Application of pyroptosis related genes identified by bioinformatics in diagnosis of acute pancreatitis

Abstract

The invention relates to the technical field of biology, and particularly discloses application of pyroptosis related genes identified by bioinformatics in diagnosis of acute pancreatitis, which comprises the following steps: obtaining a preprocessed expression data set; performing differential expression analysis on the expression data set to obtain a group of differential expression genes; screening out pyroptosis-related differential expression genes to obtain a group of pyroptosis-related differential expression genes; performing function enrichment analysis on the pyroptosis-related differential expression gene to obtain molecular mechanism analysis; analyzing by utilizing the pyroptosis-related differential expression gene and the molecular mechanism, and constructing a diagnosis model of acute pancreatitis; comparing the risk score with a preset threshold value to determine whether the subject suffers from acute pancreatitis; according to the invention, pyroptosis-related differential expression genes are identified by a bioinformatics method, the importance of cell adhesion and migration-related pathways under the AP background is emphasized by function enrichment analysis, and the identified PRDEGs and the related pathways thereof reveal a disease mechanism.

Description

Technical Field

[0001] The present invention belongs to the field of biotechnology, and particularly relates to the application of identifying pyroptosis-related genes in the diagnosis of acute pancreatitis using bioinformatics. Background Art

[0002] Acute pancreatitis (AP) is a sudden inflammation of the pancreas, the severity of which can range from a mild, self-limiting disease to a severe, life-threatening disease. It is a common gastrointestinal cause of hospitalization worldwide, and the mortality rate of severe acute pancreatitis can be as high as 30%. Despite the progress in medical care, the current management of AP mainly relies on supportive measures because no specific drug treatment is available. This highlights the urgent need to develop innovative diagnostic markers and therapeutic targets to improve the clinical prognosis of AP patients.

[0003] Pyroptosis is characterized by cell swelling, lysis, and secretion of pro-inflammatory cytokines, which contribute to the inflammatory response. Recent studies have emphasized the important role of pyroptosis in various inflammatory diseases such as sepsis, atherosclerosis, and neurodegenerative diseases. Nevertheless, the role of pyroptosis in acute pancreatitis is still poorly understood. Exploring the mechanism of pyroptosis in acute pancreatitis may provide new insights into the pathophysiology of this disease and potentially discover new therapeutic targets.

[0004] The purpose of the present invention is to explore the role of pyroptosis in acute pancreatitis by identifying differentially expressed genes related to pyroptosis (PRDEGs). Using bioinformatics methods, a comprehensive analysis of gene expression datasets obtained from the Gene Expression Omnibus (GEO) database was performed. Subsequently, functional enrichment analysis was carried out, and a diagnostic model was developed to evaluate the potential clinical significance of these genes.

[0005] In view of this, the inventor proposes an application of identifying pyroptosis-related genes in the diagnosis of acute pancreatitis using bioinformatics to solve the above problems. Summary of the Invention

[0006] The purpose of the present invention is to provide an application of identifying pyroptosis-related genes in the diagnosis of acute pancreatitis using bioinformatics to solve the problems raised in the above background art.

[0007] To achieve the above purpose, the present invention provides the following technical solutions:

[0008] An application of identifying pyroptosis-related genes in the diagnosis of acute pancreatitis using bioinformatics, comprising:

[0009] Obtaining and preprocessing gene expression data from the pancreatic tissue of the subject, and the preprocessing step includes removing batch effects and data normalization to obtain a preprocessed expression dataset;

[0010] Perform differential expression analysis on the expression dataset using a preset statistical threshold (e.g., |logFC|>1 and adj.p<0.05) to distinguish upregulated and downregulated genes, and obtain a set of differentially expressed genes;

[0011] Compare the differentially expressed genes with a predefined pyroptosis-related gene database (e.g., pyroptosis gene information obtained from databases such as GeneCards and PubMed) to screen out pyroptosis-related differentially expressed genes (PRDEGs) and obtain a set of pyroptosis-related differentially expressed genes;

[0012] Perform functional enrichment analysis on the pyroptosis-related differentially expressed genes, including biological process and signaling pathway analysis based on GO and KEGG, to clarify their functional roles in cell adhesion, cell migration, and inflammatory responses, etc., which are related to the pathological mechanism of acute pancreatitis, and obtain the molecular mechanism analysis;

[0013] Using the pyroptosis-related differentially expressed genes and the molecular mechanism analysis, apply statistical model methods (e.g., logistic regression, support vector machine, or LASSO regression analysis) to construct a diagnostic model for acute pancreatitis, and calculate the risk score (RiskScore) according to the model, so as to obtain a diagnostic index for distinguishing acute pancreatitis from non-acute pancreatitis status;

[0014] Compare the risk score with a preset threshold to determine whether the subject has acute pancreatitis.

[0015] Compared with the prior art, the beneficial effects of the present invention are:

[0016] (1) The present invention focuses on pyroptosis related to various inflammatory diseases, identifies pyroptosis-related differentially expressed genes (PRDEGs) by bioinformatics methods, and aims to clarify the role of pyroptosis in AP. Integrating multiple datasets and advanced statistical methods such as logistic regression, support vector machine (SVM), and lasso regression, a reliable AP diagnostic model is constructed. Functional enrichment and immune infiltration analysis further reveal the involved biological processes and pathways, highlighting potential biomarkers and therapeutic targets. This comprehensive approach not only deepens the understanding of the pathogenesis of AP but also provides promising directions for improving clinical diagnosis and treatment.

[0017] (2) The functional enrichment analysis in the present invention emphasizes the importance of cell adhesion and migration-related pathways in the context of AP. The identified PRDEGs and their related pathways reveal the disease mechanism and highlight potential biomarkers for diagnosis and therapeutic targets. Future research should focus on validating these findings in clinical samples and exploring the therapeutic potential of modulating these pathways in AP. Brief Description of the Drawings

[0018] Figure 1 This is the comprehensive analysis flow chart of PRDEGs of the present invention.

[0019] AP, acute pancreatitis; GSEA, gene set enrichment analysis; DEGs, differentially expressed genes; PRGs, pyroptosis-related genes; GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; PRDEGs, pyroptosis-related differentially expressed genes; ROC, receiver operating characteristic curve; TF, transcription factor;

[0020] Figure 2 This is the batch effect elimination diagram of GSE109227, GSE65146 and GSE121038 of the present invention, where: A. Box plot. B. Box plot of the distribution after batch. C. Principal component analysis diagram before batch correction. D. Go to the principal component analysis diagram and then perform batch processing;

[0021] Figure 3 This is the DGE analysis of the present invention, where: A. Volcano plot of differentially expressed genes between the control group and the AP group. B. Venn diagram of differentially expressed genes and PRGs. C. Heat map of PRDEGs. Brown represents the acute pancreatitis (AP) group, and green represents the control group. Red represents high level and blue represents low level;

[0022] Figure 4 This is the diagram of differential expression verification and correlation analysis of the present invention, where: Comparison diagram in the integration (merged dataset) of cell pyroptosis (PRDEGs) with GEO datasets of acute pancreatitis (AP) samples and the control group (Control). B-G. Expressions of PRDEGsRela and Iqgap1 (B), Actn4 and Flna (C), Anxa3 and Vtn (D), Lcn2 and Mpeg1 (E), Pah and Car9 (F) were compared. ROC curve of Pyhin1 (G). In the inter-group comparison diagram, green and brown represent the control group and the AP group respectively. **p < 0.01; ***p < 0.001;

[0023] Figure 5 This is the GO and KEGG enrichment analysis of PRDEGs of the present invention, where: A. Bar chart shows the results of GO and KEGG enrichment analysis of PRDEGs: BP, CC, MF and biological pathways. B-E. Represent the results of gene ontology (GO) and pathway (KEGG) network diagram analysis of differentially expressed genes (PRDEGs) related to cells: BP (B), CC (C), MF (D) and KEGG (E). In the figure, the two nodes represent entries and molecules respectively, and the attachment represents the relationship between the entry and the molecule. The screening criteria are adj.p < 0.05 and q < 0.25;

[0024] Figure 6 This is the GSEA combined dataset graph of the present invention, where: A. The GSEA 4 biological function bubble chart shows the integrated GEO dataset. B - E. Genes are significantly enriched in the Mapk pathway (B), Nfkb pathway (C), Tgfb pathway (D), and Pi 3kakt signaling pathway (E). In the bubble chart, the size and color of the bubbles represent the number and size of genes in the NES value;

[0025] Figure 7 This is the AP diagnostic model graph of the present invention, where: A. Forest plot of 11 PRDEGs in the AP diagnostic model. B - C. Having the lowest error rate (B) and the highest accuracy (C). The number of genes D - E. Diagnostic model graph (D) and variable trace graph (E);

[0026] Figure 8 This is the diagnostic and validation graph of the AP and Friends analysis of the present invention, where A. The RiskScore is on the ROC curve of the comprehensive GEO dataset (combined dataset). B. Genetic model (Model Genes) in the acute pancreatitis (AP) diagnostic model, and the integration of the comprehensive GEO dataset (combined dataset) is listed in the nomogram. C - d. Acute pancreatitis (AP) diagnostic model based on the comprehensive GEO dataset (combined dataset), calibration curve of genes (Model Genes) (calibration curve graph (C) and DCA (D). Gene F model (Model Genes) similarity box plot (Friends) analysis results. The y - axis of the calibration curve represents net income, and the x - axis represents the probability threshold or threshold probability (probability listed in the threshold). The ROC curve is from 0.5 to 1. High precision can be obtained when the AUC is higher than 0.9;

[0027] Figure 9 This is the differential expression verification and correlation analysis graph of the present invention, where: A. Comparison graph of gene model (model genes) between high - risk and low - risk groups. B - E. ROC curves of model genes Anxa3 (B), Iqgap1 (C), Rela (D), Vtn (E) in acute pancreatitis (AP) samples, and the data are from the combined GEO dataset. Orange represents the high - risk group comparison graph (high - risk) group, and blue represents the low - risk group (low - risk). ***p < 0.001 is represented. AUC > 0.5 indicates the promotion of events. AUC between 0.7 and 0.9 has a certain accuracy, while AUC exceeding 0.9 has a very high accuracy;

[0028] Figure 10Differential gene expression analysis and GSEA plots for the risk groups of the present invention, where: A. Volcano plot of differential gene expression analysis in high-risk and low-risk samples. B. Heatmap of the log fold change ranking of acute pancreatitis (AP) samples in the high-risk (High risk) and low-risk (Low risk) groups, with the top 10 differentially expressed genes to be enhanced and intercepted. C. Bubble plot of gene set enrichment analysis (GSEA) of the acute pancreatitis (AP) sample set showing four biological functions. D-g. Gene set enrichment analysis (GSEA) shows significant enrichment of the IL67 pathway (D), Tgfb pathway (E), Hippo signaling pathway (F), and HedgehogGli pathway (G) in acute pancreatitis (AP) high risk. AP, acute pancreatitis. In the heatmap group, orange and blue represent the high-risk and low-risk groups, respectively. In the heatmap, red and blue represent high expression and low expression, respectively. In the bubble plot, the size and color of the bubbles represent the number and size of genes enriched in the NES value;

[0029] Figure 11 Model gene regulatory network diagram of the present invention, where: A is the mRNA-TF, and B is the mRNA-miRNA model gene network;

[0030] Figure 12 Combined dataset plot of immune infiltration analysis by the CIBERSORT algorithm of the present invention, where A-B are the immune cells in the integrated GEO dataset (combined dataset), and the grouped comparison plots with histograms (A) and (B). C. Correlation heatmap of immune cells in the integrated GEO dataset (combined dataset). D. Correlation bubble plot of immune cell infiltration abundance and model genes. ns p≥0.05; **p<0.01; ***p<0.001. Green represents the control group (Control), and brown represents the acute pancreatitis (AP) group. Red represents positive correlation, and blue represents negative correlation. The depth of the color represents the strength of the correlation;

[0031] Figure 13This is the risk group immune infiltration analysis chart of the present invention by the CIBERSORT algorithm, where: A. Comparison of immune cells between the low-risk group and the high-risk group of acute pancreatitis (AP). B-C. Correlation between the heat maps of immune cells in the low-risk sample (LowRisk) group (B) and the high-risk (HighRisk) group (C) of acute pancreatitis (AP). D-E. Correlation between the genes of immune cell infiltration abundance in the low-risk sample (LowRisk) group (D) and the high-risk (HighRisk) group (E) of acute pancreatitis (AP) and the model (model genes). AP, acute pancreatitis. ns p≥0.05; *p<0.05; **p<0.01; ***p<0.001. Blue represents the low-risk (LowRisk) group, and orange represents the high-risk group (HighRisk). Red and blue represent negative correlation, and the depth of color represents the correlation. Detailed implementation mode

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0033] Example 1:

[0034] Please refer to Figures 1 to 13 As shown, an application of using bioinformatics to identify pyroptosis-related genes in the diagnosis of acute pancreatitis includes:

[0035] Data collection:

[0036] Use the R package GEOquery to download data from the GEO database, and obtain three datasets (GSE109227, GSE65146, and GSE121038), which mainly focus on acute pancreatitis (AP). The samples in the datasets GSE109227, GSE65146, and GSE121038 are all from mice, and the tissue source is the pancreas. Table 1 provides a comprehensive overview of the detailed information.

[0037] In the dataset GSE109227, the chip platform used is GPL6246, which contains 6 AP samples and 5 control samples. For the dataset GSE65146, the chip platform is also GPL6246, including 39 AP samples, 5 control samples, and 27 mutant mouse samples. The dataset GSE121038 uses the GPL10787 chip platform, which contains 8 AP samples and 7 control samples. The present invention only considers the AP group and the control group.

[0038] The GeneCards database (https: / / www.genecards.org / ) was utilized to identify pyroptosis-related genes (PRGs). By searching for the two keywords "pyroptosis" and "protein-coding", genes with a "correlation score > 1" were selected, and a total of 405 PRGs were identified. Additionally, a search was conducted in the PubMed database using "pyroptosis" as the keyword, and 33 PRGs were obtained from the literature Wu, J., et al., Comprehensive Analysis of Pyroptosis-Related Genes and Tumor Microenvironment Infiltration Characterization in Breast Cancer. Front Immunol, 2021.12: p. 748221. After removing duplicates, a final set of 413 human PRGs was obtained, and then cross-comparison with 334 mouse PRGs was performed using the R package homologene (version 1.4.68.19.3.27). More detailed information is shown in Table 1 below.

[0039] Table 1. GEO microarray chip information

[0040]

[0041] The sva R package (version 3.50.0) was used to eliminate batch effects from the datasets GSE109227, GSE65146, and GSE121038, thereby creating a combined dataset containing 53 AP cases and 17 control cases. Subsequently, the integrated GEO dataset (combined dataset) was normalized using the R package limma. Descriptive parameters were optimized and uniformly generated. To verify the effectiveness of the elimination process, principal component analysis was performed on the expression matrix. Principal component analysis is a method for reducing data dimensionality, which involves extracting eigenvectors (components) from high-dimensional data and representing these features in a low-dimensional space, usually visualizing these characteristics using two-dimensional or three-dimensional plots.

[0042] Acute pancreatitis dataset combination:

[0043] The R package "sva" was used to eliminate batch effects in GSE109227, GSE65146, and GSE121038, thereby generating a combined GEO dataset. Subsequently, distribution box plots ( Figure 2 A - B) were generated to analyze the levels of the dataset. Additionally, principal component analysis plots ( Figure 2 C - D) were created to examine the distribution. The results of both the distribution box plots and the principal component analysis plots indicated that after this removal process, the batch effects in the AP dataset samples were effectively resolved.

[0044] Differentially expressed genes related to heat shock in acute pancreatitis:

[0045] A comprehensive GEO dataset including an acute pancreatitis (AP) group and a control group was used to analyze differences at the gene level. Differential expression analysis identified differentially expressed genes, of which 213 genes met the criteria of |logFC| > 1 and adj.p < 0.05. 177 genes were upregulated (logFC > 1 and adj.p < 0.05), and 36 genes were downregulated (logFC < -1 and adj.p < 0.05). A volcano plot was created to visualize these results ( Figure 3 A).

[0046] To identify PRDEGs, all differentially expressed genes (DEGs) were intersected with PRGs with |logFC| > 1 and adj.p < 0.05. This intersection yielded 11 PRDEGs: Rela, Iqgap1, Actn4, Flna, Anxa3, Vtn, Lcn2, Mpeg1, Pah, Car9, and Pyhin1. A Venn diagram was plotted to show the overlap of these genes ( Figure 3 B). The different levels of these PRDEGs were evaluated, and a heatmap is shown in Figure 3 C.

[0047] Verification of PRDEGs and ROC curves:

[0048] To evaluate the variable PRDEGs in the combined GEO dataset, an inter-group comparison was created ( Figure 4 A) to show the results of differential analysis of PRDEGs in acute pancreatitis (AP) samples and control samples. Differential analysis showed that the expression levels of 11 PRDEGs were statistically significant (p < 0.01) in both the control group and the AP group. These genes include Rela, Iqgap1, Actn4, Flna, Anxa3, Vtn, Lcn2, Mpeg1, Pah, Car9, and Pyhin1.

[0049] In addition, ROC curves were plotted using PRDEGs. ROC curve analysis ( Figure 4 B - G) showed that five PRDEGs - Rela, Iqgap1, Actn4, Flna, and Anxa3 - showed high accuracy (AUC > 0.9) in classifying AP and control samples, while Vtn, Lcn2, Mpeg1, Pah, Car9, and Pyhin1 showed moderate accuracy (0.7 - 0.9).

[0050] Example 2:

[0051] Please refer to Figures 1 to 13 as shown below:

[0052] Enrichment analysis:

[0053] GO and KEGG were used to analyze the relationship between 11 pyroptosis-related differentially expressed genes (PRDEGs) and acute pancreatitis (AP), as shown in Table 2:

[0054] Table 2. Enrichment analysis of PRDEGs

[0055]

[0056]

[0057] These 11 PRDEGs were mainly enriched in the following categories:

[0058] Biological process (BP):

[0059] Regulation of cell-matrix adhesion, cell-matrix adhesion, amoeboid cell migration, positive regulation of cell-matrix adhesion, protein localization to cell-cell junction, Cellular component (CC):

[0060] Cortical cytoskeleton, cell cortex, cortical actin cytoskeleton, actin filament bundle, actin cytoskeleton, Molecular function (MF):

[0061] Actin filament binding, actin binding, chromatin DNA binding, transmembrane receptor protein tyrosine kinase activity, iron ion binding, Biological pathway (KEGG):

[0062] Focal adhesion (mouse), Proteoglycans in cancer (mouse):

[0063] The results of GO and KEGG enrichment analysis were visualized by histograms ( Figure 5 A). In addition, network diagrams of BP, CC, MF, and biological pathways were created based on the enrichment analysis ( Figure 5 B-E). Larger nodes represent entries containing additional molecules, and the lines reflect the annotation of related molecules and their entries. The results showed that more genes were involved in the regulation of cell-matrix adhesion, cell-matrix adhesion, and amoeboid cell migration.

[0064] GSEA for acute pancreatitis:

[0065] To evaluate the impact of these genes on acute pancreatitis (AP) in the GEO combined dataset, a GSEA study was conducted to investigate the levels of all genes and BP, CC, and MF. The results are summarized in Table 3 and visualized in Figure 6 A.

[0066] GSEA of the combined dataset GSEA of the combined dataset

[0067]

[0068]

[0069]

[0070]

[0071] Gene Set Enrichment Analysis (GSEA):

[0072] The GSEA results showed that all genes in the combined GEO dataset were significantly enriched in several key pathways and functions, including: the MAPK pathway ( Figure 6 B), the NF-κB pathway ( Figure 6 C), the TGF-β pathway ( Figure 6 D), the PI3K-Akt signaling pathway ( Figure 6 E)

[0073] These pathways are crucial for understanding the molecular mechanisms of acute pancreatitis and highlight the important biological processes (BP), cellular components (CC), and molecular functions (MF) affected by gene expression changes in AP.

[0074] Establishment of an acute pancreatitis diagnostic model:

[0075] To determine the diagnostic levels of 11 PRDEGs in acute pancreatitis (AP), logistic regression analysis was performed and visualized using a forest plot ( Figure 7 A). The results showed that all 11 PRDEGs were significant (p < 0.05). These genes included Rela, Iqgap1, Actn4, Flna, Anxa3, Vtn, Lcn2, Mpeg1, Pah, Car9, and Pyhin1.

[0076] Next, an SVM model was constructed using the 11 PRDEGs to determine the number of genes with the best accuracy rate ( Figure 7 C) and the lowest error rate ( Figure 7 B). The results showed that the accuracy rate of the SVM model reached its peak when there were 10 genes.

[0077] Subsequently, an AP diagnostic model containing 11 PRDEGs was obtained through lasso analysis. To visualize, a lasso regression model plot ( Figure 7 D) and a lasso variable trace plot ( Figure 7 E) were drawn. The results showed that four PRDEGs, known as model genes, were included in the lasso regression model: Vtn, Anxa3, Rela, and Iqgap1.

[0078] Finally, the LASSO risk score (RiskScore) was calculated based on the risk coefficients of the LASSO regression analysis. The risk score was calculated using the following formula: RiskScore = Vtn × (-0.175) + Anxa3 × 0.21 + Rela × 2.754 + Iqgap1 × 0.319

[0079] Validation and analysis of the AP diagnostic model:

[0080] First, the ROC curve was plotted using the R package pROC and RiskScore. The ROC curve ( Figure 8 A) showed that RiskScore had a high accuracy (AUC > 0.9).

[0081] A network diagram was drawn based on the model genes in the diagnostic model to illustrate their interrelationships, as Figure 8 shown in B, the Rela level was significantly more effective in the AP diagnostic model, while the Anxa3 level was significantly lower than other variables.

[0082] Next, a calibration curve was plotted through calibration analysis to demonstrate the precision and specificity of the model. The goodness of fit between the actual opportunity and the expected probability in different situations was used to evaluate the predictive ability of the model for the actual results. As Figure 8 shown in C, the calibration line deviated slightly but was close to the diagonal.

[0083] In addition, DCA was performed to obtain the clinical utility of the model ( Figure 8 D). The line of the model remained stable above "all positive" and "all negative", indicating a higher net benefit and better performance.

[0084] Finally, the functional similarity (Friends) analysis score was used to identify genes that play important roles in the biological processes of AP, Figure 8 as shown in E, Iqgap1 played a significant role in AP and was closest to the critical value (cut-off value = 0.55).

[0085] Verification of model genes and ROC curve:

[0086] To evaluate the model gene levels in AP samples, the inter-group comparison ( Figure 9 A) showed four model differentially expressed genes (Vtn, Anxa3, Rela, and Iqgap1) between the high-risk group and the low-risk group. The expression levels of these four model genes were highly significant (p < 0.001).

[0087] The ROC curve ( Figure 9B-E) showed high accuracy (AUC > 0.9) in the expression levels of two model genes, Iqgap1 and Rela. The levels of two PRDEGs, Anxa3 and Vtn, showed moderate accuracy (0.7 < AUC < 0.9) in classifying high-risk and low-risk groups.

[0088] High-risk and low-risk GSEA:

[0089] Differential analysis was performed using the R package limma to detect differentially expressed genes between the high-risk and low-risk groups. The analysis results showed that a total of 113 differentially expressed genes met the thresholds of |logFC| > 1 and adj.p < 0.05. Among them, 82 genes were upregulated (logFC > 1 and adj.p < 0.05), and 31 genes were downregulated (logFC < -1 and adj.p < 0.05).

[0090] A volcano plot was created to visualize the results of differential analysis ( Figure 10 A). In addition, a heatmap was generated to show the top 10 upregulated and downregulated DEGs based on logFC ( Figure 10 B).

[0091] GSEA explored the participation of all expressed genes in biological processes, cellular components, and molecular functions. The results were visualized using a bubble plot ( Figure 10 C) and summarized in Table 4.

[0092] Table 4: GSEA results of risk groups

[0093]

[0094]

[0095]

[0096] GSEA indicated that all genes in AP samples were highly expressed in several key pathways and functions, including:

[0097] IL-6 / JAK / STAT3 signaling pathway ( Figure 10 D)

[0098] TGF-β pathway ( Figure 10 E)

[0099] Hippo signaling ( Figure 10 F)

[0100] Hedgehog / Gli pathway ( Figure 10 G)

[0101] These pathways are crucial for understanding the molecular mechanism of AP and highlight the important BPs, CCs, and MFs affected by changes in AP gene expression.

[0102] Establishment of the regulatory network:

[0103] First, an mRNA-TF regulatory network containing 3 model genes and 20 transcription factors was constructed through gene access in the ChIPBase database and model gene transcription factors (TFs) ( Figure 11 A), and the specific information is shown in Table S2.

[0104] Next, miRNAs related to the model genes were obtained from the StarBase database, and an mRNA-miRNA regulatory network was constructed and visualized using Cytoscape software ( Figure 11 B), which includes 4 model genes and 25 miRNAs, and the specific information is shown in Table 3.

[0105] Immune infiltration analysis of acute pancreatitis (CIBERSORT)

[0106] The GEO dataset was integrated, and the abundances of 22 immune cells were calculated. First, based on the results of the immune infiltration analysis, Figure 12 A shows the proportions of immune cells in the combined GEO dataset. A set of comparison plots was also generated to show the abundance differences of different immune cell infiltrations. Group comparison plots ( Figure 12 B) showed that five immune cells were highly significant (p < 0.05): M1 macrophages, Treg cells, CD4 memory T cells, Th17 cells, and monocytes.

[0107] Next, relevant heatmaps were generated to show the immune infiltration of 5 significantly different immune cells in acute pancreatitis (AP) samples. Most immune cells showed strong correlations, and M1 macrophages and monocytes had the best positive correlation ( Figure 12 C).

[0108] Finally, a bubble plot was drawn to show the correlation between the model genes and the abundances of immune cell infiltrations ( Figure 12 D). The correlation bubble plot showed that most immune cells showed strong correlations with the gene Iqgap1, and among them, Treg cells had the strongest correlation, showing a negative correlation (r = -0.676, p < 0.05).

[0109] Immune infiltration analysis of high- and low-risk groups (CIBERSORT)

[0110] The infiltration abundances of 22 immune cells in acute pancreatitis (AP) samples were calculated using the CIBERSORT algorithm, and these samples were from the combined GEO dataset. First, according to the results of immune infiltration analysis, a between-group comparison plot was generated to show the differences in immune cell infiltration abundances among different groups. Between-group comparison ( Figure 13 A) showed eight significant immune cell types (p < 0.05), namely: CD8 memory T cells, M1 macrophages, M2 macrophages, Treg cells, Th1 cells, monocytes, γδT cells, and activated NK cells.

[0111] Next, a relevant heatmap analysis was performed to study the immune infiltration in acute pancreatitis (AP) samples. Figure 13 B-C showed that in low-risk samples, most immune cells showed strong correlations, among which Treg cells and monocytes showed the strongest negative correlation (r = -0.613, p < 0.05). In high-risk samples, most immune cells also showed strong correlations, among which M1 macrophages and γδT cells showed the strongest negative correlation (r = -0.387).

[0112] Finally, a bubble plot was drawn to show the correlation between the model genes and the infiltration abundances of immune cells ( Figure 13 D-E). The relevant bubble plot showed that in low-risk samples, most immune cells had a strong association with the Iqgap1 gene, among which Treg cells showed the strongest negative correlation (r = -0.754, p < 0.05). In high-risk samples, most immune cells had a strong association with the ANXA3 gene, among which monocytes showed the best positive correlation.

[0113] Summary of the findings:

[0114] The integration of the GEO dataset provided valuable insights into genetic and molecular mechanisms. The identification of PRDEGs and the construction of a diagnostic model verified by various statistical and bioinformatics analyses provided potential biomarkers for the diagnosis and treatment of AP. The immune infiltration analysis further elucidated the role of the immune system in the AP process, highlighting the significant differences between the low-risk and high-risk groups.

[0115] As described above, acute pancreatitis (AP) is a sudden inflammation of the pancreas, and the symptoms can range from mild discomfort to life-threatening diseases. It is characterized by the premature activation of digestive enzymes within the pancreas, leading to autodigestion and inflammation. Globally, the incidence of acute pancreatitis is on the rise, and severe cases in particular have a relatively high fatality and mortality rate. This disease not only imposes a huge burden on the medical system but also seriously affects the quality of life of patients, often resulting in prolonged hospital stays and long-term complications. Given the urgent need for early diagnosis and effective treatment strategies, understanding the molecular mechanisms of acute pancreatitis is of crucial importance.

[0116] This invention focuses on pyroptosis associated with various inflammatory diseases, including AP, and identifies pyroptosis-related differentially expressed genes (PRDEGs) through bioinformatics methods. This invention aims to clarify the role of pyroptosis in AP. By integrating multiple datasets and advanced statistical methods such as logistic regression, support vector machine (SVM), and lasso regression, a reliable AP diagnosis model is constructed. Functional enrichment and immune infiltration analyses further reveal the biological processes and pathways involved, highlighting potential biomarkers and therapeutic targets. This comprehensive approach not only deepens the understanding of the pathogenesis of AP but also provides promising directions for improving clinical diagnosis and treatment.

[0117] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An application of bioinformatics to identify pyroptosis-related genes in the diagnosis of acute pancreatitis, characterized in that: include: Acquiring and preprocessing gene expression data from pancreatic tissue of a subject, wherein the preprocessing step includes batch effect removal and data normalization, to obtain a preprocessed expression data set; Performing differential expression analysis on the expression data set, using a preset statistical threshold to distinguish up-regulated and down-regulated genes, and obtaining a group of differentially expressed genes; Comparing the differentially expressed genes with a predefined pyroptosis-related gene database to screen out pyroptosis-related differentially expressed genes PRDEGs, thereby obtaining a group of pyroptosis-related differentially expressed genes; Functional enrichment analysis was performed on the pyroptosis-related differentially expressed genes, including biological process and signal pathway analysis based on GO and KEGG, to obtain molecular mechanism analysis; Using the pyroptosis-related differentially expressed genes and the molecular mechanism analysis, a statistical model method is used to construct a diagnostic model for acute pancreatitis, and a risk score RiskScore is calculated according to the model to obtain a diagnostic index for distinguishing acute pancreatitis from non-acute pancreatitis states; The risk score is compared with a preset threshold to determine whether the subject suffers from acute pancreatitis.

2. The method according to claim 1, wherein the method comprises: The differential expression analysis was performed using the l imma package in R software; The statistical threshold was |logFC|>1 and adj.p<0.05 after Benjamini-Hochberg correction.

3. The method according to claim 1, wherein the method comprises: The pyroptosis-related genes are screened from the GeneCards database and PubMed literature based on correlation scores, and the human pyroptosis-related genes are converted into corresponding genes of mice through a homologous alignment method.

4. The method according to claim 1, wherein the method comprises: The diagnostic model for acute pancreatitis was constructed by using LASSO regression analysis to determine the core genes including Vtn, Anxa3, Rela and Iqgap1, and the risk score RiskScore was calculated by the following formula: Among them, RiskScore is the risk coefficient score, coefficient(genei) is the gene regression coefficient determined by LASSO regression analysis, and mRNAExpression(genei) is the mRNA gene expression level.

5. The method according to claim 1, wherein the method comprises: The functional enrichment analysis used the clusterProfiler package in R software to perform GO and KEGG pathway enrichment analysis on the pyroptosis-related differentially expressed genes to determine the biological functions related to cell-matrix adhesion, native phagocytic cell migration, and inflammatory signaling pathways.

6. The method according to claim 1, wherein the method comprises: The method of determining whether the subject suffers from acute pancreatitis also includes verifying the accuracy of the diagnostic model using ROC curve analysis. When the area under the ROC curve AUC of the risk score is greater than 0.9, the diagnostic effect is judged to be high accuracy, 0.7-0.9 is medium accuracy, and 0.5-0.7 is low accuracy.

7. The method according to claim 1, wherein the method comprises: The acute pancreatitis samples were divided into high-risk group and low-risk group based on the risk score, and the differences in immune cell infiltration and PRDEGs expression levels between the two groups were analyzed.

8. The method according to claim 7, wherein the method comprises: The analysis of the differences in immune cell infiltration included: using the CIBERSORT algorithm to calculate the proportions of 22 types of immune cells in the samples, and revealing the differences in the immune microenvironment between the high-risk group and the low-risk group through inter-group comparisons and related heat maps.

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