Wang Y, Peng X. Bioinformatics analysis characterizes immune infiltration landscape and identifies potential blood biomarkers for heart transplantation.
Transpl Immunol 2024;
84:102036. [PMID:
38499050 DOI:
10.1016/j.trim.2024.102036]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 03/15/2024] [Accepted: 03/15/2024] [Indexed: 03/20/2024]
Abstract
BACKGROUND
Cardiac allograft rejection (AR) remains a significant complication following heart transplantation. The primary objective of our study is to gain a comprehensive understanding of the fundamental mechanisms involved in AR and identify possible therapeutic targets.
METHODS
We acquired the GSE87301 dataset from the Gene Expression Omnibus database. In GSE87301, a comparison was conducted on blood samples from patients with and without cardiac allograft rejection (AR and NAR) to detect differentially expressed genes (DEGs). Enrichment analysis was conducted to identify the pathways that show significant enrichment during AR. Machine learning techniques, including the least absolute shrinkage and selection operator regression (LASSO) and random forest (RF) algorithms, were employed to identify potential genes for the diagnosis of AR. The diagnostic value was evaluated using a nomogram and receiver operating characteristic (ROC) curve. Additionally, immune cell infiltration was analyzed to explore any dysregulation of immune cells in AR.
RESULTS
A total of 114 DEGs were identified from the GSE87301 dataset. These DEGs were mainly found to be enriched in pathways related to the immune system. To identify the signature genes, the LASSO and RF algorithms were used, and four genes, namely ALAS2, HBD, EPB42, and FECH, were identified. The performance of these signature genes was evaluated using the receiver operating characteristic curve (ROC) analysis, which showed that the area under the curve (AUC) values for ALAS2, HBD, EPB42, and FECH were 0.906, 0.881, 0.900, and 0.856, respectively. These findings were further confirmed in the independent datasets and clinical samples. The selection of these specific genes was made to construct a nomogram, which demonstrated excellent diagnostic ability. Additionally, the results of the single-sample gene set enrichment analysis (ssGSEA) revealed that these genes may be involved in immune cell infiltration.
CONCLUSION
We identified four signature genes (ALAS2, HBD, EPB42, and FECH) as potential peripheral blood diagnostic candidates for AR diagnosis. Additionally, a nomogram was constructed to aid in the diagnosis of heart transplantation. This study offers valuable insights into the identification of candidate genes for heart transplantation using peripheral blood samples.
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