Integrated machine learning and bioinformatic analysis of mitochondrial-related signature in chronic rhinosinusitis with nasal polyps.

Background: Chronic rhinosinusitis with nasal polyps (CRSwNP) is a prevalent inflammatory disorder affecting the upper respiratory tract. Recent studies have indicated an association between CRSwNP and mitochondrial metabolic disorder characterized by impaired metabolic pathways; however, the precise mechanisms remain unclear. This study aims to investigate the mitochondrial-related signature in individuals diagnosed with CRSwNP.

Methods: Through the integration of differentially expressed genes (DEGs) with the mitochondrial gene set, differentially expressed mitochondrial-related genes (DEMRGs) were identified. Subsequently, the hub DEMRGs were selected using 4 integrated machine learning algorithms. Immune and mitochondrial characteristics were estimated based on CIBERSORT and ssGSEA algorithms. Bioinformatic findings were confirmed through RT-qPCR, immunohistochemistry, and ELISA for nasal tissues, as well as Western blotting analysis for human nasal epithelial cells (hNECs). The relationship between hub DEMRGs and disease severity was assessed using Spearman correlation analysis.

Results: A total of 24 DEMRGs were screened, most of which exhibited lower expression levels in CRSwNP samples. Five hub DEMRGs (, , , , and ) were consistently downregulated in both the discovery and validation cohorts. The hub genes showed a high diagnostic performance and were positively correlated with the infiltration of M2 macrophages and resting mast cells. Experimental results confirmed that the 5 genes were downregulated at both the mRNA and protein levels within nasal polyp tissues. Finally, a significant and inverse relationship was identified between the expression levels of these genes and both the Lund-Mackay and Lund-Kennedy scores.

Conclusion: Our findings systematically unraveled 5 hub markers correlated with mitochondrial metabolism and immune cell infiltration in CRSwNP, suggesting their potential to be based to design diagnostic and therapeutic strategies for the disease.