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

doi:10.21203/rs.3.rs-1627969/v1

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

https://doi.org/10.21203/rs.3.rs-1627969/v1

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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

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.

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.

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.

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. 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.

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No competing interests reported.

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

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