Gong Yu, Wang Yuan, Takeda Kazuyoshi, Hirota Saori, Maehara Yui, Okumura Ko, Uchida Koichiro
Center for Immune Therapeutics and Diagnosis, Advanced Research Institute for Health Science, Juntendo University, Tokyo, 113-0033, Japan.
Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, 277-0882, Japan.
Sci Rep. 2025 Aug 4;15(1):28391. doi: 10.1038/s41598-025-09780-8.
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.
由于传统方法只能检测正在发生的排斥反应，而这种反应有时可能已经对移植器官造成了不可逆转的损害，因此人们迫切需要新的方法来检测移植排斥反应。在此，我们将肝或肾移植前受者外周血单核细胞（PBMC）的转录组学数据库应用于加权基因共表达网络和机器学习模型，以评估排斥反应的风险。在肝移植中，与排斥反应呈正相关的基因簇富含与抗病毒反应以及白细胞介素-1（IL-1）调节/产生相关的基因；在肾移植中，与固有免疫反应（IL-8和Toll样受体信号通路）和T细胞反应相关的基因与排斥反应呈正相关。我们的研究提出了一种基于移植前收集的PBMC的RNA测序数据进行特征工程的新方法。从该方法中得出的特征在预测排斥反应风险方面显示出潜力，并可能在未来临床适用模型中作为候选预测指标。
