# Integrated analysis and validation of metabolism-related genes in lung transplantation-induced cold ischemia/ reperfusion injury

## TL;DR

This study identifies metabolism-related genes linked to lung transplant injury, offering potential biomarkers and therapeutic targets to reduce graft failure.

## Contribution

The study introduces a novel integration of bioinformatics and machine learning to identify metabolism-related genes in lung transplant ischemia-reperfusion injury.

## Key findings

- Nine metabolism-related genes were identified as key players in lung ischemia-reperfusion injury.
- Seven of these genes showed consistent results in both bioinformatics analysis and experimental validation.
- The identified genes may serve as biomarkers and therapeutic targets for reducing primary graft dysfunction.

## Abstract

Primary graft dysfunction (PGD) usually occurs within 72 hours after lung transplantation and is primarily caused by ischemia-reperfusion injury (IRI). Patients who develop PGD after lung transplantation tend to have a poor prognosis. However, effective clinical strategies to reduce the incidence of primary graft dysfunction remain limited. Therefore, a comprehensive understanding of the mechanisms underlying lung ischemia-reperfusion injury is essential for improving outcomes in lung transplant recipients.

In this study, we explored the differential expression of metabolism-related genes in lung transplantation induced IRI and identify its potential molecular mechanisms by bioinformatics analysis. Next, we used two machine learning algorithms and further screened for key genes in them. The outside dataset GSE8021 was used to validated the accuracy of the model established by metabolism-related genes machine learning genes. In addition, we observed the distribution and localization of metabolism-related machine learning genes in the single-cell dataset GSE220797 and analyzed the correlation between metabolism-related machine learning genes and immune cells by the CIBERSORT immune infiltration algorithm. Finally, we validated the nine metabolism-related machine learning genes by rat orthotopic left lung transplantation model and Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR), we found that seven of these metabolism-related machine learning genes were consistent with the results of the bioinformatics analysis.

We identified multiple metabolism-related genes machine learning genes (PDE4B, CDA, HMOX1, EHHADH, AMD1, GUCY1A1, GUCY1B1, UGCG, and FPGT). Significant changes were observed in some of these genes following ischemia-reperfusion. They represent important biomarkers in ischemia-reperfusion injury induced by lung transplantation and hold promise as therapeutic targets for mitigating lung ischemia-reperfusion injury and reducing the incidence of primary graft dysfunction.