Identification of Inflammasome-Related Genes and Their Role in Predicting Community-Acquired Pneumonia

Abstract

Community-acquired pneumonia (CAP) is a common respiratory infectious disease worldwide, posing a significant threat to human health. In recent years, the crucial role of inflammasomes in the occurrence and development of pneumonia has gradually been recognized. This study aims to systematically explore potential diagnostic biomarkers related to inflammasomes in CAP. CAP transcriptome datasets GSE103119 and GSE196399 were obtained from the GEO database. First, we conducted differential expression analysis and combined it with weighted gene co-expression network analysis to screen inflammasome-related differentially expressed genes (DEIRGs). Subsequently, we carried out Kyoto Encyclopedia of Genes and Genomes and Gene Ontology enrichment analysis. Then, based on multiple machine learning algorithms [least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE), Boruta, and random forest], we performed feature gene selection and combined a support vector machine (SVM) to construct a classification model. The diagnostic performance was evaluated in the training set and validation set through the receiver operating characteristic curve and precision-recall curve. Finally, we collected peripheral blood samples from 5 patients with CAP and 5 healthy controls and used quantitative real-time polymerase chain reaction (qRT-PCR) to verify the expression differences of candidate genes. A total of 414 DEIRGs were identified, mainly enriched in immune response regulation, Th17 cell differentiation, and autophagy pathways. Combined with machine learning and SVM analysis, the LASSO and RFE models showed the best performance in CAP diagnosis. Based on the intersection of these two algorithms, RPL39, INSL3, and vimentin (VIM) were ultimately determined as the core candidate genes. The qRT-PCR results further confirmed that in CAP peripheral blood samples, INSL3 and VIM showed significantly increased expression, while RPL39 showed decreased expression, which was consistent with the bioinformatics prediction. This study identifies RPL39, INSL3, and VIM as potential diagnostic markers for CAP, which may provide new evidence for early diagnosis and stratified management.