Characterization of PANoptosis-related genes in Crohn’s disease by integrated bioinformatics, machine learning and experiments

Abstract

Abstract Currently, the biological understanding of Crohn’s disease (CD) remains limited. PANoptosis is a revolutionary form of cell death reported to participate in numerous diseases, including CD. In our study, we aimed to uncover the roles of PANoptosis in CD. Differentially expressed PANoptosis-related genes (DE-PRGs) were identified by overlapping PANoptosis-related genes and differentially expressed genes between CD and normal samples in a combined microarray dataset. Three machine learning algorithms were adopted to detect hub DE-PRGs. To stratify the heterogeneity within CD patients, nonnegative matrix factorization clustering was conducted. In terms of immune landscape analysis, the “ssGSEA” method was applied. qRT-PCR was performed to examine the expression levels of the hub DE-PRGs in CD patients and colitis model mice. Ten hub DE-PRGs with satisfactory diagnostic performance were identified and validated: CD44, CIDEC, NDRG1, NUMA1, PEA15, RAG1, S100A8, S100A9, TIMP1 and XBP1. These genes displayed significant associations with certain immune cell types and CD-related genes. We also constructed gene‒microRNA, gene‒transcription factor and drug‒gene interaction networks. CD samples were classified into two PANoptosis patterns according to the expression levels of the hub DE-PRGs. Our results suggest that PANoptosis plays a nonnegligible role in CD by modulating the immune system and interacting with CD-related genes.

Keywords

• PANoptosis
• Crohn’s disease
• Bioinformatics
• Machine learning
• Regulatory networks