Integrative causal inference and predictive modeling reveal the iron-related gene SLC17A4 as a key biomarker in chronic rhinosinusitis.
To investigate whether iron metabolism exerts a causal influence on chronic rhinosinusitis (CRS) and to identify iron-related biomarkers and regulatory genes with diagnostic and therapeutic potential.
A two-sample Mendelian randomization (MR) analysis was conducted using large-scale GWAS summary statistics for four iron-related traits and three nasal inflammatory diseases. Significant SNPs were mapped to proximal genes and analyzed via Gene Ontology (GO), KEGG pathway enrichment, and protein-protein interaction (PPI) network construction. Candidate gene expression was validated using the GSE69093 transcriptomic dataset and qRT-PCR in nasal mucosal tissues from CRS patients and healthy controls. Molecular docking simulations were performed to assess ligand interactions, and clinical association and machine learning models were applied to evaluate diagnostic relevance and predictive performance.
MR analysis identified transferrin saturation (TSAT) as a causal protective factor for CRS (OR = 0.9988, P = 0.014). Thirty-one genes were mapped from MR-associated SNPs, with SLC17A4 highlighted as a key candidate gene. Enrichment analysis indicated involvement in iron metabolism and inflammatory regulation. SLC17A4 expression was significantly downregulated in both GSE69093 and clinical qRT-PCR samples. TSAT and SLC17A4 levels showed strong inverse correlations with Lund-Mackay and SNOT-22 scores. Molecular docking identified Troglitazone as a strong-binding ligand to SLC17A4 (-10.0 kcal/mol). Machine learning models integrating iron biomarkers and SLC17A4 expression achieved high discriminative performance (AUC = 0.828-0.849) and demonstrated good calibration and net clinical benefit according to calibration and decision curve analyses, supporting their potential clinical applicability.
TSAT confers protective effects in CRS, and SLC17A4 represents a promising biomarker and therapeutic target. The integrative strategy combining causal inference, transcriptomic validation, molecular docking, and machine learning modeling links iron homeostasis to CRS pathophysiology and demonstrates translational potential through clinically applicable predictive models.
A two-sample Mendelian randomization (MR) analysis was conducted using large-scale GWAS summary statistics for four iron-related traits and three nasal inflammatory diseases. Significant SNPs were mapped to proximal genes and analyzed via Gene Ontology (GO), KEGG pathway enrichment, and protein-protein interaction (PPI) network construction. Candidate gene expression was validated using the GSE69093 transcriptomic dataset and qRT-PCR in nasal mucosal tissues from CRS patients and healthy controls. Molecular docking simulations were performed to assess ligand interactions, and clinical association and machine learning models were applied to evaluate diagnostic relevance and predictive performance.
MR analysis identified transferrin saturation (TSAT) as a causal protective factor for CRS (OR = 0.9988, P = 0.014). Thirty-one genes were mapped from MR-associated SNPs, with SLC17A4 highlighted as a key candidate gene. Enrichment analysis indicated involvement in iron metabolism and inflammatory regulation. SLC17A4 expression was significantly downregulated in both GSE69093 and clinical qRT-PCR samples. TSAT and SLC17A4 levels showed strong inverse correlations with Lund-Mackay and SNOT-22 scores. Molecular docking identified Troglitazone as a strong-binding ligand to SLC17A4 (-10.0 kcal/mol). Machine learning models integrating iron biomarkers and SLC17A4 expression achieved high discriminative performance (AUC = 0.828-0.849) and demonstrated good calibration and net clinical benefit according to calibration and decision curve analyses, supporting their potential clinical applicability.
TSAT confers protective effects in CRS, and SLC17A4 represents a promising biomarker and therapeutic target. The integrative strategy combining causal inference, transcriptomic validation, molecular docking, and machine learning modeling links iron homeostasis to CRS pathophysiology and demonstrates translational potential through clinically applicable predictive models.