A prognostic model based on clusters of molecules related to epithelial–mesenchymal transition for idiopathic pulmonary fibrosis - Zhao, Jiarui; Wang, Can; Fan, Rui; Liu, Xiangyang; Zhang, Wei (Frontiers in Genetics, 2023)
Background: Most patients with idiopathic pulmonary fibrosis (IPF) have poor prognosis; Effective predictive models for these patients are currently lacking. Epithelial–mesenchymal transition (EMT) often occurs during idiopathic pulmonary fibrosis development, and is closely related to multiple pathways and biological processes. It is thus necessary for clinicians to find prognostic biomarkers with high accuracy and specificity from the perspective of Epithelial–mesenchymal transition. Methods: Data were obtained from the Gene Expression Omnibus database. Using consensus clustering, patients were grouped based on Epithelial–mesenchymal transition-related genes. Next, functional enrichment analysis was performed on the results of consensus clustering using gene set variation analysis. The gene modules associated with Epithelial–mesenchymal transition were obtained through weighted gene co-expression network analysis. Prognosis-related genes were screened via least absolute shrinkage and selection operator (LASSO) regression analysis. The model was then evaluated and validated using survival analysis and time-dependent receiver operating characteristic (ROC) analysis. Results: A total of 239 Epithelial–mesenchymal transition-related genes were obtained from patients with idiopathic pulmonary fibrosis. Six genes with strong prognostic associations (C-X-C chemokine receptor type 7 [CXCR7], heparan sulfate-glucosamine 3-sulfotransferase 1 [HS3ST1], matrix metallopeptidase 25 [MMP25], murine retrovirus integration site 1 [MRVI1], transmembrane four L6 family member 1 [TM4SF1], and tyrosylprotein sulfotransferase 1 [TPST1]) were identified via least absolute shrinkage and selection operator and Cox regression analyses. A prognostic model was then constructed based on the selected genes. Survival analysis showed that patients with high-risk scores had worse prognosis based on the training set [hazard ratio (HR) = 7.31, p < .001] and validation set (HR = 2.85, p = .017). The time-dependent receiver operating characteristic analysis showed that the area under the curve (AUC) values in the training set were .872, .905, and .868 for 1-, 2-, and 3-year overall survival rates, respectively. Moreover, the area under the curve values in the validation set were .814, .814, and .808 for 1-, 2-, and 3-year overall survival rates, respectively. Conclusion: The independent prognostic model constructed from six Epithelial–mesenchymal transition-related genes provides bioinformatics guidance to identify additional prognostic markers for idiopathic pulmonary fibrosis in the future.