Diagnostic Biomarkers and Immune Infiltration in Patients With T Cell-Mediated Rejection After Kidney Transplantation.
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ABSTRACT:
SUBMITTER: Zhou H
PROVIDER: S-EPMC8764245 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundT cell-mediated rejection is an important factor affecting early transplant kidney survival. Ferroptosis has been shown to play a pathogenic role in a variety of diseases, which was not reported in TCMR. Here we developed a model for assessing activation of ferroptosis-related genes in TCMR to find a better screening method and explore the contribution of ferroptosis in TCMR.MethodsWe performed unsupervised consensus clustering according to expression of ferroptosis-related genes based on RNA-seq data from kidney transplant biopsies, and developed an assessment model characterized by ferroptosis gene expression through PCA, which was evaluated in multiple external datasets as well as blood and urine samples. Pathway enrichment and immune cell infiltration analysis were used to explore the possible targets and pathways involved in ferroptosis and TCMR.ResultsA ferroptosis gene expression scoring model was established. The diagnostic specificity and sensitivity of TCMR in renal biopsy samples were both over 80%, AUC = 0.843, and AUC was around 0.8 in multi-dataset validation, and was also close to 0.7 in blood and urine samples, while in predicting of graft survival at 3 years, scoring model had a good prognostic effect as well. Pathway enrichment and PPI network speculated that TLR4, CD44, IFNG, etc. may be the key genes of ferroptosis in TCMR.ConclusionsFerroptosis scoring model could better diagnose TCMR and predict graft loss, and could be used as a potential screening method in blood and urine samples. We speculate that ferroptosis plays an important role in TCMR.
| S-EPMC9850582 | biostudies-literature
Project description:Despite the advances in immunosuppressive medications, antibody-mediated rejection (AMR) continues to be a major cause of kidney allograft failure and remains a barrier to improving long-term allograft survival. Recently, there have been significant advances in the understanding of the pathophysiological process of AMR, along with the development of new therapeutic options. Additionally, surveillance protocols with donor-derived cell-free DNA and gene profile testing have been established, leading to the early detection of AMR. A multitude of clinical trials are ongoing, opening numerous opportunities for improving outcome in kidney transplant recipients. In this brief review, we discuss the emerging therapies for managing both active and chronic active AMR and highlight the ongoing clinical trials.
| S-EPMC10419906 | biostudies-literature
Project description:The aim of this study was to determine how the Banff antibody-mediated rejection (ABMR) classification for kidney transplantation is interpreted in practice and affects therapy. The Banff Antibody-Mediated Injury Workgroup electronically surveyed clinicians and pathologists worldwide regarding diagnosis and treatment for 6 case-based scenarios. The participants' (95 clinicians and 72 renal pathologists) assigned diagnoses were compared to the Banff intended diagnoses (reference standard). The assigned diagnoses and reference standard differed by 26.1% (SD 28.1%) for pathologists and 34.5% (SD 23.3%) for clinicians. The greatest discordance between the reference standard and clinicians' diagnosis was when histologic features of ABMR were present but donor-specific antibody was undetected (49.4% [43/87]). For pathologists, the greatest discordance was in the case of acute/active ABMR C4d staining negative in a positive crossmatch transplant recipient (33.8% [23/68]). Treatment approaches were heterogeneous but linked to the assigned diagnosis. When acute/active ABMR was diagnosed by the clinician, treatment was recommended 95.3% (SD 18.4%) of the time vs only 77.7% (SD 39.2%) of the time when chronic active ABMR was diagnosed (P < .0001). In conclusion, the Banff ABMR classification is vulnerable to misinterpretation, which potentially has patient management implications. Continued efforts are needed to improve the understanding and standardized application of ABMR classification in the transplant community.
| S-EPMC6309659 | biostudies-literature
Project description:Background: Chronic kidney disease (CKD), characterized by sustained inflammation and immune dysfunction, is highly prevalent and can eventually progress to end-stage kidney disease. However, there is still a lack of effective and reliable diagnostic markers and therapeutic targets for CKD. Methods: First, we merged data from GEO microarrays (GSE104948 and GSE116626) to identify differentially expressed genes (DEGs) in CKD and healthy patient samples. Then, we conducted GO, KEGG, HPO, and WGCNA analyses to explore potential functions of DEGs and select clinically significant modules. Moreover, STRING was used to analyse protein-protein interactions. CytoHubba and MCODE algorithms in the cytoscape plug-in were performed to screen hub genes in the network. We then determined the diagnostic significance of the obtained hub genes by ROC and two validation datasets. Meanwhile, the expression level of the biomarkers was verified by IHC. Furthermore, we examined immunological cells' relationships with hub genes. Finally, GSEA was conducted to determine the biological functions that biomarkers are significantly enriched. STITCH and AutoDock Vina were used to predict and validate drug-gene interactions. Results: A total of 657 DEGs were screened and functional analysis emphasizes their important role in inflammatory responses and immunomodulation in CKD. Through WGCNA, the interaction network, ROC curves, and validation set, four hub genes (IL10RA, CD45, CTSS, and C1QA) were identified. Furthermore, IHC of CKD patients confirmed the results above. Immune infiltration analysis indicated that CKD had a significant increase in monocytes, M0 macrophages, and M1 macrophages but a decrease in regulatory T cells, activated dendritic cells, and so on. Moreover, four hub genes were statistically correlated with them. Further analysis exhibited that IL10RA, which obtained the highest expression level in hub genes, was involved in abnormalities in various immune cells and regulated a large number of immune system responses and inflammation-related pathways. In addition, the drug-gene interaction network contained four potential therapeutic drugs targeting IL10RA, and molecular docking might make this relationship viable. Conclusion: IL10RA and its related hub molecules might play a key role in the development of CKD and could be potential biomarkers in CKD.
| S-EPMC9838551 | biostudies-literature
Project description:An increased frequency of kidney rejection has been reported in diabetic patients who have simultaneous pancreas and kidney transplantation compared with patients who have a kidney transplant alone. Kidney graft outcome is similar in the two groups. The mechanism for increased kidney graft rejection with a simultaneous pancreas graft is not clear. It is ascribed to the immunogenicity of the exocrine pancreas that initiates migration of activated cells from the peripheral blood that are entrapped in the kidney. Since the volume of the transplanted tissue is less in islet transplantation (usually < 2 ml) than in pancreas transplantation, one might not expect an increased frequency of kidney rejection in islet cell recipients. We looked at biopsy-proven kidney rejection episodes in patients who had combined kidney and islet transplants and compared this with the frequency of rejection in diabetic and nondiabetic patients who underwent a kidney transplant alone under the same immunosuppression. Diabetic patients who had kidney islet transplants (n = 9) had a higher frequency of rejection (100%) compared with diabetic patients (n = 107, 55.1%) and nondiabetic patients (n = 327, 65%) who had a kidney transplant alone. The 1-year graft and patient survival rates were not different among the groups. Although the number of patients is small, it would appear that transplantation of a low volume of islet cells with high purity can lead to an increased frequency of kidney rejection. This is unlikely to be explained solely on the basis of fewer antigen matches in these recipients but may reflect the inherent immunogenicity of the purified islet preparations. Alternatively, there may be an effect of their direct infusion into the portal vein.
| S-EPMC2952505 | biostudies-literature
Project description:BackgroundImmune infiltrates in the tumor microenvironment have established roles in tumor growth, invasion, and metastasis. However, the diagnostic and prognostic potential of immune cell signature in esophageal squamous cell carcinoma (ESCC) remains unclear.ResultsThe proportions of 22 subsets of immune cells from 331 samples including 205 ESCC and 126 normal esophageal mucosa retrieved from TCGA, GEO, and GTEx databases were deciphered by CIBERSORT. Nine overlapping subsets of immune cells were identified as important features for discrimination of ESCC from normal tissue in the training cohort by LASSO and Boruta algorithms. A diagnostic immune score (DIS) developed by XGBoost showed high specificities and sensitivities in the training cohort, the internal validation cohort, and the external validation cohort (AUC: 0.999, 0.813, and 0.966, respectively). Furthermore, the prognostic immune score (PIS) was developed based on naive B cells and plasma cells using Cox proportional hazards model. The PIS, an independent prognostic predictor, classified patients with ESCC into low- and high-risk subgroups in the internal validation cohort (P = 0.038) and the external validation cohort (P = 0.022). In addition, a nomogram model comprising age, N stage, TNM stage, and PIS was constructed and performed excellent (HR = 4.17, 95% CI: 2.22-7.69, P < 0.0001) in all ESCC patients, with a time-dependent 5-year AUC of 0.745 (95% CI: 0.644 to 0.845), compared with PIS or TNM stage as a prognostic model alone.ConclusionOur DIS, PIS, and nomogram models based on infiltrated immune features may aid diagnosis and survival prediction for patients with ESCC.
| S-EPMC9251101 | biostudies-literature
Project description:PurposeBasal cell carcinoma (BCC) is a frequent tumor of the surface layer of skin or its accessories, and ranks first among the prevalence of skin cancer cases. However, its pathogenesis remains unclear. The purpose of this analysis was to scientifically evaluate the role of mRNAs in the occurrence and progression of BCC and further elucidate their underlying potential molecular mechanisms of action.MethodsDifferentially expression genes (DEGs) between nineteen BCC cases and five controls which initiate from the GSE103439 and GSE7553 datasets were identified and the transcriptome sequencing information was obtained. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses and Gene Ontology (GO) annotation were performed. Logistic regression (LASSO) and support vector machine (SVM) analyses were performed to identify candidate biomarkers obtained from protein-protein interaction (PPI) analysis. The tumor microenvironment comprising hub genes in BCC was investigated by immune infiltration analysis. The expression of two representative hub genes (KIF23 and NCAPG) was measured by qRT-PCR. Finally, the potential miRNAs and lncRNAs related to the hub genes were analyzed on relevant websites to obtain a ceRNA interaction network.ResultsTwenty-seven DEGs were identified. Fifteen hub genes were screened in the protein-protein interaction network. These showed marked enrichment in the cell cycle and p53 signaling pathway. FGF20, KIF23, and NCAPG were identified as the diagnostic markers of BCC. Immune cell infiltration analysis suggested their significant association with T cells CD4 memory activated, macrophages M1, and natural killer (NK) resting cells. Two miRNAs and twelve lncRNAs were used to construct the lncRNA-miRNA-mRNA ceRNA network.ConclusionFGF20, KIF23, and NCAPG are potential diagnostic markers of BCC. Our findings may shed new light on the molecular mechanisms underlying BCC occurrence.
| S-EPMC9758983 | biostudies-literature
Project description:Objective: The present study was designed to identify potential diagnostic markers for acute myocardial infarction (AMI) and determine the significance of immune cell infiltration in this pathology. Methods: Two publicly available gene expression profiles (GSE66360 and GSE48060 datasets) from human AMI and control samples were downloaded from the GEO database. Differentially expressed genes (DEGs) were screened between 80 AMI and 71 control samples. The LASSO regression model and support vector machine recursive feature elimination (SVM-RFE) analysis were performed to identify candidate biomarkers. The area under the receiver operating characteristic curve (AUC) value was obtained and used to evaluate discriminatory ability. The expression level and diagnostic value of the biomarkers in AMI were further validated in the GSE60993 dataset (17 AMI patients and 7 controls). The compositional patterns of the 22 types of immune cell fraction in AMI were estimated based on the merged cohorts using CIBERSORT. Results: A total of 27 genes were identified. The identified DEGs were mainly involved in carbohydrate binding, Kawasaki disease, atherosclerosis, and arteriosclerotic cardiovascular disease. Gene sets related to atherosclerosis signaling, primary immunodeficiency, IL-17, and TNF signaling pathways were differentially activated in AMI compared with the control. IL1R2, IRAK3, and THBD were identified as diagnostic markers of AMI (AUC = 0.877) and validated in the GSE60993 dataset (AUC = 0.941). Immune cell infiltration analysis revealed that IL1R2, IRAK3, and THBD were correlated with M2 macrophages, neutrophils, monocytes, CD4+ resting memory T cells, activated natural killer (NK) cells, and gamma delta T cells. Conclusion: IL1R2, IRAK3, and THBD can be used as diagnostic markers of AMI, and can provide new insights for future studies on the occurrence and the molecular mechanisms of AMI.
| S-EPMC7644926 | biostudies-literature
Project description:BackgroundDermatomyositis (DM) is a rare autoimmune disease characterized by severe muscle dysfunction, and the immune response of the muscles plays an important role in the development of DM. Currently, the diagnosis of DM relies on symptoms, physical examination, and biopsy techniques. Therefore, we used machine learning algorithm to screen key genes, and constructed and verified a diagnostic model composed of 5 key genes. In terms of immunity, The relationship between 5 genes and immune cell infiltration in muscle samples was analyzed. These diagnostic and immune-cell-related genes may contribute to the diagnosis and treatment of DM.MethodsGSE5370 and GSE128470 datasets were utilised from the Gene Expression Omnibus database as DM test sets. And we also used R software to merge two datasets and to analyze the results of differentially expressed genes (DEGs) and functional correlation analysis. Then, we could detect diagnostic genes adopting least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine recursive feature elimination (SVM-RFE) analyses. The validity of putative biomarkers was assessed using the GSE1551 dataset, and we confirmed the area under the receiver operating characteristic curve (AUC) values. Finally, CIBERSORT was used to evaluate immune cell infiltration in DM muscles and the correlations between disease-related biomarkers and immune cells.ResultsIn this study, a total of 414 DEGs were screened. ISG15, TNFRSF1A, GUSBP11, SERPINB1 and PTMA were identified as potential DM diagnostic biomarkers(AUC > 0.85),and the expressions of 5 genes in DM group were higher than that in healthy group (p < 0.05). Immune cell infiltration analyses indicated that identified DM diagnostic biomarkers may be associated with M1 macrophages, activated NK cells, Tfh cells, resting NK cells and Treg cells.ConclusionThe study identified that ISG15, TNFRSF1A, GUSBP11, SERPINB1 and PTMA as potential diagnostic biomarkers of DM and these genes were closely correlated with immune cell infiltration.This will contribute to future studies in diagnosis and treatment of DM.
| S-EPMC9889851 | biostudies-literature
Project description:BackgroundCrohn's disease (CD) has an increasing incidence and prevalence worldwide. It is currently believed that both the onset and progression of the disease are closely related to immune system imbalance and the infiltration of immune cells. The aim of this study was to investigate the molecular immune mechanisms associated with CD and its fibrosis through bioinformatics analysis.MethodsThree datasets from the Gene Expression Omnibus data base (GEO) were downloaded for data analysis and validation. Single sample gene enrichment analysis (ssGSEA) was used to evaluate the infiltration of immune cells in CD samples. Immune cell types with significant differences were identified by Wilcoxon test and Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Differentially expressed genes (DEGs) were screened and then subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional correlation analysis, as well as protein-protein interaction (PPI) network analysis. The cytoHubba program and the GSE75214 dataset were used to screen for hub genes and plot Receiver operating characteristic (ROC)curves to screen for possible biomarkers of CD based on diagnostic efficacy. The hub genes of CD were correlated with five significantly different immune cells. In addition, validation was performed by real time quantitative PCR (RT-qPCR) experiments in colonic tissue of CD intestinal fibrosis rats to further identify hub genes that are more related to CD intestinal fibrosis.ResultsThe DEGs were analyzed separately by 10 algorithms and narrowed down to 9 DEGs after taking the intersection. 4 hub genes were further screened by the GSE75214 validation set, namely COL1A1, CXCL10, MMP2 and FGF2. COL1A1 has the highest specificity and sensitivity for the diagnosis of CD and is considered to have the potential to diagnose CD. Five immune cells with significant differences were screened between CD and health controls (HC). Through the correlation analysis between five kinds of immune cells and four biomarkers, it was found that CXCL10 was positively correlated with activated dendritic cells, effector memory CD8+ T cells. MMP2 was positively correlated with activated dendritic cells, gamma delta T cells (γδ T) and mast cells. MMP2 and COL1A1 were significantly increased in colon tissue of CD fibrosis rats.ConclusionMMP2, COL1A1, CXCL10 and FGF2 can be used as hub genes for CD. Among them, COL1A1 can be used as a biomarker for the diagnosis of CD. MMP2 and CXCL10 may be involved in the development and progression of CD by regulating activated dendritic cell, effector memory CD8+ T cell, γδ T cell and mast cell. In addition, MMP2 and COL1A1 may be more closely related to CD intestinal fibrosis.
| S-EPMC10445157 | biostudies-literature