Identification of PANoptosis-Related Genes in Community-Acquired Pneumonia Diagnosis.

BACKGROUND

This study aimed to identify and characterize novel PANoptosis biomarkers for community-acquired pneumonia (CAP) diagnosis.

METHODS

Transcriptomic data from training set GSE196399 and validation sets GSE94916 and GSE202947 were utilized. A PANoptosis gene set was identified by intersecting DEGs linked to CAP, WGCNA hub genes, and PANoptosis-related genes. GO and KEGG analyses were conducted for enrichment analysis. PANoptosis scores were calculated via ssGSEA. Feature genes were identified using SVM-RFE, LASSO regression, and RF methods. Diagnostic performance was assessed via ROC analysis. Immune cell infiltration was evaluated using CIBERSORT. A PPI network was constructed, and a nomogram was developed for CAP prediction. Drug-gene interactions were investigated. qRT-PCR was conducted to confirm feature gene alterations in clinical samples.

RESULTS

We identified 7555 DEGs associated with CAP from the GSE196399 dataset. Through WGCNA, a PANoptosis gene set of 39 genes was found, showing significant enrichment in pathways related to apoptosis and inflammation. CAP patients exhibited significantly reduced PANoptosis scores compared to healthy controls, with a marked upregulation in the majority of the PANoptosis gene set in high-score individuals. Four feature genes (, and ) were identified as potential biomarkers, exhibiting high diagnostic accuracy with AUCs generally above 0.8. These genes also showed significant correlations with M0 macrophages and neutrophils. Drug-gene interaction analysis revealed potential therapeutic agents targeting and . Validation in clinical samples confirmed gene expression alterations in CAP patients.

CONCLUSION

The identified PANoptosis feature genes demonstrate high diagnostic accuracy for CAP, serving as potential biomarkers and therapeutic targets for CAP.