Diagnostic Biomarkers and Immune Infiltration in Patients With T Cell-Mediated Rejection After Kidney Transplantation

Zhou, Hai; Lü, Hongmei; Sun, Lingyun; Wang, Zijie; Zheng, Ming; Zhou, Honglan; Zhang, Dongliang; Tan, Ruoyun; Gu, Min

doi:10.3389/fimmu.2021.774321

Cited by 4 publications

(2 citation statements)

References 48 publications

(46 reference statements)

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“…The results of GO biological process terms indicated that the DEGs were involved in T cell activation, mononuclear cell differentiation, regulation of cell-cell adhesion, and lymphocyte differentiation, consistent with the previous enrichment results of TCMR by Zhou et.al (19). The KEGG results mainly enriched the DEGs with multiple autoimmune diseases and various T cellrelated pathways, involving differentiation of T helper cells and the T cell receptor signaling pathway.…”

Section: Discussionsupporting

confidence: 89%

Identification of PDCD1 as a potential biomarker in acute rejection after kidney transplantation via comprehensive bioinformatic analysis

Wang

Lin²,

Wang³

et al. 2023

Front. Immunol.

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BackgroundAcute rejection is a determinant of prognosis following kidney transplantation. It is essential to search for novel noninvasive biomarkers for early diagnosis and prompt treatment.MethodsGene microarray data was downloaded from the Gene Expression Omnibus (GEO) expression profile database and the intersected differentially expressed genes (DEGs) was calculated. We conducted the DEGs with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Distribution of immune cell infiltration was calculated by CIBERSORT. A hub gene marker was identified by intersecting the rejection-related genes from WGCNA and a selected KEGG pathway—T cell receptor signaling pathway (hsa04660), and building a protein-protein interaction network using the STRING database and Cytoscape software. We performed flow-cytometry analysis to validate the hub gene.ResultsA total of 1450 integrated DEGs were obtained from five datasets (GSE1563, GSE174020, GSE98320, GSE36059, GSE25902). The GO, KEGG and immune infiltration analysis results showed that AR was mainly associated with T cell activation and various T-cell related pathways. Other immune cells, such as B cells, Macrophage and Dendritic cells were also associated with the progress. After utilizing the WGCNA and PPI network, PDCD1 was identified as the hub gene. The flow-cytometry analysis demonstrated that both in CD4+ and CD8+ T cells, PD1+CD57-, an exhausted T cell phenotype, were downregulated in the acute rejection whole blood samples.ConclusionsOur study illustrated that PDCD1 may be a candidate diagnostic biomarker for acute kidney transplant rejection via integrative bioinformatic analysis.

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Section: Discussionsupporting

confidence: 89%

Identification of PDCD1 as a potential biomarker in acute rejection after kidney transplantation via comprehensive bioinformatic analysis

Wang

Lin²,

Wang³

et al. 2023

Front. Immunol.

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“… 40 , 41 Wang et al performed WGCNA and immune infiltration analysis and revealed that PDCD1 is involved in T cell activation, monocyte differentiation, cell-cell adhesion regulation, and lymphocyte differentiation, which was consistent with the enrichment results of Zhou et al The authors identified PDCD1 as a diagnostic biomarker for acute renal rejection. 42 , 43 PDCD1 is involved in the pathogenesis of various kidney diseases. Uremia is the outcome of various CKDs.…”

Section: Discussionmentioning

confidence: 99%

Identification of Immune-Related Genes as Biomarkers for Uremia

Lyu,

He,

Zhou

et al. 2023

IJGM

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Purpose Uremia, which is characterized by immunodeficiency, is associated with the deterioration of kidney function. Immune-related genes (IRGs) are crucial for uremia progression. Methods The co-expression network was constructed to identify key modular genes associated with uremia. IRGs were intersected with differentially expressed genes (DEGs) between uremia and control groups and key modular genes to obtain differentially expressed IRGs (DEIRGs). DEIRGs were subjected to functional enrichment analysis. The protein-protein interaction (PPI) network was constructed. The candidate genes were identified using the cytoHubba tool. The biomarkers were identified using various machine learning algorithms. The diagnostic value of the biomarkers was evaluated using receiver operating characteristic (ROC) analysis. The immune infiltration analysis was implemented. The biological pathways of biomarkers were identified using gene set enrichment analysis and ingenuity pathway analysis. The mRNA expression of biomarkers was validated using blood samples of patients with uremia and healthy subjects with quantitative real-time polymerase chain reaction (qRT-PCR). Results In total, four biomarkers ( PDCD1, NGF, PDGFRB , and ZAP70 ) were identified by machine learning methods. ROC analysis demonstrated that the area under the curve values of individual biomarkers were > 0.9, indicating good diagnostic power. The nomogram model of biomarkers exhibited good predictive power. The proportions of six immune cells significantly varied between the uremia and control groups. ZAP70 expression was positively correlated with the proportions of resting natural killer (NK) cells, naïve B cells, and regulatory T cells. Functional enrichment analysis revealed that the biomarkers were mainly associated with translational function and neuroactive ligand-receptor interaction. ZAP70 regulated NK cell signaling. The PDCD1 and NGF expression levels determined using qRT-PCR were consistent with those determined using bioinformatics analysis. Conclusion PDCD1, NGF, PDGFRB , and ZAP70 were identified as biomarkers for uremia, providing a theoretical foundation for uremia diagnosis.

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Unveiling Spatial Immune Cell Profile in Kidney Allograft Rejections Using 36-plex Immunofluorescence Imaging

Hirai,

Kondo,

Shimizu

et al. 2024

Transplantation

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Background. Kidney allograft rejections are orchestrated by a variety of immune cells. Because of the complex histopathologic features, accurate pathological diagnosis poses challenges even for expert pathologists. The objective of this study was to unveil novel spatial indices associated with transplant rejection by using a spatial bioinformatic approach using 36-plex immunofluorescence image data. Methods. The image obtained from 11 T cell-mediated rejection (TCMR) and 12 antibody-mediated rejection (AMR) samples were segmented into 753 737 single cells using DeepCell’s Mesmer algorithm. These cells were categorized into 13 distinct cell types through unsupervised clustering based on their biomarker expression profiles. Cell neighborhood analysis allowed us to stratify kidney tissue into 8 distinct neighborhood components consisting of unique cell type enrichment profiles. Results. In contrast to TCMR samples, AMR samples exhibited a higher frequency of neighborhood components that were characterized by an enrichment of CD31+ endothelial cells. Although the overall frequency of CD68+ macrophages in AMR samples was not significantly high, CD68+ macrophages within endothelial cell-rich lesions exhibited a significantly higher frequency in AMR samples than TCMR samples. Furthermore, the frequency of interactions between CD31+ cells and CD68+ cells was significantly increased in AMR samples, implying the pivotal role of macrophages in AMR pathogenesis. Importantly, patients demonstrating a high frequency of CD31:CD68 interactions experienced significantly poorer outcomes in terms of chronic AMR progression. Conclusions. Collectively, these data indicate the potential of spatial bioinformatic as a valuable tool for aiding in pathological diagnosis and for uncovering new insights into the mechanisms underlying transplant rejection.

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Diagnostic Biomarkers and Immune Infiltration in Patients With T Cell-Mediated Rejection After Kidney Transplantation

Cited by 4 publications

References 48 publications

Identification of PDCD1 as a potential biomarker in acute rejection after kidney transplantation via comprehensive bioinformatic analysis

Identification of PDCD1 as a potential biomarker in acute rejection after kidney transplantation via comprehensive bioinformatic analysis

Identification of Immune-Related Genes as Biomarkers for Uremia

Unveiling Spatial Immune Cell Profile in Kidney Allograft Rejections Using 36-plex Immunofluorescence Imaging

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