Identification and analysis of endoplasmic reticulum stress-related biomarkers in chronic sinusitis with nasal polyps using bioinformatics approaches

Chronic rhinosinusitis with nasal polyps (CRSwNP) is a prevalent inflammatory disease of the sinuses that significantly diminishes patients' quality of life. Endoplasmic reticulum stress (ERS) is strongly associated with the initiation and progression of numerous inflammatory diseases. This study aimed to identify ERS-related hub genes in CRSwNP using bioinformatics approaches. We combined the GSE136825 and GSE179265 datasets. Subsequently, we performed differential expression analysis and weighted gene co-expression network analysis. The genes identified were then cross-referenced with those associated with ERS, allowing us to identify ERS-related differentially expressed genes in CRSwNP. We employed 4 machine learning algorithms: support vector machine, random forest, extreme gradient boosting, and least absolute shrinkage and selection operator. These methods facilitated the identification of hub genes, which were then used to construct a nomogram for disease prediction. Twenty-eight genes related to ERS showed significant expression differences (P < .05). By overlapping the genes selected from 4 machine learning algorithms, we identified 3 hub ERS-differentially expressed genes: ATP2A3, ACTC1, and DES. The nomogram prediction model built on these pivotal genes demonstrated strong predictive performance. This study offers new insights into the molecular mechanisms of CRSwNP and suggests potential new avenues for early diagnosis and targeted treatment in future clinical practice.