Identification of glycolysis-related gene signatures for prognosis and therapeutic targeting in idiopathic pulmonary fibrosis.

Background: Glycolysis plays a crucial role in fibrosis, but the specific genes involved in glycolysis in idiopathic pulmonary fibrosis (IPF) are not well understood. Methods: Three IPF gene expression datasets were obtained from the Gene Expression Omnibus (GEO), while glycolysis-related genes were retrieved from the Molecular Signatures Database (MsigDB). Differentially expressed glycolysis-related genes (DEGRGs) were identified using the "limma" R package. Diagnostic glycolysis-related genes (GRGs) were selected through least absolute shrinkage and selection operator (LASSO) regression regression and support vector machine-recursive feature elimination (SVM-RFE). A prognostic signature was developed using LASSO regression, and time-dependent receiver operating characteristic (ROC) curves were generated to evaluate predictive performance. Single-cell RNA sequencing (scRNA-seq) data were analyzed to examine GRG expression across various cell types. Immune infiltration analysis, Gene Set Enrichment Analysis (GSEA), and Gene Set Variation Analysis (GSVA) were performed to elucidate potential molecular mechanisms. A bleomycin (BLM)-induced pulmonary fibrosis mouse model was used for experimental validation via reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Results: 14 GRGs ( Conclusion: This study identifies GRGs as potential prognostic biomarkers for IPF and highlights their role in modulating immune responses within the fibrotic lung microenvironment. Notably,