Identification and Validation of Liver Transplantation-Induced Acute Lung Injury Biomarkers Using a Bioinformatics...

Area of Science:

• Biomarkers
• Genomics
• Transplantation Immunology

Background:

• Acute lung injury (ALI) is a severe complication after liver transplantation (LT), impacting patient survival.
• Current diagnostic methods for post-LT ALI lack effective early detection biomarkers.
• Identifying reliable biomarkers is crucial for timely intervention and improved outcomes.

Purpose of the Study:

• To identify hub biomarkers and key signaling pathways in post-LT ALI using bioinformatics and machine learning.
• To validate the diagnostic potential of identified biomarkers for early detection of ALI after LT.
• To construct predictive models and networks for understanding ALI pathogenesis.

Main Methods:

• Differential gene expression analysis and functional enrichment analyses (NF-kB, TNF pathways).
• Machine-learning algorithms and network construction (transcription factor-hub gene, ceRNA).
• Single-cell RNA sequencing (scRNA-seq) and enzyme-linked immunosorbent assay (ELISA) for validation.

Main Results:

• CXCL3, CD48, and IRAK3 were identified as hub biomarkers for post-LT ALI.
• Machine learning models and a diagnostic nomogram confirmed their predictive value.
• scRNA-seq and ELISA validated significant alterations in biomarker expression and protein levels.

Conclusions:

• CXCL3, CD48, and IRAK3 are promising novel biomarkers for predicting post-LT ALI.
• These biomarkers can aid in early diagnosis, prevention, and targeted therapies.
• Findings support improved management strategies for patients undergoing liver transplantation.