WB results.\u003c/p\u003e \u003cp\u003eThe pancreas tissue was fixed and embedded in paraffin wax, and was sliced by 4mm. Sections were dewaxed and hydrated regularly, and then incubated with a citrate antigen retrieval solution (Beyotime Biotechnology, Shanghai, China) for 1 h. The slides were then incubated overnight with primary antibodies of IL18 (Proteintech). On one hand, fluorescent secondary antibody (Servicebio, 1:200 dilution) combined with DAPI was added the next day for tissue immunofluorescence analysis. On the other hand, half of the slides were incubated for 1 hour by biotinylated secondary antibody (Servicebio, 1:200 dilution) for IHC staining graphed by the microscope (DFC550; Leica, IL, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistics\u003c/h2\u003e \u003cp\u003eThe T-test is used to analyze differences between groups for variables with a normal distribution. Otherwise, the Mann-Whitney U test is applied. Chi-square tests are used to compare quantities. The Pearson correlation method was used to analyze the correlation between two different genes. All statistical analyses were performed by R software version 4.3.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. We have marked * in the results, where * means p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** means p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and *** means p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eDifferential genes and function of core gene\u003c/h2\u003e\n \u003cp\u003eThe transcriptome of whole blood samples from patients with AP and normal controls was analyzed. A total of 1356 differential genes were obtained according to the threshold value (Supplementary Table 1). The PPI interaction network of 1356 differential genes was constructed via STRING online database (http-string-db.org) to better understand the interactions between these differential genes. And number of interactions\u0026thinsp;\u0026ge;\u0026thinsp;5 is considered as the threshold for core genes (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). The cytoHubba module of Cytoscape software computed and visualized these 100 core genes (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e\n \u003cp\u003eTo explore the role of these core genes in the AP process, we focused on function, pathway and disease analysis of them. The results of GO analysis are mainly positive regulation of cytokine production, defense response to bacterium and regulation of inflammatory response. This suggests that a variety of cytokines and inflammatory responses take part in the development of AP (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC). The pathways of core genes are mainly enriched in Tuberculosis, Inflammatory bowel disease and Staphylococcus aureus infection (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD). DO analysis showed that the onset of AP may be associated with diseases such as periodontal disease, arteriosclerosis, bacterial infectious disease, etc. The circle graph presented the six diseases corresponding to core genes (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE). The role of 100 core genes in AP was consistent with the law of previous studies and clinical evidence. They can be further screened out by machine learning.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eMachine learning\u003c/h2\u003e\n \u003cp\u003eThree machine learning methods, namely LASSO regression, SVM-RFE and RF, were used to learn core genes in pancreatitis. LASSO regression obtained 17 AP gene subsets (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). The SVM-REF algorithm got 13 AP gene subsets (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). The RF algorithm gained 8 AP gene subsets (Supplementary Table 2). A Venn diagram was drawn to indicate the intersection of subsets from three machine learning algorithms. Finally IL18 was identified as a characteristic gene for pancreatitis (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eExpression levels and clinical value of IL18\u003c/h2\u003e\n \u003cp\u003eExpression level of IL18 in all organs was depicted based on the GTEx database. The red box suggested the expression of IL18 in the pancreas (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). According toGSE194331, we compared the expression level of IL18 in whole blood samples in AP and control group (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). The ROC curve indicated that IL18could diagnose AP with good ability, with AUC value of 0.917 (95%CI 0.859\u0026ndash;0.961) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC). The whole blood expression of IL18 in all pathological types of AP was higher than that in normal controls (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The expression of IL18 increased with the severity of pancreatitis, and the expression of IL18 was higher in SAP than in mild pancreatitis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e\n \u003cp\u003eWe conducted AP model in mice, HE staining showed that AP animal models were established successfully (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). Furthermore, the relative mRNA expression level of IL18 in AP was significantly increased than that in wild type controls (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD, n\u0026thinsp;=\u0026thinsp;5, P\u0026thinsp;=\u0026thinsp;0.0032). At the same time, the quantitative and qualitative results of pancreatic tissue protein extracted from pancreas also showed that the expression of IL18 protein in AP group was higher than that in wild type controls (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB, C, E).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eCorrelation and function of IL18\u003c/h2\u003e\n \u003cp\u003eGenes with a correlation coefficient over 0.6 are considered as IL18 related genes. A total of 964 associated genes are listed in Supplementary Table 3. The corNetwork showed the correlation among the 12 genes with the largest correlation coefficient with IL18 (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA). GO analysis suggested that BP mainly involves activation of immune response, immune response-regulating signaling pathway and immune response-activating signaling pathway. CC is mainly secretory granule membrane, secretory granule lumen and cytoplasmic vesicle lumen. MF is mainly immune receptor activity, phosphortyrosine residue binding and oxidoreductase activity, acting on a sulfur group of donors (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC, details in Supplementary Table 4).KEGG showed that IL18 was mainly associated with Salmonella infection, Osteoclast differentiation, NF-kappa B signaling pathway, Th1 and Th2 cell differentiation and T cell receptor signaling pathway (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eE, details in Supplementary Table 4). All these indicated that IL18 was closely related to immune response and status in AP. The pathway map exhibited the correlated genes with altered expression in the pathways of NF-kappa B, Th1 and Th2 cell differentiation, and T cell receptor (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB, D, F).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eImmune infiltration\u003c/h2\u003e\n \u003cp\u003eWe used the CIBERSORT method to measure the status of 22 types of immune cells in normal controls and AP (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA). Differences existed with regard to 12 kinds of immune cells such as B cells naive, Macrophages, T cells CD8 and T cells CD4 memory resting. PCA results showed that AP and controls could be well distinguished based on immune cells (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB). IL18 was positively associated with 6 types of immune cells and negatively correlated to 4 types of immune cells (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC). Except Macrophages, Neutrophils and Treg cells, the other immune pathways were downregulated when the expression of IL18 was high, which were assessed via ssGSEA. Overall, most of immune cells and signaling pathways were inhibited under the condition of high expression of IL18 (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eD). Hence we propose that IL18 works like a coin which has two sides, affecting immune cells and immune pathways in different ways.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003eMendelian randomization result\u003c/h2\u003e\n \u003cp\u003eLast but not least, we estimated the causal relationship between IL-18level and AP. Scatter plots showed the causal effect of SNP on AP: the higher IL18level was, the lower risk for AP became (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA).The forest map reflects the results of every SNP via wald ratio method, while the bottom red line reflects the IVW result (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB). We found that IL18level was associated with the risk of AP by IVW approach, with an OR of 0.908 (95% CI\u0026thinsp;=\u0026thinsp;0.843\u0026ndash;0.978, P\u0026thinsp;=\u0026thinsp;0.011). The IVW heterogeneity test indicated that heterogeneity did not exist (P\u0026thinsp;=\u0026thinsp;0.777). The causal effects of the funnel plot are roughly symmetrical (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eC). Horizontal pleiotropy showed no confounding factors (P\u0026thinsp;=\u0026thinsp;0.514), suggesting that IL18level was reliable to predict the risk of AP. Leave-one-out sensitivity analysis showed that there was no dominant SNP of IL18level in AP (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eD), demonstrating significant causal association between all the calculated results of SNPs. These results elucidated that genetically determined higher IL18 levels are causally associated with lower AP incidence.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussions","content":"\u003cp\u003eAP is an inflammatory disease caused by the activation of intracellular trypsinogen due to various reasons [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In 2022, Maryam Nesvaderani provided the first high-throughput sequencing data of blood samples in AP patients[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This raw dataset is remarkable for researchers to probe for machine learning of novel markers in AP [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. With the help of this dataset, we found there\u0026rsquo;s associations between AP and oral problems, as well as atherosclerosis. Then three machine learning algorithms were used to dig deeply into AP. And IL18 was considered to be the characteristic gene of AP. The expression of IL18 increased significantly in AP compared to normal controls, and mice model confirmed the result. ROC curve illustrated that IL18 was a good indicator for clinical diagnosis of AP. Furthermore, IL18 was involved in a variety of immune activities and immune responses. Intriguingly, MR analysis suggested that high serum levels of IL18 could be causally associated with a reduced risk of AP.\u003c/p\u003e \u003cp\u003eAlthough the triggers for AP may vary, the immune response to cell damage is similar[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Pro-inflammatory mediators released in AP were found to be associated with the severity of inflammation[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. IL18 was a member of the cytokine IL-1 family and was recognized as an important regulator of inflammation, immune response, and tissue damage [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Mechanistically, IL-18 is involved in Th1 and Th2 immune responses and activation of M2 macrophages [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. IL-18 has been shown to induce the production of cytokines and chemokines by neutrophils [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Neutrophil infiltration in the SAP-damaged pancreas was significantly reduced when IL-18 was knocked out, along with reduced T cell activation [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Our ssGSEA showed that neutrophil and macrophage scores increased and T cell scores decreased in patients with high expression of IL18. Further lollipop chart showed that IL18 was positively correlated with T cells CD4 memory activated, and negatively correlated with T cells CD8. In line with our study, Takashi Ueda et al. proposed that IL-18 was positively correlated with CD4-positive lymphocytes [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].GO analysis also suggested that IL18 was involved in the activation and regulation of immune response. Activation of NF-κB is a classic signaling pathway in AP [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Early inhibition of NF-κB activation may significantly reduce the severity and lethal outcome of AP [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Some studies reported that IL18 co-operated with other cytokines in the activation of NF-κB [\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In addition, IL18 was involved in Th1 and Th2 immune responses [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Our results showed that IL18 not only affected the NF\u0026thinsp;\u0026minus;\u0026thinsp;kappa B signaling pathway and Th1 and Th2 cell differentiation, but also influenced T cell receptor signaling pathway and B cell receptor signaling pathway.\u003c/p\u003e \u003cp\u003eIL18 is the characteristic gene via three machine learning algorithms, indicating that it plays an important role in AP. Previous studies have shown that serum IL-18 concentration corresponded to the severity of AP [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Compared with healthy controls, AP patients had significantly higher serum IL-18 levels within 24 hours of symptom onset [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Moreover, the expression of serum IL18 was much higher in patients with SAP than that in mild AP [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Interestingly, IL-18 will return to normal levels if no complications occur [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Meanwhile, levels of circulating IL-18 would further elevated in patients with pancreatic necrosis or systemic damage on lung, kidney, heart, liver and multiple organ dysfunction syndromes [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Therefore, serum IL-18 concentration can be used to reflect the severity of AP. This is consistent with our clinical results. At the same time, we plotted the ROC curve with an AUC value of 0.917, suggesting that IL18 could serve as a good biomarker for AP in clinical diagnosis [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. However, the above studies were based on serum levels, little was known about the expression of IL18 in tissues. We further established AP animal models in mice induced by caerulein, and found that mRNA and protein relative expression levels of IL18 were increased. These results indicated that the level of serum IL-18 is elevated in AP.\u003c/p\u003e \u003cp\u003eTherefore, it is generally believed that IL18 can reflect the severity of AP. Nevertheless, whether it is destructive or protective to AP is still unified. Sendler et al. observed that IL-18 deficiency reduced the severity of AP [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In addition, Rau et al. found that serum IL-18 concentration usually increased before and after the occurrence of AP-related complications, suggesting that IL-18 may be involved in the aggravation of AP [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Ueno et al. reported that compared with AP wild-type mice, serum amylase, lipase and the numbers of acinar cells with parenchyma vacuolization were significantly increased in AP mice with IL18 knockout, and the above parameters could be improved after pretreatment with recombinant mouse IL-18. Therefore, they believe that IL-18 has a protective effect in AP [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Our study suggests that IL18 promoted multiple immune cells at the same time suppressed many more immune cells and pathways. But there are too many confounding factors. MR can use genetic variation as an instrumental variable to determine causality between risk factors and outcomes. Since genetic variation is not associated with confounding factors, MR Analysis has an obvious advantage in addressing the above issue. We conducted a two-sample MR Analysis of GWAS data to explore the association between IL18 and the risk of AP. The results showed that there was a causal relationship between increased IL18 level and reduced risk of AP. In conclusion, IL-18 is like a coin with two sides: it increases corresponding to disease severity meanwhile revealing as a protective factor against injury.\u003c/p\u003e \u003cp\u003eIt is convincing that we used the mice experiments and MR to validate our findings, but there are still some shortcomings. First, we used STRING database for differential genes to mine the core regulatory genes. And there might be possibilities to miss some unusual genes although STRING covered the most species and had the largest interaction information. Second, the microarray data for AP we used was based on peripheral blood sequencing, and some differences could exist when we applied mice pancreatic tissue for validation. Finally, because of the complex and dynamic regulation of the immune response of AP, there should be more evidence from different induction methods to prove our results since we only used caerulein to induce AP.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIL18 is regarded as the characteristic gene for AP after machine learning. Besides, the higher expression of IL18 is, the more severe AP becomes. In addition, IL18 is associated with a variety of immune pathways and immune cells. There is a causal relationship between IL18 and AP. MR showed that elevated expression level ofIL18 could lessen the risk of pancreatitis. In summary, we strongly believe that IL-18 is a unique target for the therapy of AP.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability:\u0026nbsp;\u003c/strong\u003eThe datasets generated and/or analyzed during the current study are available in the GEO (https://www.ncbi.nlm.nih.gov/geo/) and GTEx (https://xena.ucsc.edu/) online repository.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by National Natural Science Foundation of China [grant number 82370656] and National Natural Science Foundation of Shanghai [grant number 23ZR1450900].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eWe are very grateful to the GEO and GTEx databases for providing gene expression information for our study. Moreover, we appreciate all the mice sacrificed in the experiment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions to manuscript:\u0026nbsp;\u003c/strong\u003eKena Zhou and Leheng Liu: Formal analysis, investigation, methodology, software, writing \u0026ndash; original draft. Jingpiao Bao and Chuanyang Wang: Data curation, methodology, validation. Xingpeng Wang, Weiliang Jiang, Rong Wan: Conceptualization, funding acquisition, supervision, writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGardner TB. Acute Pancreatitis. Ann Intern Med 2021; 174:ITC17-ITC32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSternby H, Bolado F, Canaval-Zuleta HJ, Marra-Lopez C, Hernando-Alonso AI, Del-Val-Antonana A, et al. 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Interleukin 18 levels reflect the severity of acute pancreatitis. Res Commun Mol Pathol Pharmacol 2001; 110:285\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJaniak A, Lesniowski B, Jasinska A, Pietruczuk M, Malecka-Panas E. Interleukin 18 as an early marker or prognostic factor in acute pancreatitis. Prz Gastroenterol 2015; 10:203\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerejaslov A, Chooklin S, Bihalskyy I. Implication of interleukin 18 and intercellular adhesion molecule (ICAM)-1 in acute pancreatitis. Hepatogastroenterology 2008; 55:1806\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKusnierz-Cabala B, Galicka-Latala D, Naskalski JW, Sieradzki J. [Usefulness of interleukin 18 determination in clinical diagnostics]. Przegl Lek 2006; 63:789\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Supplementary Tables","content":"\u003cp\u003eSupplementary Tables 1 to 4 are not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"IL18, acute pancreatitis, machine learning, Mendelian randomization, immune responses","lastPublishedDoi":"10.21203/rs.3.rs-3965868/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3965868/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eAcute pancreatitis is a common disease whose treatment is limited to symptomatic support, thus finding effective biomarkers is of great significance for early diagnosis and therapy.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eBioinformatics and machine learning were applied to evaluate the expression, clinical features, biological function and immunological effects of the characteristic genes in AP. Meanwhile, AP mice models were constructed to verify the results in vivo. Finally, Mendelian randomization studies were performed to determine the causal relationship between IL-18 and AP through genome-wide association studies.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 100 core genes were obtained via differential analysis and PPI interaction network. IL18 was identified as the characteristic gene for AP by machine learning through three algorithmic. The expression of IL18 was increased significantly in AP (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The AUC value of IL18 in the diagnosis of AP was 0.917, exhibiting high clinical value. Moreover, IL18 was associated with various immune cells involved in the progression of AP. Through inverse variance weighting (IVW), we found that the OR for IL18 and AP was 0.908 (95%CI\u0026thinsp;=\u0026thinsp;0.843\u0026ndash;0.978, p\u0026thinsp;=\u0026thinsp;0.011).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIL18 is a pivotal biomarker predicting the clinical prognosis and immune response in AP, which is proved to serve as a protective factor.\u003c/p\u003e","manuscriptTitle":"IL18 in acute pancreatitis: Machine learning and two-sample Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-23 17:32:34","doi":"10.21203/rs.3.rs-3965868/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ece232fb-1184-4d55-986e-677072e265d0","owner":[],"postedDate":"February 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-29T17:39:50+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-23 17:32:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3965868","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3965868","identity":"rs-3965868","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}