Diagnostic value and immune microenvironment regulatory network of metabolic reprogramming in chronic rhinosinusitis with nasal polyps identified by multidimensional transcriptome integration and machine learning

BackgroundChronic Rhinosinusitis with Nasal polyps (CRSwNP) are characterized by chronic inflammation and occur in 1–4% of the population worldwide. Patients often have comorbid asthma, and standard treatments among them are hindered by significant recurrence and lack of durability.

Currently, knowledge of the molecular circuitry and immune microenvironmental interplay that utilizes metabolic reprogramming within CRSwNP is incomplete.MethodsUtilizing CRSwNP datasets from the GEO database, we performed bioinformatics analysis to identify differentially expressed genes (DEGs) implicated in metabolic reprogramming. Key regulatory genes were subsequently selected by weighted gene co-expression network analysis (WGCNA) and machine learning algorithms; their relationship with the immune microenvironment was then evaluated.

To further investigate the underlying pathogenic mechanisms, we performed single-cell RNA sequencing (scRNA-seq) to map cellular expression patterns and applied Mendelian randomization (MR) analysis to assess potential causal relationships. Key molecules were subsequently experimentally validated by quantitative real-time PCR (qRT-PCR).ResultsWe identified 21 DEGs associated with metabolic reprogramming that are relevant to CRSwNP.

This subset was then analyzed using machine learning to identify 8 hub genes - ERBB4, FBP1, HMGCS2, LYZ, NDRG2, PIP, PYCR1, and SLC43A1.