Similar Datasets
Project description:Formalin-fixed, paraffin-embedded (FFPE) tissues are an underused resource for molecular analyses. This proof of concept study aimed to compare RNAseq results from FFPE biopsies with the corresponding RNAlater® (Qiagen, Germany) stored samples from clear cell renal cell carcinoma (ccRCC) patients, to enable the decision of whether or not it is feasible to apply RNAseq technology to archival tissue. Core biopsies were obtained with a 16g needle from 16 patients undergoing partial or full nephrectomy. The RNA sequencing libraries were generated applying the new Illumina TruSeq® Access library preparation protocol.
2016-01-08 | GSE76207 | GEO
Project description:Histologic findings on 1-year biopsies such as inflammation with fibrosis and transplant glomerulopathy predict renal allograft loss by 5 years. However, almost half of the patients with graft loss have a 1-year biopsy that is either normal or has only interstitial fibrosis. The goal of this study was to determine if there was a gene expression profile in these relatively normal 1-year biopsies that predicted subsequent decline in renal function. Using transcriptome microarrays we measured intragraft mRNA levels in a retrospective Discovery cohort (170 patients with a normal/minimal fibrosis 1-year biopsy, 54 with progressive decline in function/graft loss and 116 with stable function) and developed a nested 10-fold cross-validated gene classifier that predicted progressive decline in renal function (positive predictive value=38±34%%; negative predictive value=73±30%, c-statistic=0.59). In a prospective, multicenter Validation cohort (270 patients with Normal/Interstitial Fibrosis [IF]), the classifier had a 20% positive predictive value, 85% negative predictive value and 0.58 c-statistic. Importantly, the majority of patients with graft loss in the prospective study had 1-year biopsies scored as Normal or IF. We conclude predicting graft loss in many renal allograft recipients (i.e. those with a relatively normal 1-year biopsy and eGFR >40) remains difficult.
2021-08-31 | GSE181757 | GEO
Project description:Kidney transplants that develop dysfunction or proteinuria after one year post transplant are at considerable risk for progression to renal failure. Identifying the molecules associated with graft failure could potentially lead to interventions that would slow the progression of organ failure. We analyzed the relationship between gene expression in late biopsies for cause in human kidney transplants and subsequent graft loss, and assessed the predictive value of gene expression. We evaluated the performance of these genes in an independent , and in a population of early biopsies that have a very low risk of subsequent graft failure. All consenting renal transplant patients undergoing biopsies for cause as standard of care between 09/2004 and 10/2007 at the university of Alberta or between 11/2006 and 02/2007 at the University of Illinois were included in the analysis. In addition, biopsies obtained from Minnesota between 09/2006 and 09/2007 were used as an independent . In addition to the cores required for standard histopathology, we collected one core for gene expression studies. the relationship between gene expression in the biopsy and subsequent graft loss was analyzed. This dataset is part of the TransQST collection.
2010-05-24 | E-GEOD-21374 | biostudies-arrayexpress
Project description:This dataset complements GSE76207. Formalin-fixed, paraffin-embedded (FFPE) tissues are an underused resource for molecular analyses. This proof of concept study aimed to compare RNAseq results from FFPE biopsies (Eikrem et al., PLoS One. 2016 Feb 22;11(2):e0149743. doi: 10.1371/journal.pone.0149743. eCollection 2016. PMID: 26901863) with FFPE confirmation dataset from clear cell renal cell carcinoma (ccRCC) patients. Core biopsies were obtained with a 16g needle from 16 patients undergoing partial or full nephrectomy. The RNA sequencing libraries were generated applying the new Illumina TruSeq® Access library preparation protocol. The goal of the study is to compare the discovery and the confirmation data and to assess the utilization of the data for ccRCC biomarker development.
2016-11-21 | GSE82122 | GEO
Project description:Specific early diagnosis of renal allograft rejection is gaining importance in the current trend to minimize and individualize immunosuppression. Gene expression analyses could contribute significantly by defining âmolecular Banffâ signatures. Several previous studies have applied transcriptomics to distinguish different classes of kidney biopsies. However, the heterogeneity of microarray platforms, clinical samples and data analysis methods complicates the identification of robust signatures for the different types and grades of rejection. To address these issues, a comparative meta-analysis was performed across five different microarray datasets of heterogeneous sample collections from two published clinical datasets and three own datasets including biopsies for clinical indications, protocol biopsies, as well as comparative samples from non-human primates (NHP). This work identified conserved gene expression signatures that can differentiate groups with different histopathological findings in both human and NHP, regardless of the technical platform used. The marker panels comprise genes that clearly support the biological changes known to be involved in allograft rejection. A characteristic dynamic expression change of genes associated with immune and kidney functions was observed across samples with different grades of CAN. In addition, differences between human and NHP rejection were essentially limited to genes reflecting interstitial fibrosis progression. This data set comprises all renal allograft biopsies for clinical indications from patients at Hôpital Tenon, Paris (February 2003 until September 2004) and few respective patients from Hôpital Bicêtre, Paris, Hôpital Pellegrin, Bordeaux, and Hôpital Dupuytren, Limoges, plus control normal kidney samples from Hôpital Tenon, Paris, France (first batch). We used microarrays to identify different gene expression signatures of renal allograft biopsies that can classify them according to different types of allograft rejection or CAN. Experiment Overall Design: 47 renal allograft core biopsies for clinical indications with different histopathological diagnoses according to BANFF'97 criteria, and 13 normal kidney samples as controls.
2009-02-28 | E-GEOD-9489 | biostudies-arrayexpress
Project description:Specific early diagnosis of renal allograft rejection is gaining importance in the current trend to minimize and individualize immunosuppression. Gene expression analyses could contribute significantly by defining “molecular Banff” signatures. Several previous studies have applied transcriptomics to distinguish different classes of kidney biopsies. However, the heterogeneity of microarray platforms, clinical samples and data analysis methods complicates the identification of robust signatures for the different types and grades of rejection. To address these issues, a comparative meta-analysis was performed across five different microarray datasets of heterogeneous sample collections from two published clinical datasets and three own datasets including biopsies for clinical indications, protocol biopsies, as well as comparative samples from non-human primates (NHP). This work identified conserved gene expression signatures that can differentiate groups with different histopathological findings in both human and NHP, regardless of the technical platform used. The marker panels comprise genes that clearly support the biological changes known to be involved in allograft rejection. A characteristic dynamic expression change of genes associated with immune and kidney functions was observed across samples with different grades of CAN. In addition, differences between human and NHP rejection were essentially limited to genes reflecting interstitial fibrosis progression. This data set comprises all renal allograft biopsies for clinical indications from patients at Hôpital Tenon, Paris (February 2003 until September 2004) and few respective patients from Hôpital Bicêtre, Paris, Hôpital Pellegrin, Bordeaux, and Hôpital Dupuytren, Limoges, plus control normal kidney samples from Hôpital Tenon, Paris, France (first batch). We used microarrays to identify different gene expression signatures of renal allograft biopsies that can classify them according to different types of allograft rejection or CAN. Keywords: disease state analysis
2009-02-24 | GSE9489 | GEO
Project description:Glomerular abnormalities have been demonstrated in kidney biopsies of patient after orthotopic liver transplantation (OLT). We hypothesize that these changes exist prior to OLT and may play a role in the development of renal failure after OLT. We use gene expression microarrays to investigate the mechanism of kidney disease in patients listed for OLT. Gene expression profiles of biopsies of cirrhotic patients were compared with pre-implantation living donor biopsies. Glomerular abnormalities were seen in 92% of the biopsies, the most common being increase in mesangial matrix. Electron microscope showed effacement of podocytes (93%) and duplication (35%) and widening of glomerular basement membrane (45%). Gene Ontology analysis revealed significant up-regulation of genes implicated in immune response, including T-cell, leucocyte and platelet activation and differentiation. Pathogenesis-based transcripts analysis revealed significantly increased expression of cytotoxic T-cell, macrophage, B-cell, natural killer cell, and endothelial cell associated transcripts, indicating an ongoing inflammatory immune response.
2013-09-17 | GSE50892 | GEO
Project description:Formalin-fixed, paraffin-embedded (FFPE) tissues are an underused resource for molecular analyses. This proof of concept study aimed to compare RNAseq results from FFPE biopsies with the corresponding RNAlater® (Qiagen, Germany) stored samples from clear cell renal cell carcinoma (ccRCC) patients, to enable the decision of whether or not it is feasible to apply RNAseq technology to archival tissue. Core biopsies were obtained with a 16g needle from 16 patients undergoing partial or full nephrectomy. The RNA sequencing libraries were generated applying the new Illumina TruSeq® Access library preparation protocol. Comparative analysis was done using voom/Limma R-package. The analysis of the FFPE and RNAlater® datasets yielded similar numbers of detected genes, differentially expressed transcripts and affected pathways. The FFPE and RNAlater datasets shared 80% (n=1106) differentially expressed genes. The average expression of these transcripts in both datasets correlated with R2=0.97, and the log2 fold changes of these transcripts, which are significantly altered in both datasets, correlated with R2=0.96. Among transcripts with the highest fold changes in both datasets were carbonic anhydrase 9 (CA9), neuronal pentraxin-2 (NPTX2) and uromodulin (UMOD) that were confirmed by immunohistochemistry. IPA revealed the presence of gene signatures of cancer and nephrotoxicity, renal damage and immune response. To simulate the feasibility of clinical biomarker studies with FFPE samples, a classifier model was developed for the FFPE dataset: expression data for CA9 alone had an accuracy, specificity and sensitivity of 94%, respectively, and achieved similar performance in the RNAlater dataset. Transforming growth factor-Ã1 (TGFB1)-regulated genes, epithelial to mesenchymal transition (EMT) and NOTCH signaling cascade may support novel therapeutic strategies. In conclusion, in this proof of concept study, RNAseq data obtained from FFPE kidney biopsies are comparable to data obtained from fresh stored material, thereby expanding the utility of archival tissue specimens. *** Access to raw data can be requested from submitters ***
2016-01-08 | E-GEOD-76207 | biostudies-arrayexpress
Project description:Kidney transplants that develop dysfunction or proteinuria after one year post transplant are at considerable risk for progression to renal failure. Identifying the molecules associated with graft failure could potentially lead to interventions that would slow the progression of organ failure. We analyzed the relationship between gene expression in late biopsies for cause in human kidney transplants and subsequent graft loss, and assessed the predictive value of gene expression. We evaluated the performance of these genes in an independent , and in a population of early biopsies that have a very low risk of subsequent graft failure.
2010-05-24 | GSE21374 | GEO
Project description:Specific early diagnosis of renal allograft rejection is gaining importance in the current trend to minimize and individualize immunosuppression. Gene expression analyses could contribute significantly by defining M-bM-^@M-^\molecular BanffM-bM-^@M-^] signatures. Several previous studies have applied transcriptomics to distinguish different classes of kidney biopsies. However, the heterogeneity of microarray platforms, clinical samples and data analysis methods complicates the identification of robust signatures for the different types and grades of rejection. To address these issues, a comparative meta-analysis was performed across five different microarray datasets of heterogeneous sample collections from two published clinical datasets and three own datasets including biopsies for clinical indications, protocol biopsies, as well as comparative samples from non-human primates (NHP). This work identified conserved gene expression signatures that can differentiate groups with different histopathological findings in both human and NHP, regardless of the technical platform used. The marker panels comprise genes that clearly support the biological changes known to be involved in allograft rejection. A characteristic dynamic expression change of genes associated with immune and kidney functions was observed across samples with different grades of CAN. In addition, differences between human and NHP rejection were essentially limited to genes reflecting interstitial fibrosis progression. This data set comprises all renal allograft biopsies for clinical indications from patients at HM-CM-4pital Tenon, Paris (February 2003 until September 2004) and few respective patients from HM-CM-4pital BicM-CM-*tre, Paris, HM-CM-4pital Pellegrin, Bordeaux, and HM-CM-4pital Dupuytren, Limoges, plus control normal kidney samples from HM-CM-4pital Tenon, Paris, France (first batch). We used microarrays to identify different gene expression signatures of renal allograft biopsies that can classify them according to different types of allograft rejection or CAN. Keywords: disease state analysis Keywords: Expression profiling by array 16 renal allograft core biopsies for clinical indications with different histopathological diagnoses according to BANFF'97 criteria (additional samples associated with GSE9489)
2009-08-27 | E-GEOD-17861 | biostudies-arrayexpress