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<p>Acute rejection (AR) after kidney transplantation, is a common and serious complication that occurs when the recipient’s immune system attacks the graft, and the specific genes and molecular mechanisms underlying the role of mitophagy are still unclear. This study integrated two transcriptomic datasets (GSE129166 and GSE25902) from the GEO database. Thirty differential mitophagy-related genes were identified by intersecting differentially expressed genes, module genes obtained through weighted gene co-expression network analysis and mitophagy-related genes. Functional enrichment analysis uncovered several biological processes and signaling pathways associated with these genes. Four candidate genes including <i>CCND1</i>, <i>ZC3H15</i>, <i>RPL38</i>, and <i>ARPC4</i>, were further identified through Random Forest and Support Vector Machine with recursive feature elimination. Internal, external datasets and a nomogram confirmed they could effectively predict AR. Moreover, these genes significantly correlated with the infiltration of multiple immune cells. Differential expressions of the four genes were also validated in patient’s peripheral blood and AR mice. These four mitophagy-related genes may be novel biomarkers for predicting the occurrence and diagnosis of AR.</p>

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Identification of mitophagy-related genes with diagnostic value in acute rejection following kidney transplantation using bioinformatics analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月1日

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  • Jianan Ma,
  • Dawei Wu,
  • Xinyuan An,
  • Yongrui Zhang,
  • Haijian Wei

摘要

Acute rejection (AR) after kidney transplantation, is a common and serious complication that occurs when the recipient’s immune system attacks the graft, and the specific genes and molecular mechanisms underlying the role of mitophagy are still unclear. This study integrated two transcriptomic datasets (GSE129166 and GSE25902) from the GEO database. Thirty differential mitophagy-related genes were identified by intersecting differentially expressed genes, module genes obtained through weighted gene co-expression network analysis and mitophagy-related genes. Functional enrichment analysis uncovered several biological processes and signaling pathways associated with these genes. Four candidate genes including CCND1, ZC3H15, RPL38, and ARPC4, were further identified through Random Forest and Support Vector Machine with recursive feature elimination. Internal, external datasets and a nomogram confirmed they could effectively predict AR. Moreover, these genes significantly correlated with the infiltration of multiple immune cells. Differential expressions of the four genes were also validated in patient’s peripheral blood and AR mice. These four mitophagy-related genes may be novel biomarkers for predicting the occurrence and diagnosis of AR.