P0216 PANoptosis-related genes can predict the progression of ulcerative colitis and colitis-associated colorectal cancer
Authors: Li Y , Zheng S , Yan Q , Zhong X 
Published: January 2025

Abstract

Background
Ulcerative colitis (UC) is a complex inflammatory bowel disease that significantly increases the risk of developing colitis-associated colorectal cancer (CAC). The etiology of UC remains unclear, and the mechanisms underlying its progression to CAC require further exploration. Recent studies have identified a novel form of cell death called pan-apoptosis, which is closely linked to inflammatory diseases. However, its specific role in UC and CAC progression is not yet fully understood. This study aims to develop a reliable predictive model based on pan-apoptosis using high-throughput RNA sequencing and validate it, along with an analysis of cellular characteristics in UC patients through single-cell sequencing data. This research offers new insights for the prognosis and treatment of UC patients.

Methods
This study collected single-cell and high-throughput sequencing data from UC patients and defined a pan-apoptosis gene set. The dataset was analyzed for immune infiltration, differential expression, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). Additionally, weighted gene co-expression network analysis (WGCNA) was used to identify key genes associated with UC, which were then utilized to construct a predictive model. Finally, single-cell data analysis was conducted on UC patients, followed by in vitro validation of the expression of key genes.

Results
By analyzing differentially expressed genes in UC samples and using the pan-apoptosis gene set, WGCNA identified key genes associated with UC, including CASP1, LCN2, STAT3, and ZBP1. These genes were used to construct a pan-apoptosis-related predictive model, establishing a pan-apoptosis-related score (PAR-Score). The results showed that PAR-Score has strong classification ability and is positively correlated with immune cell infiltration and the inflammatory microenvironment in UC patients. Notably, CAC patients had significantly higher PAR-Scores. Single-cell sequencing data confirmed that monocyte activity in UC patients is linked to pan-apoptosis, while intercellular communication among immune cells was notably reduced. The expression levels of key genes in the predictive model were validated in vivo. Drug predictions targeting these genes suggested that Leridistim, interferon-γ, and other compounds could serve as potential therapeutic agents for UC or CAC.

Conclusion
The predictive model established based on pan-apoptosis exhibits good predictive performance, providing new perspectives for assessing the condition and progression of UC patients, and opening up new avenues for the treatment of UC and CAC in the future.

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