Developing an IPF Prognostic Model and Screening for Key Genes Based on Cold Exposure-Related Genes Using Bioinformatics Approaches.

Cold exposure has an impact on various respiratory diseases. However, its relationship with idiopathic pulmonary fibrosis (IPF) remains to be elucidated. In this study, bioinformatics methods were utilized to explore the potential link between cold exposure and IPF. Cold exposure-related genes (CERGs) were identified using RNA-Seq data from mice exposed to cold versus room temperature conditions, along with cross-species orthologous gene conversion. Consensus clustering analysis was performed based on the CERGs. A prognostic model was established using univariate and multivariate risk analyses, as well as Lasso-Cox analysis. Differential analysis, WGCNA, and Lasso-Cox methods were employed to screen for signature genes. This study identified 151 CERGs. Clustering analysis based on these CERGs revealed that IPF patients could be divided into two subgroups with differing severity levels. Significant differences were observed between these two subgroups in terms of hypoxia score, EMT score, GAP score, immune infiltration patterns, and mortality rates. A nine-gene prognostic model for IPF was established based on the CERG (AUC: 1 year: 0.81, 3 years: 0.79, 5 years: 0.91), which outperformed the GAP score (AUC: 1 year: 0.66, 3 years: 0.75, 5 years: 0.72) in prognostic accuracy. IPF patients were classified into high-risk and low-risk groups based on the RiskScore from the prognostic model, with significant differences observed between these groups in hypoxia score, EMT score, GAP score, immune infiltration patterns, and mortality rates. Ultimately, six high-risk signature genes associated with cold exposure in IPF were identified: GASK1B, HRK1, HTRA1, KCNN4, MMP9, and SPP1. This study suggests that cold exposure may be a potential environmental factor contributing to the progression of IPF. The prognostic model built upon cold exposure-related genes provides an effective tool for assessing the severity of IPF patients. Meanwhile, GASK1B, HRK1, HTRA1, KCNN4, MMP9, and SPP1 hold promise as potential biomarkers and therapeutic targets for IPF.