Bioinformatics Analysis Identifies Significant Ferroptosis Genes and LncRNA-miRNA-mRNA Network in the Pathogenesis of Acute Pancreatitis

Bioinformatics Analysis Identifies Significant Ferroptosis Genes and LncRNA-miRNA-mRNA Network in the Pathogenesis of Acute Pancreatitis | 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 Bioinformatics Analysis Identifies Significant Ferroptosis Genes and LncRNA-miRNA-mRNA Network in the Pathogenesis of Acute Pancreatitis Xu Yan, Tianjiao Lin, Qingyun Zhu, Fei Tian, Yushi Zhang, Xinting Pan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1627969/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 Objectives The present study aims to identify the underlying mechanisms of ferroptosis and lncRNA-miRNA-mRNA network in AP by bioinformatics tools. Methods The ferroptosis genes interacted with data obtained from the Gene Expression Omnibus to identify differentially expressed genes (DEGs). Gene ontology, KEGG pathway enrichment analysis, protein-protein interaction network construction, hub genes association analysis, and transcription factor prediction were used to select and analyze the differentially expressed genes. lncRNA-miRNA-mRNA interaction network were constructed by integrating the lncRNA-miRNA pairs and miRNA-mRNA pairs. Results We identified 38 differentially expressed genes from the datasets. The function enrichment and pathway enrichment analysis of the DEGs were mostly implicated in stress response and the MAPK signaling pathway. The lncRNA-miRNA-mRNA network contained 145 lncRNA nodes, 4 miRNA nodes, 18 mRNA nodes and 235 edges. SQSTM1, KRAS, NFE2L2, HMOX1, SLC7A11, EGFR and ATF3 were identified as hub DEGs, and hub gene-related main TF MYC was discovered to have critical positive associations with these hub genes in pancreatic tissues. Conclusion The results of this study indicated that hub genes and the lncRNA-miRNA-mRNA network may play an important role in the mechanisms of ferroptosis in AP and provide treatment targets for AP. Acute pancreatitis Noncoding RNA Bioinformatical analysis Ferroptosis Differentially expressed genes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Acute Pancreatitis (AP) is one of the most common acute disease discharge diagnoses, with both local and systemic complications [ 1 – 3 ]. AP is the most frequent discharge diagnosis in acute abdominal disease requiring hospital admission with severe complications and high mortality [ 4 , 5 ]. The key pathologic response of AP is cell death and inflammation, while necrosis and apoptosis are the most studied cell death type in AP [ 6 ]. According to a prior study, programmed cell death-like processes including autophagy, pyroptosis and necroptosis may be a favorable response to acinar cell [ 7 ]. While ferroptosis is considered a type of programmed cell death, and it is well established that ferroptosis exists in pancreatitis [ 8 ], however, the mechanism of ferroptosis in AP remains unknown. Ferroptosis is a very new type of cell death that has only recently been found and is characterized as lipid peroxidation by reactive oxygen species in iron-dependent [ 9 ]. Ferroptosis has been linked to inflammation in a growing number of studies, while GPX4 has been identified as an anti-inflammatory factor when activated [ 10 ]. Ferroptosis is thought to be involved in the therapy of AP, according to recent reports, including alleviating AP-associated lung injury [ 11 ], kidney injury [ 12 ] and intestinal barrier injury [ 13 ]. However, many ferroptosis genes related to AP have not yet to be discovered. We discovered DEGs in AP and normal pancreatic tissues in this study, then intersected them with the ferroptosis dataset to get ferroptosis DEGs based on bioinformation. Meanwhile, we explore the gene-related biological functions and pathways as well as protein-protein interaction and lncRNA-miRNA-mRNA network. The findings of this research will improve our understanding of ferroptosis following AP and bring new ideas for AP clinical diagnosis and treatment. Materials And Methods Microarray Data Information Gene Expression Omnibus (GEO), a public internet database, provided the relevant gene profiles. AP was screened as the keyword to discover the associated title, an mRNA database, GSE109227 and the details of the database were finally obtained. This dataset contains 11 samples, including five wild-type mice which were given sodium chloride as control and six mice were given caerulein intraperitoneally to induce experimental AP; pancreatic tissues were obtained from these samples and were labeled from GSM2935589 to GSM2935599. We also obtained a dataset from the Ferroptosis Database, including 259 genes. These microarray data are provided by public databases thus the approval of patients and the ethical committee is not required. Differential Expression Analysis GSE109227 raw data were obtained from the GEO database and analyzed using GEO2R, an online analysis tool. GEO2R was used to compare the expression profiles of AP and control groups of samples to find DEGs using |log(fold-change)| > 1.5, and adjusted P-value <0.05 as the criteria. The genes that we obtained from Ferroptosis Database were interested with GSE109227 to identify ferroptosis DEGs. The internet application Venny2.1 was used to construct a Venn graphic of DEGs, and a heatmap of DEGs was created using Networkanalyst, which is an analytics tool for supporting integrative gene expression analysis of data utilizing statistical, visual, and network-based methodologies [14]. Gene Functional Enrichment Analysis of DEGs We utilized DAVID 6.8, a database that provides systematic, complete biological data, to predict DEGs' functional annotation and pathway enrichment analysis [15]. DEGs enrichment results of Gene Ontology (GO) including molecular functions (MFs), biological processes (BPs), and cellular components (CCs) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were obtained using a P-value < 0.05 as the enrichment terms threshold. The function annotation of DEGs by the Metascape website was performed using genes that were overlapped in GSE109227 and the ferroptosis dataset. Construction of DEG PPI Networks and Significant Module Screening STRING is an online database that may be used to find out how a set of proteins interact, in this study STRING was utilized to display protein-protein interactions and explore functional relationships between DEG-encoded proteins. Subsequently, with interaction scores >0.4, we obtained network visualization by Cytoscape. Following that, the PPI network's major submodules and hub genes were screened using the analysis program MCODE. Degree cutoff=2, node score cutoff=0.2, and K-core=2 were the criteria. Construction of Gene-miRNA Interaction Pairs The previous study showed that several miRNAs exert contribution in the acinar cell inflammation and death. Meanwhile miR-22-3p, miR-29a-3p, miR-135a-5p and miR-148b-3p have been considered playing important role in the acinar cell death [16]. Hence, we selected miR-22-3p, miR-29a-3p, miR-135a-5p and miR-148b-3p as potential miRNAs. We used ENCORI which contains 6 databases (PicTar, Targetscan, miRanda, microT, miRmap, PITA) to predicted the target mRNA of the potential miRNAs. The selected mRNAs were incorporated with the DEG genes to identify the common genes, and these genes were used to build miRNA-mRNA pairs. Construction of lncRNA-miRNA-mRNA interactions The ENCORI database was used to predict the target lncRNA from the four selected miRNAs and to simultaneously identify the lncRNA-miRNA pairs. A preliminary lncRNA-miRNA-mRNA network was constructed by deleting the nodes that cannot match the axis and integrating the miRNA-mRNA pairs and lncRNA-miRNA pairs. The we utilized Cytoscape to visualize the lncRNA-miRNA-mRNA network. Analysis of Transcriptional Factors (TFs) of Hub Genes The hub gene-related transcriptional factors were predicted by Networkanalyst. The NetworkAnalyst was used to enroll a list of hub genes, which was then processed step by step until the gene-related TFs and TF-gene interaction pairs were displayed. The associations between hub genes and anticipated core TFs were then assessed using GEPIA, an online website to provide primary interactive and customizable functions [17]. GEPIA used Pearson, Spearman, and Kendall to identify pair-wise gene expression correlation analyses for specified sets of TCGA and/or GTEx expression data. Results Identification of DEGs in Acute Pancreatitis and Ferroptosis The GEO database provided the raw data and we extracted DEGs by comparing AP and control group samples. Subsequently, we intersected 259 genes obtained from the Ferroptosis Database with DEGs of GSE109227 to identify ferroptosis DEGs. It showed one downregulated gene and 37 upregulated genes ( Table 1 ). Figure 1 depicts a heat map, volcano plots, and a DEG Venn diagram. In addition, we divided these DEGs into three groups: ferroptosis driver, ferroptosis suppressor, and ferroptosis marker ( Table 2 ). TABLE1 Acute pancreatitis genes are differentially expressed in ferroptosis Gene.symbol P.Value logFC Gene.title ID Dusp1 9.79E-11 4.340851 dual specificity phosphatase 1 10449284 Txnrd1 1.41E-06 2.213799 thioredoxin reductase 1 10365260 Srxn1 4.78E-08 3.658947 sulfiredoxin 1 homolog (S. cerevisiae) 10477061 Chac1 1.49E-06 2.379898 ChaC, cation transport regulator 1 10474972 Slc7a11 0.000206 2.249066 solute carrier family 7 (cationic amino acid transporter, y+ system), member 11 10498024 Ddit4 1.29E-07 2.639451 DNA-damage-inducible transcript 4 10369290 Sesn2 4.77E-08 2.452913 sestrin 2 10516932 Txnip 0.000209 1.636378 thioredoxin interacting protein 10494428 Atf3 9.52E-10 4.190948 activating transcription factor 3 10361091 Slc3a2 2.1E-08 1.642015 solute carrier family 3 (activators of dibasic and neutral amino acid transport), member 2 10465772 Trib3 1.19E-07 2.416594 tribbles pseudokinase 3 10488608 Cebpg 9.01E-09 1.68513 CCAAT/enhancer binding protein (C/EBP), gamma 10562416 Rela 1.23E-09 2.837673 v-rel reticuloendotheliosis viral oncogene homolog A (avian) 10460631 Hmox1 2.71E-09 2.970025 heme oxygenase 1 10572897 Hspb1 1.13E-08 3.11411 heat shock protein 1 10408928 Nfe2l2 1.08E-07 1.690716 nuclear factor, erythroid derived 2, like 2 10483809 Map3k5 1.93E-08 2.27558 mitogen-activated protein kinase kinase kinase 5 10361926 Slc2a1 6.37E-06 2.168648 solute carrier family 2 (facilitated glucose transporter), member 1 10507594 Capg 5.84E-08 1.852655 capping protein (actin filament), gelsolin-like 10539135 Gclc 2.11E-09 4.284143 glutamate-cysteine ligase, catalytic subunit 10587266 Sqstm1 3.86E-07 2.771646 sequestosome 1 10385572 Cd44 9.79E-12 3.501365 CD44 antigen 10485405 Jun 1.26E-06 1.687334 jun proto-oncogene 10514466 Plin2 6.4E-09 3.393173 perilipin 2 10514221 Gch1 8.05E-06 1.573587 GTP cyclohydrolase 1 10419288 Pgd 3.29E-05 1.63238 phosphogluconate dehydrogenase 10518570 Acsl4 3.84E-09 1.527963 acyl-CoA synthetase long-chain family member 4 10607089 Nras 9.04E-09 1.766424 neuroblastoma ras oncogene 10494857 Kras 3.63E-09 2.608895 Kirsten rat sarcoma viral oncogene homolog 10549256 Slc38a1 2.56E-08 3.663981 solute carrier family 38, member 1 10431874 Got1 4.71E-09 2.403565 glutamic-oxaloacetic transaminase 1, soluble 10467842 Map1lc3a 2.41E-06 1.708451 microtubule-associated protein 1 light chain 3 alpha 10477637 Wipi2 1.3E-08 1.907656 WD repeat domain, phosphoinositide interacting 2 10527133 Sat1 2.07E-11 3.020738 spermidine/spermine N1-acetyl transferase 1 10607467 Egfr 3.31E-10 3.350486 epidermal growth factor receptor 10374366 Prkaa1 4.21E-08 1.917747 protein kinase, AMP-activated, alpha 1 catalytic subunit 10422707 Ano6 4.42E-10 2.274626 anoctamin 6 10426479 Bnip3 2.44E-06 -1.55474 BCL2/adenovirus E1B interacting protein 3 10414269 TABLE 2 The ferroptosis differentially expressed genes were divided into three parts Suppressor Diver Marker Gclc, Sqstm1, Cd44, Jun, Plin2, Gch1 Pgd, Acsl4, Nras, Kras, Slc38a1, Got1, Map1lc3a, Wipi2, Sat1, Egfr, Prkaa1, Ano6 Dusp1, Txnrd1, Srxn1, Chac1, Slc7a11, Ddit4, Sesn2, Txnip, Atf3, Slc3a2, Trib3, Cebpg, Rela, Hmox1, Hspb1, Nfe2l2, Map3k5, Slc2a1, Capg, Bnip3 Functional Enrichment and Pathway Analysis of the Ferroptosis DEGs First, the online software DAVID was used to detect functional enrichment and pathway analysis to further clarify the functions of the DEGs in acute pancreatitis. Functional enrichment analysis of GO terms contains BP, CC and MF three categories. The P-value was indicated by the color of bubbles, and the size of bubbles, which had a significant positive relationship with the number of DEGs engaged in this term, indicated the number of DEGs contained in the term ( Figure 2 ). Cellular response to hydrogen peroxide, oxidative stress, apoptotic process, negative regulation of the apoptotic process, positive regulation of the apoptotic process, and so on were among the GO terms in the BP category. The GO terms in the CC category were mainly enriched in the cytosol, cytoplasm, macromolecular complex, etc. The DEGs were mostly enriched in MF terms such as identical protein binding, protein homodimerization activity, protein kinase binding, and ubiquitin protein ligase binding. The KEGG pathway analysis was conducted, containing MAPK signaling pathway, autophagy, ferroptosis pathway and fluid shear stress and atherosclerosis and so on. Second, we submitted the related DEGs to Metascape. The biological process was considerably enriched in response to oxidative stress, cellular response to famine, and positive control of cell death, according to the results of the enrichment pathway and analysis. The MAPK signaling pathway, ferroptosis, oxidative stress and redox pathway, and oxidative stress response were all significantly activated in biological pathways ( Figure 3 ). Finally, the MAPK signaling pathway was identified as the most important biological pathway involved in both DAVID and Metascape analyses. Protein-Protein Interaction Network Construction of DEGs To further invested in the potential relationships between DEGs, we uploaded them to STRING online database. Finally, a PPI network of the associated DEGs was created, with 35 nodes and 123 edges with pairs combined score >0.4 ( Figure 4 ). The genes are represented by the nodes in the network, while the edges reflect the relationships between them. MCODE, a Cytoscape program, was utilized to select the submodule of significance, and the result showed a submodule score of 6.9, containing 12 nodes and 38 edges. Subsequently, we also calculated the degree of those nodes. In this study, we selected 8 nodes with degree ≥10 as criteria, including Jun (degree=15); Sqstm1, Kras, Nfe2l2 and Hmox1 (degree=14); Slc7a11 (degree=12); Egfr (degree=11); Atf3 (degree=10). Except for Jun, 7 of the genes were contained in the submodule and they were all up-regulated in AP samples. We identified these 7 genes as hub genes. Additionally, the hub genes were uploaded to Metascape for functional analysis, and these hub genes were shown to be primarily involved in oxidative stress response, oxidative stress response, and oxidative stress response, according to the results ( Figure 5 ). Construction of Gene-Related miRNA pairs The database ENCORI predicted a total of 19619 targets in 6 databases ( Figure 6 ). The DEG genes of GSE109227 and Ferroptosis Database were integrated with the targeted genes, and 26 miRNA-mRNA pairs were identified. There were 7 mRNAs of miR-148b-3p, 15 mRNAs of miR-22-3p, 1 mRNA of miR-29a-3p, and 3 mRNAs of miR-135a-5p. LncRNA-miRNA-mRNA Network Analysis We used the ENCORI database to identify the potential lncRNAs of the selected miRNAs, and 208 lncRNA-miRNA pairs were obtained. The lncRNA-miRNA-mRNA network was shown in Figure 7 , which contained 145 lncRNA nodes, 4 miRNA nodes, 18 mRNA nodes and 235 edges. Transcriptional Factor Regulation Network Analysis of Hub Genes To determine how hub genes are transcribed and how transcription factors affect their expression, we used Networkanalyst to build a gene-TFs regulation network ( Figure 8 ). The network includes 27 TFs, in addition to the hub genes, for a total of 73 gene-TF interaction pairings. The regulation network of gene-TFs shows that transcriptional regulators were substantially enriched in the majority of the hub genes. We considered MYC to be the key TF that regulates the majority of the hub genes: Atf3, Hmox1, Sqstm1 and Nfe2l2. Other TFs like UBTF, NRF1, CDH1, ETS1, JUND and HCFC1 were likewise thought to be a key TF in the regulation among most hub genes. Hub Genes and Core TFs Correlation Analysis As a result of the finding that MYC has a critical regulatory relationship with seven hub genes, we used GEPIA to look for hub genes and projected core TFs correlation. The result of the correlation analysis between MYC and hub genes: SQSTM1, KRAS, NFE2L2, HMOX1, SLC7A11, EGFR and ATF3 are shown in Figure 9 . The non-log-scale axis was utilized for calculation, and the log-scale axis was used for visualization. We finally identified positive correlations of MYC and hub genes with criteria of P value less than 0.05. Discussion The development of AP is influenced by multiple factors including alcohol, gallstones and other factors. Increasing evidence suggests that ferroptosis may play a role in the development of AP. Fan's research has shown that ferroptosis play a crucial role in AP, while we can inhibit the activity of GPX4 to mediate the ferroptosis finally mediate AP and associated lung injury [ 11 ]. The intestinal barrier injury and acute kidney injury induced by SAP have also been related with ferroptosis, according to earlier studies [ 13 , 12 ]. Ferroptosis is a new type of nonapoptotic programmed cell death connected to lipid peroxidation and reactive oxygen species in the presence of iron [ 18 , 19 ]. The accumulation of lipids within the cell ROS is a characteristic of ferroptosis, and it is thought to be the outcome of lipid oxidation, which leads to cell membrane damage and death [ 20 ]. However, extensive validations are needed to improve the understanding of ferroptosis in the pathogenesis of AP. The main genes involved in ferroptosis and acute pancreatitis caused by caerulein injection were discovered in this investigation. The datasets GSE109227 and FerrDb provided 38 DEGs for our research. In addition, the DEGs' function enrichment and pathway enrichment analyses showed that these genes are mostly implicated in stress response and the MAPK signaling pathway. The ceRNA network was constructed to indicate the underlying connection between non-coding RNA and ferroptosis in AP. Hub DEGs hub gene-related major TFs were discovered. The core TF MYC was found to have significant positive relations with all these hub genes in the pancreas. The establishment of PPI networks is effective in the investigation of a variety of disorders [ 21 ]. The hub genes were suggested by calculating the degree, and NFE2L2 was considered as the core gene based on the degree score. When a cell is exposed to oxidative stress, NFE2L2/Nrf2 (nuclear factor, erythroid derived 2, like 2), a basic region leucine zipper transcription, will migrate into the nucleus and stimulates some genes' transcription to defend the oxidant response [ 22 ]. In the acute pancreatitis animal modules induced by caerulein, the severity of AP was observably alleviated by activating Nrf2 [ 23 ]. Meanwhile, many studies have confirmed that activated Nrf2 could ameliorate SAP, against AP-associated lung injury, liver injury and intestinal inflammation [ 23 – 25 , 22 ]. Furthermore, Nrf2 has been demonstrated to affect the activity of various ferroptosis and lipid peroxidation-related proteins, the previous study has shown that Nrf2 is an essential antioxidant response regulator by affecting the activity of various lipid peroxidation and ferroptosis-related proteins [ 26 ]. The expression of Nrf2 was likewise shown to be significantly up-regulated in AP and ferroptosis in the current investigation. This protein was also found to be involved in the cellular response to hydrogen peroxide, cellular response to oxidative stress, and cellular response to glucose starvation, according to GO analysis. These results imply that this gene has a role in the progression of ferroptosis in AP patients. 7 hub genes were mainly enriched in the response to oxidative stress, oxidative stress and redox pathway and ferroptosis pathway by using Metascape database. The findings revealed that oxidative stress defined AP ferroptosis during the disease process. In this study, HMOX1 (heme oxygenase, also known as HO-1) was found to have anti-oxidant and anti-inflammatory properties. HMOX1 is a rate-limiting enzyme that catalyzes the conversion of heme into biliverdin, carbon monoxide, and iron [ 27 ], was upregulated in the ferroptosis pathway and MAPK signaling pathway. As downstream genes of MAPK signaling pathway, activation of HO-1 has been shown to reduce the severity of AP in mice [ 28 ]. In a previous study, MAPK signaling pathway might be inducted into ferroptosis and activated to promote the generation of ROS [ 29 ]. In our work, EGFR expression in AP ferroptosis was overexpressed, and the MAPK signaling pathway, adherens junction signaling route, and relaxin signaling pathway were all found to be enriched in EGFR by analysis of KEGG. Several pathways including NF-κB, PI3K/AKT could activate EGFR. Li’s research proved that EGFR was significantly increased [ 30 ], and the activated EGFR/AKT pathway can defend from acinar cell necrosis by increasing Bcl-2 and Bcl-xl expression in AP modules [ 31 ]. Furthermore, EGFR signaling is necessary for the healing and regeneration of pancreatic tissue. We discovered that hub genes are regulated by core TFs, and MYC could be a target for preventing pancreatic inflammation in AP patients [ 32 ]. MYC regulates cell proliferation, metabolism, and differentiation and is a master transcription factor [ 33 , 34 ]. Furthermore, DNA methylation and inflammation stimuli coactivate the production of CtBPs, which associate with PCAF and c-MYC to form the CPM complex, aggravating inflammation and resulting in AP [ 35 ]. miRNAs are thought to bind the 3'UTR region of a gene and can regulate gene expression by degrading or inhibiting the target gene's translation [ 36 ]. The current study found that miR-22-3p and miR-135a-5p related to the acinar cell death in the acute edema pancreatic tissues [ 37 ]. Meanwhile the expression of miR-29a-3p is increased in the apoptosis of pancreatic acinar cells has been confirmed [ 38 ]. What’s more, it is also shown that miR-148b-3p may be an important regulatory miRNA in autophagy of pancreatic acinar cells [ 39 ]. In the present study, we integrated these potential miRNAs which related with acinar cell death with the DEGs of GSE109227 and data downloaded from FerrDb, to find miRNAs that may regulate ferroptosis in AP by bioinformatics analysis. While some studies found that lncRNAs can regulate miRNA abundance [ 40 ]. We selected 208 lncRNA-miRNA pairs contain 145 lncRNAs that regulated the potential miRNAs and may further regulated ferroptosis in acute pancreatitis. More animal and clinical research are also required to further verify the conclusions. In conclusion, we identified 38 DEGs by bioinformatics analysis based on dataset GSE109227 and data downloaded from FerrDb. We indicated the hub DEGs and discovered they were enriched in response to oxidative stress as well as oxidative stress and the redox pathway. We also predicted related TFs of these hub genes, such as MYC. And the lncRNA-miRNA-mRNA network was constructed to further understand the potential functions of ncRNAs in AP. 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Mediators Inflamm. 2018;2018:3232491. doi: 10.1155/2018/3232491 . Haddad JJ, Land SC. Redox/ROS regulation of lipopolysaccharide-induced mitogen-activated protein kinase (MAPK) activation and MAPK-mediated TNF-alpha biosynthesis. Br J Pharmacol. 2002;135(2):520–36. Li Z, Ma B, Lu M, Qiao X, Sun B, Zhang W et al. Construction of network for protein kinases that play a role in acute pancreatitis. Pancreas. 2013;42(4):607–13. doi: 10.1097/MPA.0b013e31826dc2b2 . Hu G, Shen J, Cheng L, Guo C, Xu X, Wang F et al. Reg4 protects against acinar cell necrosis in experimental pancreatitis. Gut. 2011;60(6):820–8. doi: 10.1136/gut.2010.215178 . Xu D, Xie R, Xu Z, Zhao Z, Ding M, Chen W et al. mTOR-Myc axis drives acinar-to-dendritic cell transition and the CD4 T cell immune response in acute pancreatitis. Cell Death Dis. 2020;11(6):416. doi: 10.1038/s41419-020-2517-x . Satoh K, Yachida S, Sugimoto M, Oshima M, Nakagawa T, Akamoto S et al. Global metabolic reprogramming of colorectal cancer occurs at adenoma stage and is induced by MYC. Proc Natl Acad Sci U S A. 2017;114(37):E7697-E706. doi: 10.1073/pnas.1710366114 . Stine ZE, Walton ZE, Altman BJ, Hsieh AL, Dang CV. MYC, Metabolism, and Cancer. Cancer Discov. 2015;5(10):1024–39. doi: 10.1158/2159-8290.CD-15-0507 . Zeng J, Chen J-Y, Meng J, Chen Z. Inflammation and DNA methylation coregulate the CtBP-PCAF-c-MYC transcriptional complex to activate the expression of a long non-coding RNA in acute pancreatitis. Int J Biol Sci. 2020;16(12):2116–30. doi: 10.7150/ijbs.43557 . Sun K-T, Chen MYC, Tu M-G, Wang IK, Chang S-S, Li C-Y. MicroRNA-20a regulates autophagy related protein-ATG16L1 in hypoxia-induced osteoclast differentiation. Bone. 2015;73:145–53. doi: 10.1016/j.bone.2014.11.026 . Qin T, Fu Q, Pan Y-F, Liu C-J, Wang Y-Z, Hu M-X et al. Expressions of miR-22 and miR-135a in acute pancreatitis. J Huazhong Univ Sci Technolog Med Sci. 2014;34(2):225–33. doi: 10.1007/s11596-014-1263-7 . Fu Q, Qin T, Chen L, Liu C-J, Zhang X, Wang Y-Z et al. miR-29a up-regulation in AR42J cells contributes to apoptosis via targeting TNFRSF1A gene. World J Gastroenterol. 2016;22(20):4881–90. doi: 10.3748/wjg.v22.i20.4881 . Gao B, Wang D, Sun W, Meng X, Zhang W, Xue D. Differentially expressed microRNA identification and target gene function analysis in starvation-induced autophagy of AR42J pancreatic acinar cells. Mol Med Rep. 2016;14(1):590–8. doi: 10.3892/mmr.2016.5240 . Statello L, Guo C-J, Chen L-L, Huarte M. Gene regulation by long non-coding RNAs and its biological functions. Nat Rev Mol Cell Biol. 2021;22(2). doi: 10.1038/s41580-020-00315-9 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-1627969","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":103942945,"identity":"91ade114-380d-4cfd-bd6e-2093483f4a46","order_by":0,"name":"Xu Yan","email":"","orcid":"","institution":"The Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Xu","middleName":"","lastName":"Yan","suffix":""},{"id":103942946,"identity":"d9915900-1f93-4383-b433-69070c5df7c5","order_by":1,"name":"Tianjiao Lin","email":"","orcid":"","institution":"The Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Tianjiao","middleName":"","lastName":"Lin","suffix":""},{"id":103942947,"identity":"eedf1aea-6ff2-4454-9786-19f2893671ba","order_by":2,"name":"Qingyun Zhu","email":"","orcid":"","institution":"The Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Qingyun","middleName":"","lastName":"Zhu","suffix":""},{"id":103942948,"identity":"f1b8fed7-a847-4f4b-a53c-ca5312fe8f71","order_by":3,"name":"Fei Tian","email":"","orcid":"","institution":"The Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Tian","suffix":""},{"id":103942950,"identity":"8ad38471-ce08-4537-a0ee-d6c08493ab30","order_by":4,"name":"Yushi Zhang","email":"","orcid":"","institution":"The Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Yushi","middleName":"","lastName":"Zhang","suffix":""},{"id":103942953,"identity":"7b74a0ec-8c0e-4e8f-a95f-a5f21244154e","order_by":5,"name":"Xinting Pan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYBACNvnjBx8AaTk29vYDxGnhl+BJNmBgMDDm4zmTQJwWyRkMZgJALYnzJBwMiNNicLshjbmw7U96mwRDAsOPim1EaLlz8NjjGWcMctukGw8w9py5TYSWAwnpxjwVQC0yBxKYGduI02ImzWNgkM4mkWBAnBbJGSAtFQYJxGvh5zmTbDzjjLFhGzCQDxLlF2AMHnxc2CYnL9/efvDBjwoitIAAM4xxgDj1yFpGwSgYBaNgFGAFANHbOqDVTI27AAAAAElFTkSuQmCC","orcid":"","institution":"The Affiliated Hospital of Qingdao University","correspondingAuthor":true,"prefix":"","firstName":"Xinting","middleName":"","lastName":"Pan","suffix":""}],"badges":[],"createdAt":"2022-05-06 03:14:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1627969/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1627969/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":21286519,"identity":"a0e7a1ba-b674-431e-bb50-6cdf4cd60444","added_by":"auto","created_at":"2022-05-10 14:23:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":728775,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e: Volcano plots of the 310 DEGs in acute pancreatitis and normal pancreatic tissues. Genes with high levels of expression are shown in red, whereas genes with low levels of expression are shown in blue. \u003cstrong\u003eb\u003c/strong\u003e: Heat map of the first 50 DEGs in acute pancreatitis and normal pancreatic tissues, with red indicating genes that were considerably up-regulated and blue indicating genes that were significantly down-regulated in the samples. \u003cstrong\u003ec\u003c/strong\u003e: Venn diagram of the DEGs by intersecting GSE109227 and ferroptosis dataset to identify ferroptosis DEGs.\u003c/p\u003e","description":"","filename":"Figure.1.png","url":"https://assets-eu.researchsquare.com/files/rs-1627969/v1/e2dafce458da4ef815ca7c20.png"},{"id":21286521,"identity":"246dcc16-007c-4fe5-b397-3efd39ebe296","added_by":"auto","created_at":"2022-05-10 14:23:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":495515,"visible":true,"origin":"","legend":"\u003cp\u003eDEGs’ enrichment in functional and pathway terms \u003cstrong\u003ea\u003c/strong\u003e: biological process (BP), \u003cstrong\u003eb\u003c/strong\u003e: cellular component (CC), \u003cstrong\u003ec\u003c/strong\u003e: molecular function (MF), \u003cstrong\u003ed\u003c/strong\u003e: KEGG pathways\u003c/p\u003e","description":"","filename":"Figure.2.png","url":"https://assets-eu.researchsquare.com/files/rs-1627969/v1/44e737db70336f2b557bf15f.png"},{"id":21287363,"identity":"de0221e4-a406-41cf-999c-f698f3ea36d0","added_by":"auto","created_at":"2022-05-10 14:28:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2351431,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e: The enriched terms network b: The biological pathway of DEGs drawn by Metascape. P-value \u0026lt;0.01 are statistically significant\u003c/p\u003e","description":"","filename":"Figure.3.png","url":"https://assets-eu.researchsquare.com/files/rs-1627969/v1/b951dbb595b1fad817f18903.png"},{"id":21287360,"identity":"cf410be3-cdc4-42d2-b8f1-fd591ecae2a2","added_by":"auto","created_at":"2022-05-10 14:28:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":439080,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e: The 21 nodes and 58 edges with interaction scores \u0026gt;0.4 were shown in Cytoscape as a network by using the online database STRING, the color of the nodes indicated degree of the genes. High degree genes are marked by red color while yellow means low degree genes. \u003cstrong\u003eb\u003c/strong\u003e: MCODE was used to obtain a key module from the PPI network.\u003c/p\u003e","description":"","filename":"Figure.4.png","url":"https://assets-eu.researchsquare.com/files/rs-1627969/v1/1a3665ffb9b902738dd24e86.png"},{"id":21287359,"identity":"1bc6cd27-2a11-475b-804d-2bea46b4c81c","added_by":"auto","created_at":"2022-05-10 14:28:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":238149,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of hub genes used the online database Metascape.\u003c/p\u003e","description":"","filename":"Figure.5.png","url":"https://assets-eu.researchsquare.com/files/rs-1627969/v1/a1388f8e273ba7ad8f27dca4.png"},{"id":21286522,"identity":"935d0d63-0653-484f-bb2b-3967fe22abc8","added_by":"auto","created_at":"2022-05-10 14:23:18","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":234343,"visible":true,"origin":"","legend":"\u003cp\u003eUpset plot built by ENCORI tool with 6 databases including predicted mRNAs for the potential miRNAs.\u003c/p\u003e","description":"","filename":"Figure.6.png","url":"https://assets-eu.researchsquare.com/files/rs-1627969/v1/3451e250aa5adfc131d3cca2.png"},{"id":21286526,"identity":"6a27e159-8b67-4d5d-ba83-8517180538d1","added_by":"auto","created_at":"2022-05-10 14:23:18","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1140528,"visible":true,"origin":"","legend":"\u003cp\u003eLncRNA-miRNA-mRNA interaction network with 145 lncRNA nodes, 4 miRNA nodes, 18 mRNA nodes and 235 edges for acute pancreatitis. Yellow rhombus indicated lncRNAs, green triangles represented miRNAs and red circles indicated mRNAs.\u003c/p\u003e","description":"","filename":"Figure.7.png","url":"https://assets-eu.researchsquare.com/files/rs-1627969/v1/2b060aca32a58db9ddf3fdd8.png"},{"id":21288351,"identity":"8449d778-ddf4-4f22-8591-d5331f7c51df","added_by":"auto","created_at":"2022-05-10 14:33:18","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":211255,"visible":true,"origin":"","legend":"\u003cp\u003eGene-TFs regulation network, the circle in red means hub genes and the rectangle in blue indicates TFs that may regulate the hub genes.\u003c/p\u003e","description":"","filename":"Figure.8.png","url":"https://assets-eu.researchsquare.com/files/rs-1627969/v1/ba7c519e4e59758d79a3910f.png"},{"id":21287362,"identity":"81d4f0c7-57e1-4f21-a7c2-35994dad95d2","added_by":"auto","created_at":"2022-05-10 14:28:18","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":279603,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis of hub genes and core TFs MYC in the pancreas.\u003c/p\u003e","description":"","filename":"Figure.9.png","url":"https://assets-eu.researchsquare.com/files/rs-1627969/v1/5a34f9390619b7aa35fcd78e.png"},{"id":21342369,"identity":"b61d0163-5b0a-4f93-87b5-d8f306bfa75b","added_by":"auto","created_at":"2022-05-11 15:44:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2852550,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1627969/v1/18067dac-3837-44ca-ae19-735fd935b31f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bioinformatics Analysis Identifies Significant Ferroptosis Genes and LncRNA-miRNA-mRNA Network in the Pathogenesis of Acute Pancreatitis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute Pancreatitis (AP) is one of the most common acute disease discharge diagnoses, with both local and systemic complications [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. AP is the most frequent discharge diagnosis in acute abdominal disease requiring hospital admission with severe complications and high mortality [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The key pathologic response of AP is cell death and inflammation, while necrosis and apoptosis are the most studied cell death type in AP [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. According to a prior study, programmed cell death-like processes including autophagy, pyroptosis and necroptosis may be a favorable response to acinar cell [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. While ferroptosis is considered a type of programmed cell death, and it is well established that ferroptosis exists in pancreatitis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], however, the mechanism of ferroptosis in AP remains unknown.\u003c/p\u003e \u003cp\u003eFerroptosis is a very new type of cell death that has only recently been found and is characterized as lipid peroxidation by reactive oxygen species in iron-dependent [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Ferroptosis has been linked to inflammation in a growing number of studies, while GPX4 has been identified as an anti-inflammatory factor when activated [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Ferroptosis is thought to be involved in the therapy of AP, according to recent reports, including alleviating AP-associated lung injury [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], kidney injury [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and intestinal barrier injury [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, many ferroptosis genes related to AP have not yet to be discovered.\u003c/p\u003e \u003cp\u003eWe discovered DEGs in AP and normal pancreatic tissues in this study, then intersected them with the ferroptosis dataset to get ferroptosis DEGs based on bioinformation. Meanwhile, we explore the gene-related biological functions and pathways as well as protein-protein interaction and lncRNA-miRNA-mRNA network. The findings of this research will improve our understanding of ferroptosis following AP and bring new ideas for AP clinical diagnosis and treatment.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eMicroarray Data Information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene Expression Omnibus (GEO), a public internet database, provided the relevant gene profiles. AP was screened as the keyword to discover the associated title, an mRNA database, GSE109227 and the details of the database were finally obtained. This dataset contains 11 samples, including five wild-type mice which were given sodium chloride as control and six mice were given caerulein intraperitoneally to induce experimental AP; pancreatic tissues were obtained from these samples and were labeled from GSM2935589 to GSM2935599. We also obtained a dataset from the Ferroptosis Database, including 259 genes. These microarray data are provided by public databases thus the approval of patients and the ethical committee is not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential Expression Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGSE109227 raw data were obtained from the GEO database and analyzed using GEO2R, an online analysis tool. GEO2R was used to compare the expression profiles of AP and control groups of samples to find DEGs using |log(fold-change)| \u0026gt; 1.5, and adjusted P-value \u0026lt;0.05 as the criteria. The genes that we obtained from Ferroptosis Database were interested with GSE109227 to identify ferroptosis DEGs. The internet application Venny2.1 was used to construct a Venn graphic of DEGs, and a heatmap of DEGs was created using Networkanalyst, which is an analytics tool for supporting integrative gene expression analysis of data utilizing statistical, visual, and network-based methodologies\u0026nbsp;[14].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene Functional Enrichment Analysis of DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe utilized DAVID 6.8, a database that provides systematic, complete biological data, to predict DEGs\u0026apos; functional annotation and pathway enrichment analysis\u0026nbsp;[15]. DEGs enrichment results of Gene Ontology (GO) including molecular functions (MFs), biological processes (BPs), and cellular components (CCs) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were obtained using a P-value \u0026lt; 0.05 as the enrichment terms threshold. The function annotation of DEGs by the Metascape website was performed using genes that were overlapped in GSE109227 and the ferroptosis dataset.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of DEG PPI Networks and Significant Module Screening\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSTRING is an online database that may be used to find out how a set of proteins interact, in this study STRING was utilized to display protein-protein interactions and explore functional relationships between DEG-encoded proteins. Subsequently, with interaction scores \u0026gt;0.4, we obtained network visualization by Cytoscape. Following that, the PPI network\u0026apos;s major submodules and hub genes were screened using the analysis program MCODE. Degree cutoff=2, node score cutoff=0.2, and K-core=2 were the criteria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of Gene-miRNA Interaction Pairs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe previous study showed that several miRNAs exert contribution in the acinar cell inflammation and death. Meanwhile miR-22-3p, miR-29a-3p, miR-135a-5p and miR-148b-3p have been considered playing important role in the acinar cell death\u0026nbsp;[16]. \u0026nbsp; Hence, we selected miR-22-3p, miR-29a-3p, miR-135a-5p and miR-148b-3p as potential miRNAs. We used ENCORI which contains 6 databases (PicTar, Targetscan, miRanda, microT, miRmap, PITA) to predicted the target mRNA of the potential miRNAs. The selected mRNAs were incorporated with the DEG genes to identify the common genes, and these genes were used to build miRNA-mRNA pairs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of lncRNA-miRNA-mRNA interactions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ENCORI database was used to predict the target lncRNA from the four selected miRNAs and to simultaneously identify the lncRNA-miRNA pairs. A preliminary lncRNA-miRNA-mRNA network was constructed by deleting the nodes that cannot match the axis and integrating the miRNA-mRNA pairs and lncRNA-miRNA pairs. The we utilized Cytoscape to visualize the lncRNA-miRNA-mRNA network.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of Transcriptional Factors (TFs) of Hub Genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe hub gene-related transcriptional factors were predicted by Networkanalyst. The NetworkAnalyst was used to enroll a list of hub genes, which was then processed step by step until the gene-related TFs and TF-gene interaction pairs were displayed. The associations between hub genes and anticipated core TFs were then assessed using GEPIA, an online website to provide primary interactive and customizable functions [17]. GEPIA used Pearson, Spearman, and Kendall to identify pair-wise gene expression correlation analyses for specified sets of TCGA and/or GTEx expression data.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eIdentification of DEGs in Acute Pancreatitis and Ferroptosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GEO database provided the raw data and we extracted DEGs by comparing AP and control group samples. Subsequently, we intersected 259 genes obtained from the Ferroptosis Database with DEGs of GSE109227 to identify ferroptosis DEGs. It showed one downregulated gene and 37 upregulated genes (\u003cstrong\u003eTable 1\u003c/strong\u003e). \u003cstrong\u003eFigure 1\u003c/strong\u003e depicts a heat map, volcano plots, and a DEG Venn diagram. In addition, we divided these DEGs into three groups: ferroptosis driver, ferroptosis suppressor, and ferroptosis marker (\u003cstrong\u003eTable 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE1 \u0026nbsp;\u0026nbsp;\u003c/strong\u003eAcute pancreatitis genes are differentially expressed in ferroptosis\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eGene.symbol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eP.Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003elogFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003eGene.title\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eDusp1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e9.79E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e4.340851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003edual specificity phosphatase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10449284\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eTxnrd1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e1.41E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e2.213799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003ethioredoxin reductase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10365260\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eSrxn1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e4.78E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e3.658947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003esulfiredoxin 1 homolog (S. cerevisiae)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10477061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eChac1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e1.49E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e2.379898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003eChaC, cation transport regulator 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10474972\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eSlc7a11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e0.000206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e2.249066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003esolute carrier family 7 (cationic amino acid transporter, y+ system), member 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10498024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eDdit4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e1.29E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e2.639451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003eDNA-damage-inducible transcript 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10369290\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eSesn2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e4.77E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e2.452913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003esestrin 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10516932\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eTxnip\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e0.000209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e1.636378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003ethioredoxin interacting protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10494428\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eAtf3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e9.52E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e4.190948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003eactivating transcription factor 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10361091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eSlc3a2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e2.1E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e1.642015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003esolute carrier family 3 (activators of dibasic and neutral amino acid transport), member 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10465772\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eTrib3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e1.19E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e2.416594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003etribbles pseudokinase 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10488608\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eCebpg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e9.01E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e1.68513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003eCCAAT/enhancer binding protein (C/EBP), gamma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10562416\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eRela\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e1.23E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e2.837673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003ev-rel reticuloendotheliosis viral oncogene homolog A (avian)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10460631\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eHmox1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e2.71E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e2.970025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003eheme oxygenase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10572897\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eHspb1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e1.13E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e3.11411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003eheat shock protein 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10408928\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eNfe2l2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e1.08E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e1.690716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003enuclear factor, erythroid derived 2, like 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10483809\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eMap3k5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e1.93E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e2.27558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003emitogen-activated protein kinase kinase kinase 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10361926\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eSlc2a1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e6.37E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e2.168648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003esolute carrier family 2 (facilitated glucose transporter), member 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10507594\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eCapg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e5.84E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e1.852655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003ecapping protein (actin filament), gelsolin-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10539135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eGclc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e2.11E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e4.284143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003eglutamate-cysteine ligase, catalytic subunit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10587266\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eSqstm1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e3.86E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e2.771646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003esequestosome 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10385572\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eCd44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e9.79E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e3.501365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003eCD44 antigen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10485405\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eJun\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e1.26E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e1.687334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003ejun proto-oncogene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10514466\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003ePlin2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e6.4E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e3.393173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003eperilipin 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10514221\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eGch1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e8.05E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e1.573587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003eGTP cyclohydrolase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10419288\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003ePgd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e3.29E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e1.63238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003ephosphogluconate dehydrogenase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10518570\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eAcsl4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e3.84E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e1.527963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003eacyl-CoA synthetase long-chain family member 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10607089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eNras\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e9.04E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e1.766424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003eneuroblastoma ras oncogene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10494857\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eKras\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e3.63E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e2.608895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003eKirsten rat sarcoma viral oncogene homolog\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10549256\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eSlc38a1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e2.56E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e3.663981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003esolute carrier family 38, member 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10431874\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eGot1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e4.71E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e2.403565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003eglutamic-oxaloacetic transaminase 1, soluble\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10467842\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eMap1lc3a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e2.41E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e1.708451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003emicrotubule-associated protein 1 light chain 3 alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10477637\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eWipi2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e1.3E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e1.907656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003eWD repeat domain, phosphoinositide interacting 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10527133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eSat1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e2.07E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e3.020738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003espermidine/spermine N1-acetyl transferase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10607467\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eEgfr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e3.31E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e3.350486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003eepidermal growth factor receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10374366\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003ePrkaa1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e4.21E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e1.917747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003eprotein kinase, AMP-activated, alpha 1 catalytic subunit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10422707\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eAno6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e4.42E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e2.274626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003eanoctamin 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10426479\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eBnip3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e2.44E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e-1.55474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\"\u003e\n \u003cp\u003eBCL2/adenovirus E1B interacting protein 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003e10414269\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 2\u0026nbsp;\u003c/strong\u003eThe ferroptosis differentially expressed genes were divided into three parts\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSuppressor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiver\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eGclc, Sqstm1, Cd44, Jun, Plin2, Gch1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003ePgd, Acsl4, Nras, Kras, Slc38a1, Got1, Map1lc3a, Wipi2, Sat1, Egfr, Prkaa1, Ano6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eDusp1, Txnrd1, Srxn1, Chac1, Slc7a11, Ddit4, Sesn2, Txnip, Atf3, Slc3a2, Trib3, Cebpg, Rela, Hmox1, Hspb1, Nfe2l2, Map3k5, Slc2a1, Capg, Bnip3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional Enrichment and Pathway Analysis of the Ferroptosis DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, the online software DAVID was used to detect functional enrichment and pathway analysis to further clarify the functions of the DEGs in acute pancreatitis. Functional enrichment analysis of GO terms contains BP, CC and MF three categories. The P-value was indicated by the color of bubbles, and the size of bubbles, which had a significant positive relationship with the number of DEGs engaged in this term, indicated the number of DEGs contained in the term (\u003cstrong\u003eFigure 2\u003c/strong\u003e). Cellular response to hydrogen peroxide, oxidative stress, apoptotic process, negative regulation of the apoptotic process, positive regulation of the apoptotic process, and so on were among the GO terms in the BP category. The GO terms in the CC category were mainly enriched in the cytosol, cytoplasm, macromolecular complex, etc. The DEGs were mostly enriched in MF terms such as identical protein binding, protein homodimerization activity, protein kinase binding, and ubiquitin protein ligase binding. The KEGG pathway analysis was conducted, containing MAPK signaling pathway, autophagy, ferroptosis pathway and fluid shear stress and atherosclerosis and so on. Second, we submitted the related DEGs to Metascape. The biological process was considerably enriched in response to oxidative stress, cellular response to famine, and positive control of cell death, according to the results of the enrichment pathway and analysis. The MAPK signaling pathway, ferroptosis, oxidative stress and redox pathway, and oxidative stress response were all significantly activated in biological pathways (\u003cstrong\u003eFigure 3\u003c/strong\u003e). Finally, the MAPK signaling pathway was identified as the most important biological pathway involved in both DAVID and Metascape analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtein-Protein Interaction Network Construction of DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further invested in the potential relationships between DEGs, we uploaded them to STRING online database. Finally, a PPI network of the associated DEGs was created, with 35 nodes and 123 edges with pairs combined score \u0026gt;0.4 (\u003cstrong\u003eFigure 4\u003c/strong\u003e). The genes are represented by the nodes in the network, while the edges reflect the relationships between them. MCODE, a Cytoscape program, was utilized to select the submodule of significance, and the result showed a submodule score of 6.9, containing 12 nodes and 38 edges. Subsequently, we also calculated the degree of those nodes. In this study, we selected 8 nodes with degree \u0026ge;10 as criteria, including Jun (degree=15); Sqstm1, Kras, Nfe2l2 and Hmox1 (degree=14); Slc7a11 (degree=12); Egfr (degree=11); Atf3 (degree=10). Except for Jun, 7 of the genes were contained in the submodule and they were all up-regulated in AP samples. We identified these 7 genes as hub genes. Additionally, the hub genes were uploaded to Metascape for functional analysis, and these hub genes were shown to be primarily involved in oxidative stress response, oxidative stress response, and oxidative stress response, according to the results (\u003cstrong\u003eFigure 5\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of Gene-Related miRNA pairs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe database ENCORI predicted a total of 19619 targets in 6 databases (\u003cstrong\u003eFigure 6\u003c/strong\u003e). The DEG genes of GSE109227 and Ferroptosis Database were integrated with the targeted genes, and 26 miRNA-mRNA pairs were identified. There were 7 mRNAs of miR-148b-3p, 15 mRNAs of miR-22-3p, 1 mRNA of miR-29a-3p, and 3 mRNAs of miR-135a-5p.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLncRNA-miRNA-mRNA Network Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used the ENCORI database to identify the potential lncRNAs of the selected miRNAs, and 208 lncRNA-miRNA pairs were obtained. The lncRNA-miRNA-mRNA network was shown in \u003cstrong\u003eFigure 7\u003c/strong\u003e, which contained 145 lncRNA nodes, 4 miRNA nodes, 18 mRNA nodes and 235 edges.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranscriptional Factor Regulation Network Analysis of Hub Genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine how hub genes are transcribed and how transcription factors affect their expression, we used Networkanalyst to build a gene-TFs regulation network (\u003cstrong\u003eFigure 8\u003c/strong\u003e). The network includes 27 TFs, in addition to the hub genes, for a total of 73 gene-TF interaction pairings. The regulation network of gene-TFs shows that transcriptional regulators were substantially enriched in the majority of the hub genes. We considered MYC to be the key TF that regulates the majority of the hub genes: Atf3, Hmox1, Sqstm1 and Nfe2l2. Other TFs like UBTF, NRF1, CDH1, ETS1, JUND and HCFC1 were likewise thought to be a key TF in the regulation among most hub genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHub Genes and Core TFs Correlation Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs a result of the finding that MYC has a critical regulatory relationship with seven hub genes, we used GEPIA to look for hub genes and projected core TFs correlation. The result of the correlation analysis between MYC and hub genes: SQSTM1, KRAS, NFE2L2, HMOX1, SLC7A11, EGFR and ATF3 are shown in \u003cstrong\u003eFigure 9\u003c/strong\u003e. The non-log-scale axis was utilized for calculation, and the log-scale axis was used for visualization. We finally identified positive correlations of MYC and hub genes with criteria of P value less than 0.05.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe development of AP is influenced by multiple factors including alcohol, gallstones and other factors. Increasing evidence suggests that ferroptosis may play a role in the development of AP. Fan's research has shown that ferroptosis play a crucial role in AP, while we can inhibit the activity of GPX4 to mediate the ferroptosis finally mediate AP and associated lung injury [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The intestinal barrier injury and acute kidney injury induced by SAP have also been related with ferroptosis, according to earlier studies [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Ferroptosis is a new type of nonapoptotic programmed cell death connected to lipid peroxidation and reactive oxygen species in the presence of iron [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The accumulation of lipids within the cell ROS is a characteristic of ferroptosis, and it is thought to be the outcome of lipid oxidation, which leads to cell membrane damage and death [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, extensive validations are needed to improve the understanding of ferroptosis in the pathogenesis of AP.\u003c/p\u003e \u003cp\u003eThe main genes involved in ferroptosis and acute pancreatitis caused by caerulein injection were discovered in this investigation. The datasets GSE109227 and FerrDb provided 38 DEGs for our research. In addition, the DEGs' function enrichment and pathway enrichment analyses showed that these genes are mostly implicated in stress response and the MAPK signaling pathway. The ceRNA network was constructed to indicate the underlying connection between non-coding RNA and ferroptosis in AP. Hub DEGs hub gene-related major TFs were discovered. The core TF MYC was found to have significant positive relations with all these hub genes in the pancreas.\u003c/p\u003e \u003cp\u003eThe establishment of PPI networks is effective in the investigation of a variety of disorders [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The hub genes were suggested by calculating the degree, and NFE2L2 was considered as the core gene based on the degree score. When a cell is exposed to oxidative stress, NFE2L2/Nrf2 (nuclear factor, erythroid derived 2, like 2), a basic region leucine zipper transcription, will migrate into the nucleus and stimulates some genes' transcription to defend the oxidant response [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In the acute pancreatitis animal modules induced by caerulein, the severity of AP was observably alleviated by activating Nrf2 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Meanwhile, many studies have confirmed that activated Nrf2 could ameliorate SAP, against AP-associated lung injury, liver injury and intestinal inflammation [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Furthermore, Nrf2 has been demonstrated to affect the activity of various ferroptosis and lipid peroxidation-related proteins, the previous study has shown that Nrf2 is an essential antioxidant response regulator by affecting the activity of various lipid peroxidation and ferroptosis-related proteins [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The expression of Nrf2 was likewise shown to be significantly up-regulated in AP and ferroptosis in the current investigation. This protein was also found to be involved in the cellular response to hydrogen peroxide, cellular response to oxidative stress, and cellular response to glucose starvation, according to GO analysis. These results imply that this gene has a role in the progression of ferroptosis in AP patients.\u003c/p\u003e \u003cp\u003e7 hub genes were mainly enriched in the response to oxidative stress, oxidative stress and redox pathway and ferroptosis pathway by using Metascape database. The findings revealed that oxidative stress defined AP ferroptosis during the disease process. In this study, HMOX1 (heme oxygenase, also known as HO-1) was found to have anti-oxidant and anti-inflammatory properties. HMOX1 is a rate-limiting enzyme that catalyzes the conversion of heme into biliverdin, carbon monoxide, and iron [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], was upregulated in the ferroptosis pathway and MAPK signaling pathway. As downstream genes of MAPK signaling pathway, activation of HO-1 has been shown to reduce the severity of AP in mice [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In a previous study, MAPK signaling pathway might be inducted into ferroptosis and activated to promote the generation of ROS [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our work, EGFR expression in AP ferroptosis was overexpressed, and the MAPK signaling pathway, adherens junction signaling route, and relaxin signaling pathway were all found to be enriched in EGFR by analysis of KEGG. Several pathways including NF-κB, PI3K/AKT could activate EGFR. Li\u0026rsquo;s research proved that EGFR was significantly increased [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and the activated EGFR/AKT pathway can defend from acinar cell necrosis by increasing Bcl-2 and Bcl-xl expression in AP modules [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Furthermore, EGFR signaling is necessary for the healing and regeneration of pancreatic tissue.\u003c/p\u003e \u003cp\u003eWe discovered that hub genes are regulated by core TFs, and MYC could be a target for preventing pancreatic inflammation in AP patients [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. MYC regulates cell proliferation, metabolism, and differentiation and is a master transcription factor [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Furthermore, DNA methylation and inflammation stimuli coactivate the production of CtBPs, which associate with PCAF and c-MYC to form the CPM complex, aggravating inflammation and resulting in AP [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003emiRNAs are thought to bind the 3'UTR region of a gene and can regulate gene expression by degrading or inhibiting the target gene's translation [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The current study found that miR-22-3p and miR-135a-5p related to the acinar cell death in the acute edema pancreatic tissues [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Meanwhile the expression of miR-29a-3p is increased in the apoptosis of pancreatic acinar cells has been confirmed [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. What\u0026rsquo;s more, it is also shown that miR-148b-3p may be an important regulatory miRNA in autophagy of pancreatic acinar cells [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In the present study, we integrated these potential miRNAs which related with acinar cell death with the DEGs of GSE109227 and data downloaded from FerrDb, to find miRNAs that may regulate ferroptosis in AP by bioinformatics analysis. While some studies found that lncRNAs can regulate miRNA abundance [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. We selected 208 lncRNA-miRNA pairs contain 145 lncRNAs that regulated the potential miRNAs and may further regulated ferroptosis in acute pancreatitis. More animal and clinical research are also required to further verify the conclusions.\u003c/p\u003e \u003cp\u003eIn conclusion, we identified 38 DEGs by bioinformatics analysis based on dataset GSE109227 and data downloaded from FerrDb. We indicated the hub DEGs and discovered they were enriched in response to oxidative stress as well as oxidative stress and the redox pathway. We also predicted related TFs of these hub genes, such as MYC. And the lncRNA-miRNA-mRNA network was constructed to further understand the potential functions of ncRNAs in AP. These results of the present research will provide potential targets for the treatment and lead a more clear understanding of the pathogenesis of ferroptosis in AP.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBoxhoorn L, Voermans RP, Bouwense SA, Bruno MJ, Verdonk RC, Boermeester MA et al. Acute pancreatitis. 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Gene regulation by long non-coding RNAs and its biological functions. Nat Rev Mol Cell Biol. 2021;22(2). doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41580-020-00315-9\u003c/span\u003e\u003cspan address=\"10.1038/s41580-020-00315-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003c/ol\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":"Acute pancreatitis, Noncoding RNA, Bioinformatical analysis, Ferroptosis, Differentially expressed genes ","lastPublishedDoi":"10.21203/rs.3.rs-1627969/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1627969/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives \u003c/strong\u003eThe present study aims to identify the underlying mechanisms of ferroptosis and lncRNA-miRNA-mRNA network in AP by bioinformatics tools.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e The ferroptosis genes interacted with data obtained from the Gene Expression Omnibus to identify differentially expressed genes (DEGs). Gene ontology, KEGG pathway enrichment analysis, protein-protein interaction network construction, hub genes association analysis, and transcription factor prediction were used to select and analyze the differentially expressed genes.\u0026nbsp;lncRNA-miRNA-mRNA interaction network were constructed by integrating the lncRNA-miRNA pairs and miRNA-mRNA pairs.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e We identified 38 differentially expressed genes from the datasets. The function enrichment and pathway enrichment analysis of the DEGs were mostly implicated in stress response and the MAPK signaling pathway. The lncRNA-miRNA-mRNA network contained 145 lncRNA nodes, 4 miRNA nodes, 18 mRNA nodes and 235 edges. SQSTM1, KRAS, NFE2L2, HMOX1, SLC7A11, EGFR and ATF3 were identified as hub DEGs, and hub gene-related main TF MYC was discovered to have critical positive associations with these hub genes in pancreatic tissues. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003eThe results of this study indicated that hub genes and the lncRNA-miRNA-mRNA network may play an important role in the mechanisms of ferroptosis in AP and provide treatment targets for AP.\u003c/p\u003e","manuscriptTitle":"Bioinformatics Analysis Identifies Significant Ferroptosis Genes and LncRNA-miRNA-mRNA Network in the Pathogenesis of Acute Pancreatitis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-05-10 14:23:15","doi":"10.21203/rs.3.rs-1627969/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":"fa496acb-949e-4c13-bce2-b0cd0e91fa4f","owner":[],"postedDate":"May 10th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-05-11T15:44:29+00:00","versionOfRecord":[],"versionCreatedAt":"2022-05-10 14:23:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1627969","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1627969","identity":"rs-1627969","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}