An Integrated Machine Learning Framework for Developing and Validating a Diagnostic Model of Hub Genes Related to Lipid Metabolism in Chronic Rhinosinusitis

Purpose: The study aimed to identify key genes related to lipid metabolism in chronic sinusitis and understand their biological implications, considering the growing interest in the association between chronic sinusitis - a complex inflammatory condition - and lipid metabolism due to lipids' role in inflammation and immunity.

Methods: Gene expression data from bulk - RNA sequence was analyzed and intersected with lipid metabolism genes and WGCNA module genes from the MSigDB database. Immune infiltration analysis was conducted. Machine learning techniques were used to develop a diagnostic model. qRT - PCR and immunofluorescence techniques were employed to confirm gene involvement. Potential targeted drugs were identified through relevant analyses.

Results: 41 hub genes were identified, which were involved in pathways like G protein - coupled receptor signaling, TGF - beta receptor signaling, and responses to oxidative stress and nitrogen compounds. Enrichment analyses suggested links to ubiquitin - mediated proteolysis, mTOR signaling, and MAPK signaling. A significant presence of immune cells was detected in the chronic sinusitis group. A combined RF+Stepglm model was developed, comprising six genes (KPNA3, RAB35, GLE1, RNF139, OSMR, and PDPK1), which demonstrated good diagnostic performance (AUC = 0.848). Potential targeted drugs such as Raloxifene and Hesperidin were identified. qRT - PCR and immunofluorescence confirmed that the expression levels of RAB35, GLE1, and OSMR were significantly higher in CRS samples compared to normal ones.

Conclusion: This research highlights the role of lipid metabolism in chronic sinusitis and provides a basis for the development of targeted therapies.

chronic rhinosinusitis; diagnostic model; drug; hub gene; lipid metabolism; machine learning.