Similar Datasets
Project description:Histological features of acute rejection can be detected in surveillance biopsies despite stable graft function and can negatively impact graft outcomes. However, routine surveillance biopsies for detection of subclinical rejection are not generally performed due to potential risks and costs associated with repeated biopsies. Noninvasive biomarkers are required to facilitate early detection of acute rejection and borderline changes. We examined the impact of histological abnormalities at 3-month in surveillance biopsies on graft outcomes in kidney transplant recipients from the prospective Genomics of Chronic Allograft Rejection (GoCAR) study. We then performed RNA sequencing on whole blood collected at the time of biopsy in 88 patients (22 vs. 66) to identify transcripts associated with histological abnormalities. Subjects with subclinical ACR or borderline ACR at 3 month (ACR-3) had higher risk of subsequent clinical acute rejection at 12 and 24 month (P < 0.05), more rapid functional decline (P < 0.05) and a decreased graft survival in adjusted cox analysis (P < 0.01) than patients with no abnormalities on 3-month biopsy. We then identified a 17-gene signature in peripheral blood that accurately diagnosed ACR-3. The gene set was then validated for diagnosis of ACR-3 using microarray data (N=65; 12 vs. 53; 26 overlapping with the RNAseq cohort).
2019-09-11 | GSE120397 | GEO
Project description:Histological features of acute rejection can be detected in surveillance biopsies despite stable graft function and can negatively impact graft outcomes. However, routine surveillance biopsies for detection of subclinical rejection are not generally performed due to potential risks and costs associated with repeated biopsies. Noninvasive biomarkers are required to facilitate early detection of acute rejection and borderline changes. We examined the impact of histological abnormalities at 3-month in surveillance biopsies on graft outcomes in kidney transplant recipients from the prospective Genomics of Chronic Allograft Rejection (GoCAR) study. We then performed RNA sequencing on whole blood collected at the time of biopsy in 88 patients (22 vs. 66) to identify transcripts associated with histological abnormalities. Subjects with subclinical ACR or borderline ACR at 3 month (ACR-3) had higher risk of subsequent clinical acute rejection at 12 and 24 month (P < 0.05), more rapid functional decline (P < 0.05) and a decreased graft survival in adjusted cox analysis (P < 0.01) than patients with no abnormalities on 3-month biopsy. We then identified a 17-gene signature in peripheral blood that accurately diagnosed ACR-3.
2019-09-11 | GSE120396 | GEO
Project description:A peripheral blood gene expression signature diagnoses subclinical acute rejection
| PRJNA492955 | ENA
Project description:BackgroundIn kidney transplant recipients, surveillance biopsies can reveal, despite stable graft function, histologic features of acute rejection and borderline changes that are associated with undesirable graft outcomes. Noninvasive biomarkers of subclinical acute rejection are needed to avoid the risks and costs associated with repeated biopsies.MethodsWe examined subclinical histologic and functional changes in kidney transplant recipients from the prospective Genomics of Chronic Allograft Rejection (GoCAR) study who underwent surveillance biopsies over 2 years, identifying those with subclinical or borderline acute cellular rejection (ACR) at 3 months (ACR-3) post-transplant. We performed RNA sequencing on whole blood collected from 88 individuals at the time of 3-month surveillance biopsy to identify transcripts associated with ACR-3, developed a novel sequencing-based targeted expression assay, and validated this gene signature in an independent cohort.ResultsStudy participants with ACR-3 had significantly higher risk than those without ACR-3 of subsequent clinical acute rejection at 12 and 24 months, faster decline in graft function, and decreased graft survival in adjusted Cox analysis. We identified a 17-gene signature in peripheral blood that accurately diagnosed ACR-3, and validated it using microarray expression profiles of blood samples from 65 transplant recipients in the GoCAR cohort and three public microarray datasets. In an independent cohort of 110 transplant recipients, tests of the targeted expression assay on the basis of the 17-gene set showed that it identified individuals at higher risk of ongoing acute rejection and future graft loss.ConclusionsOur targeted expression assay enabled noninvasive diagnosis of subclinical acute rejection and inflammation in the graft and may represent a useful tool to risk-stratify kidney transplant recipients.
| S-EPMC6683710 | biostudies-literature
Project description:A peripheral blood gene expression signature diagnoses subclinical acute rejection [validation]
| PRJNA492957 | ENA
Project description:A peripheral blood gene expression signature diagnoses subclinical acute rejection [discovery]
| PRJNA492956 | ENA
Project description:This SuperSeries is composed of the SubSeries listed below.
2018-07-24 | GSE107509 | GEO
Project description:BackgroundSubclinical acute rejection (subAR) can only be diagnosed by protocol biopsy and is correlated with worse graft outcomes. However, noninvasive biomarkers of subAR are lacked for kidney transplantation recipients in clinic. This study aims to utilize to construct a peripheral blood-based gene signature for subAR diagnosis after kidney transplantation.MethodsAfter systematically screening databases, two cohorts of high quality with 3-month blood profiles and biopsy-proven graft status from the Gene Expression Omnibus databases were employed as training and validation cohorts. Then, the support vector machine recursive feature elimination (SVM-RFE) and the least absolute shrinkage and selection operator (LASSO) logistic regression were used to identify key biomarkers for subAR. Subsequently, the stepwise logistic regression method was applied to construct a gene signature for subAR in the training cohort. Patients were divided into high-risk and low-risk groups based on the cutoff point identified by the receiver operating characteristic (ROC) curve. Then, the signature was validated in a validation cohort with fixed formula. The single-sample gene set enrichment analysis was used to estimate immune cells in the blood.ResultsFifty key biomarkers were filtered out with the machine learning algorithms. Then, a novel six-gene signature was constructed using the LASSO and stepwise logistic regression method. The signature had high accuracy in both training [area under the curve (AUC) =0.923] and validation cohort (AUC =0.855). Additionally, these six genes were found to have significant and consistent relationships with blood immune cells in both cohorts, especially for T cells subtypes.ConclusionsWe developed and validated a novel noninvasive six-gene signature based on peripheral blood to diagnose subAR, which offered a potential tool for clinical practice. The six-gene signature offered a potential method to monitor patients following transplantation and make a timely intervention.
| S-EPMC9641058 | biostudies-literature
Project description:Commonly available clinical parameters fail to predict early acute cellular rejection (EAR, occurring within 6 months after transplant), a major risk factor for graft loss after kidney transplantation. We performed whole-blood RNA sequencing at the time of transplant in 235 kidney transplant recipients enrolled in a prospective cohort study (Genomics of Chronic Allograft Rejection [GoCAR]) and evaluated the relationship of pretransplant transcriptomic profiles with EAR. EAR was associated with downregulation of NK and CD8+ T cell gene signatures in pretransplant blood. We identified a 23-gene set that predicted EAR in the discovery (n = 81, and AUC = 0.80) and validation (n = 74, and AUC = 0.74) sets. Exclusion of recipients with 5 or 6 HLA donor mismatches increased the AUC to 0.89. The risk score derived from the gene set was also significantly associated with acute cellular rejection after 6 months, antibody-mediated rejection and/or de novo donor-specific antibodies, and graft loss in a cohort of 154 patients, combining the validation set and additional GoCAR patients with surveillance biopsies between 6 and 24 months (n = 80) posttransplant. This 23-gene set is a potentially important new tool for determination of the recipient's immunological risk before kidney transplantation, and facilitation of an individualized approach to immunosuppressive therapy.
| S-EPMC6629121 | biostudies-literature
Project description:Background: Acute rejection (AR) remains a major issue that negatively impacts long-term allograft survival in renal transplantation. The current study aims to apply machine learning methods to develop a non-invasive diagnostic test for AR based on gene signature in peripheral blood. Methods: We collected blood gene expression profiles of 251 renal transplant patients with biopsy-proven renal status from three independent cohorts in the Gene Expression Omnibus database. After differential expression analysis and machine learning algorithms, selected biomarkers were applied to the least absolute shrinkage and selection operator (LASSO) logistic regression to construct a diagnostic model in the training cohort. The diagnostic ability of the model was further tested in validation cohorts. Gene set enrichment analysis and immune cell assessment were also conducted for further investigation. Results: A novel diagnostic model based on three genes (TSEN15, CAPRIN1 and PRR34-AS1) was constructed in the training cohort (AUC = 0.968) and successfully verified in the validation cohort (AUC = 0.925) with high accuracy. Moreover, the diagnostic model also showed a promising value in discriminating T cell-mediated rejection (TCMR) (AUC = 0.786). Functional enrichment analysis and immune cell evaluation demonstrated that the AR model was significantly correlated with adaptive immunity, especially T cell subsets and dendritic cells. Conclusion: We identified and validated a novel three-gene diagnostic model with high accuracy for AR in renal transplant patients, and the model also performed well in distinguishing TCMR. The current study provided a promising tool to be used as a precise and cost-effective non-invasive test in clinical practice.
| S-EPMC8129004 | biostudies-literature