Landscape of the immune infiltration and identification of molecular diagnostic markers associated with immune cells in patients with kidney transplantation
Abstract Rejection seriously affects the success of kidney transplantations. However, the molecular mechanisms underlying this rejection remain unclear. The GSE21374 and GSE36059 datasets were downloaded from the Gene Expression Omnibus (GEO) database. Next, the Cell-type Identification by Estimatin...
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Nature Portfolio
2024-10-01
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| Online Access: | https://doi.org/10.1038/s41598-024-75052-6 |
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| author | Zhangxiao Xu Xun Sun Xiaobo Ma Bo Tao Jian Wu Yunpeng He Yuan Zhao Hexiang Mao Jie Yang Dehui Jiang Lijun Wang Chao Song |
| author_facet | Zhangxiao Xu Xun Sun Xiaobo Ma Bo Tao Jian Wu Yunpeng He Yuan Zhao Hexiang Mao Jie Yang Dehui Jiang Lijun Wang Chao Song |
| author_sort | Zhangxiao Xu |
| collection | DOAJ |
| description | Abstract Rejection seriously affects the success of kidney transplantations. However, the molecular mechanisms underlying this rejection remain unclear. The GSE21374 and GSE36059 datasets were downloaded from the Gene Expression Omnibus (GEO) database. Next, the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to infer the proportions of 22 immune cells. Moreover, infiltrating immune cell-related genes were identified using weighted gene co-expression network analysis (WGCNA), and enrichment analysis was conducted to observe their biological functions. Extreme Gradient Boosting (XGBoost) and Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression algorithms were used to screen hub genes. Quantitative real-time PCR was conducted to verify the number of immune cells and hub gene expression levels. The rejection and non-rejection groups showed significantly different distributions (P < 0.05) of eight immune cells (B cell memory, Plasma cells, mast cells, follicular helper T cells, T CD8 cells, Macrophages M1, T Cells CD4 memory activated, and gamma delta T cells). Subsequently, CD8A, CRTAM, GBP2, WARS, and VAMP5 were screened as hub genes using the XGBoost and LASSO algorithms and could be used as diagnostic biomarkers. Finally, differential analysis and quantitative real-time PCR suggested that CD8A, CRTAM, GBP2, WARS, and VAMP5 were upregulated in rejection samples compared to non-rejection samples. The present study identified five key infiltrating immune cell-related genes (CD8A, CRTAM, GBP2,WARS, and VAMP5) involved in kidney transplant rejection, which may explain the molecular mechanism of rejection in kidney transplantation development. |
| format | Article |
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| issn | 2045-2322 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-4739f70d023e4ab78104912d9cdab2ed2025-08-20T02:16:40ZengNature PortfolioScientific Reports2045-23222024-10-0114111610.1038/s41598-024-75052-6Landscape of the immune infiltration and identification of molecular diagnostic markers associated with immune cells in patients with kidney transplantationZhangxiao Xu0Xun Sun1Xiaobo Ma2Bo Tao3Jian Wu4Yunpeng He5Yuan Zhao6Hexiang Mao7Jie Yang8Dehui Jiang9Lijun Wang10Chao Song11Faculty of Life Science and Technology & The affiliated Anning First People’s Hospital, Kunming University of Science and TechnologyThe Department of Urology, Kunming First People’s Hospital, Affiliated Calmette Hospital of Kunming Medical UniversityDepartment of Laboratory Medicine, The First Affiliated Hospital of Kunming Medical UniversityFaculty of Life Science and Technology & The affiliated Anning First People’s Hospital, Kunming University of Science and TechnologyFaculty of Life Science and Technology & The affiliated Anning First People’s Hospital, Kunming University of Science and TechnologyFaculty of Life Science and Technology & The affiliated Anning First People’s Hospital, Kunming University of Science and TechnologyFaculty of Life Science and Technology & The affiliated Anning First People’s Hospital, Kunming University of Science and TechnologyFaculty of Life Science and Technology & The affiliated Anning First People’s Hospital, Kunming University of Science and TechnologyFaculty of Life Science and Technology & The affiliated Anning First People’s Hospital, Kunming University of Science and TechnologyFaculty of Life Science and Technology & The affiliated Anning First People’s Hospital, Kunming University of Science and TechnologyFaculty of Life Science and Technology & The affiliated Anning First People’s Hospital, Kunming University of Science and TechnologyFaculty of Life Science and Technology & The affiliated Anning First People’s Hospital, Kunming University of Science and TechnologyAbstract Rejection seriously affects the success of kidney transplantations. However, the molecular mechanisms underlying this rejection remain unclear. The GSE21374 and GSE36059 datasets were downloaded from the Gene Expression Omnibus (GEO) database. Next, the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to infer the proportions of 22 immune cells. Moreover, infiltrating immune cell-related genes were identified using weighted gene co-expression network analysis (WGCNA), and enrichment analysis was conducted to observe their biological functions. Extreme Gradient Boosting (XGBoost) and Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression algorithms were used to screen hub genes. Quantitative real-time PCR was conducted to verify the number of immune cells and hub gene expression levels. The rejection and non-rejection groups showed significantly different distributions (P < 0.05) of eight immune cells (B cell memory, Plasma cells, mast cells, follicular helper T cells, T CD8 cells, Macrophages M1, T Cells CD4 memory activated, and gamma delta T cells). Subsequently, CD8A, CRTAM, GBP2, WARS, and VAMP5 were screened as hub genes using the XGBoost and LASSO algorithms and could be used as diagnostic biomarkers. Finally, differential analysis and quantitative real-time PCR suggested that CD8A, CRTAM, GBP2, WARS, and VAMP5 were upregulated in rejection samples compared to non-rejection samples. The present study identified five key infiltrating immune cell-related genes (CD8A, CRTAM, GBP2,WARS, and VAMP5) involved in kidney transplant rejection, which may explain the molecular mechanism of rejection in kidney transplantation development.https://doi.org/10.1038/s41598-024-75052-6Kidney transplantationRejectionImmune cellsGEOImmune infiltration landscapeMolecular diagnostic markers |
| spellingShingle | Zhangxiao Xu Xun Sun Xiaobo Ma Bo Tao Jian Wu Yunpeng He Yuan Zhao Hexiang Mao Jie Yang Dehui Jiang Lijun Wang Chao Song Landscape of the immune infiltration and identification of molecular diagnostic markers associated with immune cells in patients with kidney transplantation Scientific Reports Kidney transplantation Rejection Immune cells GEO Immune infiltration landscape Molecular diagnostic markers |
| title | Landscape of the immune infiltration and identification of molecular diagnostic markers associated with immune cells in patients with kidney transplantation |
| title_full | Landscape of the immune infiltration and identification of molecular diagnostic markers associated with immune cells in patients with kidney transplantation |
| title_fullStr | Landscape of the immune infiltration and identification of molecular diagnostic markers associated with immune cells in patients with kidney transplantation |
| title_full_unstemmed | Landscape of the immune infiltration and identification of molecular diagnostic markers associated with immune cells in patients with kidney transplantation |
| title_short | Landscape of the immune infiltration and identification of molecular diagnostic markers associated with immune cells in patients with kidney transplantation |
| title_sort | landscape of the immune infiltration and identification of molecular diagnostic markers associated with immune cells in patients with kidney transplantation |
| topic | Kidney transplantation Rejection Immune cells GEO Immune infiltration landscape Molecular diagnostic markers |
| url | https://doi.org/10.1038/s41598-024-75052-6 |
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