JoVE - Journal of Visualized Experiments
JoVE Visualize
Contact Us

Bioinformatics-based analysis of the role of immune-related genes in acute rejection after kidney transplantation and renal cancer development

  • 0Department of Urology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
Medicine +

|

January 14, 2025

|

January 14, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Early diagnosis of acute rejection (AR) after kidney transplantation is crucial. This study identified four immune-related genes (CD1D, FPR2, FAM3C, HMOX1) as key biomarkers for AR diagnosis and potential therapeutic targets for AR and renal cancer.

Area Of Science

  • Immunology
  • Transplantation Medicine
  • Bioinformatics
  • Oncology

Background

  • Acute rejection (AR) is a frequent complication post-kidney transplantation, impacting long-term graft survival.
  • Both AR and tumor development involve complex immune cell and gene interactions, highlighting the need for early diagnostic markers.
  • Understanding the interplay between AR and tumorigenesis is critical for patient management.

Purpose Of The Study

  • To identify early diagnostic biomarkers for acute rejection (AR) following kidney transplantation using bioinformatics.
  • To explore the correlation between AR, immune-related genes, and the development of renal cancer.
  • To develop and evaluate machine learning-based diagnostic models for AR.

Main Methods

  • Differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) were performed on AR patient data.
  • Intersection analysis with immune-related genes, followed by Lasso regression and Boruta algorithm for gene selection.
  • Logistic regression and 10 machine learning methods were employed to construct and evaluate AR diagnostic models.

Main Results

  • Four feature genes (CD1D, FPR2, FAM3C, HMOX1) were identified as significantly associated with AR.
  • Logistic regression demonstrated superior performance in constructing the AR diagnostic model compared to other machine learning methods.
  • The gene FAM3C showed potential as a diagnostic biomarker for AR and was implicated in renal cancer development.

Conclusions

  • Immune-related genes are vital for the early diagnosis of AR in kidney transplant recipients.
  • The identified gene FAM3C represents a promising therapeutic target for both AR and renal cancer.
  • Logistic regression-based models offer an effective approach for AR diagnosis in clinical settings.