Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive condition with an unfavorable prognosis. A recent study has demonstrated that IPF patients exhibit characteristic alterations in the fatty acid metabolism in their lungs, suggesting an association with IPF pathogenesis. Therefore, in this study, we have explored whether the gene signature associated with fatty acid metabolism could be used as a reliable biological marker for predicting the survival of IPF patients.
Data on the fatty acid metabolism-related genes (FAMRGs) were extracted from databases like Kyoto Encyclopedia of Genes and Genomes (KEGG), Hallmark, and Reactome pathway. The GSE70866 dataset with information on IPF patients was retrieved from the Gene Expression Omnibus (GEO). Next, the consensus clustering method was used to identify novel molecular subgroups. Gene Set Enrichment Analysis (GSEA) was performed to understand the mechanisms involved. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to evaluate the level of immune cell infiltration in the identified subgroups based on gene expression signatures of immune cells. Finally, the Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate Cox regression analysis were performed to develop a prognostic risk model.
The gene expression signature associated with fatty acid metabolism was used to create two subgroups with significantly different prognoses. GSEA reveals that immune-related pathways were significantly altered between the two subgroups, and the two subgroups had different metabolic characteristics. High infiltration of immune cells, mainly activated NK cells, monocytes, and activated mast cells, was observed in the subgroup with a poor prognosis. A risk model based on FAMRGs had an excellent ability to predict the prognosis of IPF. The nomogram constructed using the clinical features and the risk model could accurately predict the prognosis of IPF patients.
The fatty acid metabolism-related gene expression signature could be used as a potential biological marker for predicting clinical outcomes and the level of infiltration of immune cells. This could eventually enhance the accuracy of the treatment of IPF patients.