# Prediction of the risk of transplant rejection based on RNA sequencing data of PBMCs before transplantation

## TL;DR

This study uses RNA sequencing of blood cells before organ transplants to predict the risk of rejection, offering a new early warning system.

## Contribution

A novel method for predicting transplant rejection risk using RNA-seq data and machine learning from pre-transplant PBMCs.

## Key findings

- Gene clusters linked to antiviral and IL-1 responses correlate with liver transplant rejection.
- Innate immune and T cell response genes correlate with kidney transplant rejection.
- RNA-seq-based features show potential for predicting rejection risk in future clinical models.

## Abstract

Novel methods for detecting transplant rejection are craved, since conventional methods can detect ongoing rejection that may sometimes have already caused irreversible damage in transplanted organs. Here, we applied a transcriptomics database of recipients’ peripheral blood mononuclear cells (PBMCs) before liver or kidney transplantation on the weighted gene co-expression network and machine learning models to evaluate the risk of rejection. Gene clusters positively correlated with rejection were enriched for genes related to antiviral response and regulation/production of interleukin-1(IL-1) in liver transplantation, and genes related to innate immune responses (IL-8 and toll-like receptor signaling pathways) and T cell responses were positively correlated with rejection in kidney transplantation. Our study presents a novel approach for feature engineering based on RNA-seq data of PBMCs collected before transplantation. The features derived from this method demonstrated potential in predicting the risk of rejection and may serve as candidate predictors in future clinically applicable models.

The online version contains supplementary material available at 10.1038/s41598-025-09780-8.