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

Background: Chronic 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. Methods: Utilizing 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). Results: We 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. A prediction model built using these biomarkers yielded high diagnostic performance (AUC = 0.979). Single-cell resolution analysis revealed that distinct expression patterns were exhibited by these genes across subsets of immune cells. MR analysis determined that lower expression of FBP1, LYZ and NDRG2 could be risk factors for CRSwNP. Subsequent qRT-PCR in independent samples validated the downregulation of these genes in CRSwNP tissues. Conclusions: We systematically identify and validate a set of metabolic reprogramming-related genes with diagnostic value in CRSwNP. Collectively, these findings not only heighten the current mechanistic understanding of CRSwNP pathogenesis but also offer a novel platform to devise diagnostic and therapeutic avenues focusing on metabolism.