Revealing Prdx4 as a potential diagnostic and therapeutic target for acute pancreatitis based on machine learning analysis

Abstract

Acute pancreatitis (AP) is a common systemic inflammatory disease resulting from the activation of trypsinogen by various incentives in ICU. The annual incidence rate is approximately 30 out of 100,000. Some patients may progress to severe acute pancreatitis, with a mortality rate of up to 40%. Therefore, the goal of this article is to explore the key genes for effective diagnosis and treatment of AP. The analysis data for this study were merged from two GEO datasets. 1357 DEGs were used for functional enrichment and cMAP analysis, aiming to reveal the pathogenic genes and potential mechanisms of AP, as well as potential drugs for treating AP. Importantly, the study used LASSO and SVM-RFE machine learning to screen the most likely AP occurrence biomarker for Prdx4 among numerous candidate genes. A receiver operating characteristic of Prdx4 was used to estimate the incidence of AP. The ssGSEA algorithm was employed to investigate immune cell infiltration in AP. The biomarker Prdx4 gene exhibited significant associations with a majority of immune cells and was identified as being expressed in NKT cells, macrophages, granulocytes, and B cells based on single-cell transcriptome data. Finally, we found an increase in Prdx4 expression in the pancreatic tissue of AP mice through immunohistochemistry. After treatment with recombinant Prdx4, the pathological damage to the pancreatic tissue of AP mice was relieved. In conclusion, our study identified Prdx4 as a potential AP hub gene, providing a new target for treatment.

Keywords: Acute pancreatitis (AP); Bioinformatics analysis; Diagnostic value; Immune cell infiltration; Machine learning.

Fig. 1

Flow chart of the study

Fig. 2

The integration of AP datasets and differential expression analysis of the integrated AP dataset. (A) PCA of three original AP datasets before batch-effect correction. (B) PCA of the integrated AP dataset after batch-effect correction. (C) The volcano plot representing AP DEGs in the integrated AP dataset. The upregulated genes are presented in red dots, whereas, the downregulated genes are presented in blue dots. (D) Heatmap plot of DEGs (up- and downregulated genes each displaying the top 10)

Fig. 3

Functional enrichment analysis of DEGs. (A) Bar plot of DEGs functional enrichment terms. (B) Network relationship plots among all enriched terms. Colored by p-value, where terms containing more genes tend to have a more significant p-value. (C) GSEA enrichment analysis results in AP or low expression Prdx4 tissue samples. (D) GSEA enrichment analysis results in Control or high expression Prdx4 tissue samples

Fig. 4

Screening of the potential small-molecular compounds for the treatment of AP via cMAP analysis. (A) The heatmap presenting the top10 compounds with the most significantly negative enrichment scores in 10 cell lines based on cMAP analysis, and descriptions of the top 10 compounds. (B) The chemical structures of those 10 compounds were shown. (C) cMAP connectivity map

Fig. 5

Screening of hub genes with diagnostic value via machine learning. (A) 13 diagnostic markers were screened by the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm. (B) 4 diagnostic markers were screened by a support vector machine-recursive feature elimination (SVM-RFE) algorithm. (C) Venn diagram of variables intersecting LASSO and SVM-RFE algorithms. (D) The expression level of hub gene Prdx4 in the merged dataset between AP and control groups. (E) The expression level of hub gene Prdx4 in the in the validation cohort between AP and control groups. (F) Analyze of diagnostic validity of the diagnostic marker Prdx4. (G) ROC validation of diagnostic validity of the diagnostic marker Prdx4 in the validation cohort. AUC = 0.917.

Fig. 6

Docking diagram of small molecule drugs with targets. (A) Line diagram of the lowest binding energy for molecular docking. (B) Docking diagram of NVP-AUY922, brefeldin-a, tyrphostin-AG-1478, TPCA-1, cyclosporin-a, tunicamycin, indirubin, tivozanib, geldanamycin, and ABT-737 docked to Prdx4, respectively

Fig. 7

A diagram of the association between infiltrating immune cells and the pivot gene Prdx4

Fig. 8

The hub gene Prdx4 plays a critical role in occurrence of AP. (A) The expression of Prdx4 in pancreatic tissue of AP model mice was detected by immunohistochemical staining (magnification, ×200). (B) Percentage of Prdx4⁺ cells in total number of cells in pancreatic tissue. (C) Representative histological sections of pancreatic tissue from AP model mice (hematoxylin and eosin staining; magnification, ×200)(D) The pathological pancreatic injury score based on histological sections

Fig. 9

The expression of Prdx4 was different in various cells during acute pancreatitis. (A) UMAP plot of all the single cells, with each color-coded for the 9 major cell types. (B) UMAP plots showing the expression of Prdx4 and its co-expressed genes. (C) Bubble plot showing the expression levels of Prdx4 and its co-expressed genes. The size of each dot represents the percent expressed; average expression is shown by color