An Integrated Machine Learning Framework for Developing and Validating a Diagnostic Model of Hub Genes Related to Lipid Metabolism in Chronic Rhinosinusitis - Xiong P; Liu L; Pi J; Wang J; Lu T; Ke X; Jiang Y; Shen Y; Yang Y (Journal of Inflammation Research)
Panhui Xiong,1,* Lei Liu,1,2,* Jingting Pi,3,* Ji Wang,1 Tao Lu,1 Xia Ke,1 Yu Jiang,1 Yang Shen,1 Yucheng Yang1 1Department of Otolaryngology Head and Neck Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People’s Republic of China; 2Department of Otolaryngology Head and Neck Surgery, Mianyang Central Hospital, Mianyang, 621000, People’s Republic of China; 3Department of Otolaryngology Head and Neck Surgery, Yongchuan Hospital Affiliated of Chongqing Medical University, Chongqing, 402160, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yucheng Yang, Department of Otolaryngology Head and Neck Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People’s Republic of China, Email yychxh@163.com Yang Shen, Department of Otolaryngology Head and Neck Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People’s Republic of China, Email sy_smile@sina.cnPurpose: 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.Keywords: chronic rhinosinusitis, lipid metabolism, hub gene, machine learning, diagnostic model, drug