Immuno-transcriptomic analysis based on machine learning identifies immunity signature genes of chronic...

Chronic rhinosinusitis with nasal polyps (CRSwNP) is a prevalent inflammatory disease where immunomodulation plays a pivotal role. However, immuno-transcriptomic characteristics and its clinical relevance remains largely known. We analyzed transcriptome data of 48 patients with CRSwNP and 34 healthy control subjects from different cohorts and investigated the immuno-transcriptomic characteristics. Differential immune-related genes (DIRGs) were identified and subjected to enrichment analysis. Protein-protein interaction (PPI) networks were constructed to identify hub genes. The least absolute shrinkage and selection operator (LASSO) regression model and multivariate support vector machine recursive feature elimination (mSVM-RFE) were used to identify potential biomarkers, which were validated using the real time quantitative polymerase chain reaction (RT-PCR) and immunohistochemistry (IHC). Infiltration abundance of immune cells in the microenvironment were estimated using CIBERSORT algorithm. Our study identified a total of 660 differentially expressed genes (DEGs) and 81 differentially immune-related genes (DIRGs) in CRSwNP compared to controls. Functional enrichment analysis revealed that the DIRGs were primarily associated with cell chemotaxis and leukocyte migration, and cytokine-cytokine receptor interaction. Through machine learning, we further identified five candidate genes, CXCR1, CCL13, CCR3, PPBP, and MMP9. These five potential CRSwNP biomarkers were experimentally verified in our in-house cohort. Analysis of immune cell infiltration landscape revealed significant variations in the abundance of macrophages and mast cells between CRSwNP and healthy control. Our findings illuminate the significance of immune characteristics in CRSwNP pathogenesis. Future studies focusing on these candidate genes can help elucidate the underlying mechanisms and identify potential therapeutic targets for CRSwNP.