Establishment and validation of a prognostic model for idiopathic pulmonary fibrosis based on mitochondrial-related genes.

Abstract

Background: The prognosis for patients diagnosed with idiopathic pulmonary fibrosis (IPF) is exceedingly grim, and there are currently no pharmacological interventions available that effectively reduce mortality rates. Emerging evidence underscores the intimate connection between mitochondrial dysfunction and the onset and advancement of IPF. However, there remains a scarcity of prognostic models for assessing the risk associated with mitochondrial-related genes in IPF. This study aims to develop a comprehensive prognostic model for IPF that incorporates mitochondrial-related genes to enhance risk assessment and guide clinical decision-making.

Methods: Two IPF-related microarray expression profiling datasets (GSE28042 and GSE70866) accompanied with survival data were acquired from the Gene Expression Omnibus (GEO) database. The "limma" R package was used to identify differentially expressed mitochondrial-related genes between normal samples and IPF samples. The prognostic model was constructed using univariate Cox regression, the least absolute shrinkage and selection operator (LASSO) Cox regression analyses, and multivariate Cox regression analysis. Multivariate independent prognostic analysis was conducted to ascertain whether the risk score could serve as an independent prognostic factor for predicting clinicopathological outcomes. A nomogram was employed to forecast the survival probability of IPF patients, providing valuable support for clinical decision-making processes. The CIBERSORT algorithm was utilized to examine discrepancies in immune cell infiltration within the model. The expression of genes screened from the prognostic model was validated in external data sets and western blot assays.

Results: We developed a prognostic model for mitochondrial-related risks, incorporating ARMCX2 and ACOT11, and subsequently validated its predictive efficacy in the validation set. The IPF samples were stratified into high-risk and low-risk groups based on the median of the risk score. According to Kaplan-Meier curve analysis, the high-risk group exhibited inferior outcomes compared to the low-risk group. The time-dependent receiver operating characteristic (ROC) analysis demonstrated the accurate prognostic capability of the risk model for IPF. A nomogram, accompanied by calibration curves, was presented to predict 1-, 2-, and 3-year survival in IPF patients. The risk model we employed not only unveiled significant disparities in functional enrichment between the high-risk and low-risk groups, but also demonstrated a robust correlation with the infiltration of specific immune cells.

Conclusions: In this study, the mitochondrial-related prognostic model incorporating ARMCX2 and ACOT11 demonstrates potential clinical utility for informing decision-making in IPF patients and offers valuable insights for future therapeutic interventions.