Prediction of the risk of transplant rejection based on RNA sequencing data of PBMCs before transplantation
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 cel...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-09780-8 |
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| author | Yu Gong Yuan Wang Kazuyoshi Takeda Saori Hirota Yui Maehara Ko Okumura Koichiro Uchida |
| author_facet | Yu Gong Yuan Wang Kazuyoshi Takeda Saori Hirota Yui Maehara Ko Okumura Koichiro Uchida |
| author_sort | Yu Gong |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-dff714a0a21147a9ac694d228c64fa07 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-dff714a0a21147a9ac694d228c64fa072025-08-20T03:04:31ZengNature PortfolioScientific Reports2045-23222025-08-0115111210.1038/s41598-025-09780-8Prediction of the risk of transplant rejection based on RNA sequencing data of PBMCs before transplantationYu Gong0Yuan Wang1Kazuyoshi Takeda2Saori Hirota3Yui Maehara4Ko Okumura5Koichiro Uchida6Center for Immune Therapeutics and Diagnosis, Advanced Research Institute for Health Science, Juntendo UniversityDepartment of Integrated Biosciences, Graduate School of Frontier Sciences, The University of TokyoCenter for Immune Therapeutics and Diagnosis, Advanced Research Institute for Health Science, Juntendo UniversityCenter for Immune Therapeutics and Diagnosis, Advanced Research Institute for Health Science, Juntendo UniversityCenter for Immune Therapeutics and Diagnosis, Advanced Research Institute for Health Science, Juntendo UniversityCenter for Immune Therapeutics and Diagnosis, Advanced Research Institute for Health Science, Juntendo UniversityCenter for Immune Therapeutics and Diagnosis, Advanced Research Institute for Health Science, Juntendo UniversityAbstract 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.https://doi.org/10.1038/s41598-025-09780-8Liver transplantationKidney transplantationRejectionPeripheral blood mononuclear cellsRNA sequencing |
| spellingShingle | Yu Gong Yuan Wang Kazuyoshi Takeda Saori Hirota Yui Maehara Ko Okumura Koichiro Uchida Prediction of the risk of transplant rejection based on RNA sequencing data of PBMCs before transplantation Scientific Reports Liver transplantation Kidney transplantation Rejection Peripheral blood mononuclear cells RNA sequencing |
| title | Prediction of the risk of transplant rejection based on RNA sequencing data of PBMCs before transplantation |
| title_full | Prediction of the risk of transplant rejection based on RNA sequencing data of PBMCs before transplantation |
| title_fullStr | Prediction of the risk of transplant rejection based on RNA sequencing data of PBMCs before transplantation |
| title_full_unstemmed | Prediction of the risk of transplant rejection based on RNA sequencing data of PBMCs before transplantation |
| title_short | Prediction of the risk of transplant rejection based on RNA sequencing data of PBMCs before transplantation |
| title_sort | prediction of the risk of transplant rejection based on rna sequencing data of pbmcs before transplantation |
| topic | Liver transplantation Kidney transplantation Rejection Peripheral blood mononuclear cells RNA sequencing |
| url | https://doi.org/10.1038/s41598-025-09780-8 |
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