Abstract

Acute pancreatitis (AP) and chronic pancreatitis (CP) are considered to be two separate pancreatic diseases in most studies, but some clinical retrospective analyses in recent years have found some degree of correlation between the two in actual treatment, however, the exact association is not clear. In this study, bioinformatics analysis was utilized to examine microarray sequencing data in mice, with the aim of elucidating the critical signaling pathways and genes involved in the progression from AP to CP. Differential gene expression analyses on murine transcriptomes were conducted using the R programming language and the R/Bioconductor package. Additionally, gene network analysis was performed using the STRING database to predict correlations among genes in the context of pancreatic diseases. Functional enrichment and gene ontology pathways common to both diseases were identified using Metascape. The hub genes were screened in the cytoscape algorithm, and the mRNA levels of the hub genes were verified in mice pancreatic tissues of AP and CP. Then the drugs corresponding to the hub genes were obtained in the drug-gene relationship. A set of hub genes, including Jun, Cd44, Epcam, Spp1, Anxa2, Hsp90aa1, and Cd9, were identified through analysis, demonstrating their pivotal roles in the progression from AP to CP. Notably, these genes were found to be enriched in the Helper T-cell factor (Th17) signaling pathway. Up-regulation of these genes in both AP and CP mouse models was validated through quantitative real-time polymerase chain reaction (qRT-PCR) results. The significance of the Th17 signaling pathway in the transition from AP to CP was underscored by our findings. Specifically, the essential genes driving this progression were identified as Jun, Cd44, Epcam, Spp1, Anxa2, Hsp90aa1, and Cd9. Crucial insights into the molecular mechanisms underlying pancreatitis progression were provided by this research, offering promising avenues for the development of targeted therapeutic interventions.

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