Homo sapiens
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ABSTRACT:
PROVIDER: PRJNA123865 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:This SuperSeries is composed of the following subset Series: GSE18916: Expression data from 42 prostate cancer samples - 16 recurrent and 26 recurrence-free GSE18917: Expression data from 22 prostate cancer samples - 6 recurrent and 16 recurrence-free from the validation dataset Refer to individual Series
2011-06-15 | E-GEOD-18918 | biostudies-arrayexpress
Project description:We analyzed the protein-coding and non-coding gene expression profiles of 64 samples of prostate cancer primary tumors. All samples were collected between 1998 and 2001 with informed consent from patients subjected to radical prostatectomy at Hospital Sirio-Libanes in São Paulo. Selected patients were identified with clinical Stage T1-2 prostate cancer and no lymph node involvement, and received no adjuvant treatment after surgery as long as they remained recurrence-free. Biochemical recurrence was defined as an increase in patient blood PSA level to 0.2 ng per mL of blood at any time during the 5-year follow-up after prostatectomy. For this kind of experiment, also called self-self hybridization, the microarrays were cohybridized with each of Cy3- and Cy5-labeled cRNA replicates. This strategy has been used to derive intensity-dependent cutoffs to classify a gene as differentially expressed or divergent in comparative genomic hybridization (CGH) studies. The comparative analysis of constant fold change cutoffs and intensity-dependent ones has been extensively discussed, showing a superior performance of the intensity-dependent strategy. For the validation dataset processing, reference values obtained with the training dataset processin were applied to normalize the validation dataset. These values were: Average ranked intensities (quantile normalization), batch information (batch adjustment), and gene average and standard deviations (z-score transformation. Here we describe the validation of the gene expression profile comprised of 32 protein-coding mRNAs and 6 intronic non-coding RNAs (ncRNAs) in an independent set of 22 samples, 16 from recurrence-free patients and 6 recurrent patients. In order to compare the expression levels of training and independent validation samples, gene intensity levels of samples in the validation dataset were transformed using normalization factors that had been generated with the training dataset.
2011-06-15 | E-GEOD-18917 | biostudies-arrayexpress
Project description:Expression data from 42 prostate cancer samples - 16 recurrent and 26 recurrence-free
| PRJNA123863 | ENA
Project description:We performed a mass spectrometry-based proteomic analysis of normal and malignant prostate tissues from 22 men who underwent surgery for prostate cancer. Prostate cancer samples included Grade Groups (3 to 5), with 8 patients experiencing recurrence and 14 without evidence of recurrence with a mean of 6.8 years of follow-up.
2022-05-04 | PXD028118 | Pride
Project description:We analyzed the protein-coding and non-coding gene expression profiles of 64 samples of prostate cancer primary tumors. All samples were collected between 1998 and 2001 with informed consent from patients subjected to radical prostatectomy at Hospital Sirio-Libanes in São Paulo. Selected patients were identified with clinical Stage T1-2 prostate cancer and no lymph node involvement, and received no adjuvant treatment after surgery as long as they remained recurrence-free. Biochemical recurrence was defined as an increase in patient blood PSA level to 0.2 ng per mL of blood at any time during the 5-year follow-up after prostatectomy. For this kind of experiment, also called self-self hybridization, the microarrays were cohybridized with each of Cy3- and Cy5-labeled cRNA replicates. This strategy has been used to derive intensity-dependent cutoffs to classify a gene as differentially expressed or divergent in comparative genomic hybridization (CGH) studies. The comparative analysis of constant fold change cutoffs and intensity-dependent ones has been extensively discussed, showing a superior performance of the intensity-dependent strategy. For the validation dataset processing, reference values obtained with the training dataset processin were applied to normalize the validation dataset. These values were: Average ranked intensities (quantile normalization), batch information (batch adjustment), and gene average and standard deviations (z-score transformation.
2011-06-15 | GSE18917 | GEO
Project description:This SuperSeries is composed of the following subset Series: GSE26022: [Gene Expression Training Set] Protein-coding and MicroRNA Biomarkers of Recurrence of Prostate Cancer Following Radical Prostatectomy GSE26242: [Gene Expression Validation Set] Protein-coding and MicroRNA Biomarkers of Recurrence of Prostate Cancer Following Radical Prostatectomy GSE26245: [miRNA Training Set] Protein-coding and MicroRNA Biomarkers of Recurrence of Prostate Cancer Following Radical Prostatectomy GSE26247: [miRNA Validation Set] Protein-coding and MicroRNA Biomarkers of Recurrence of Prostate Cancer Following Radical Prostatectomy Refer to individual Series
2011-06-01 | E-GEOD-26367 | biostudies-arrayexpress
Project description:Background and aims: Liver transplantation (LT) is the most radical treatment for hepatocellular carcinoma (HCC) with high rates of long-term survival, but tumor recurrence after LT is an unresolved problem. The aim of our study was to identify predictive markers for tumor recurrence after liver transplantation. Methods: In a retrospective single-center study, we included all patients with LT for HCC in our institution (01/2007-12/2012). Beside demographic data, we analyzed course, bridging therapies, Serum-AFP, time point of tumor recurrence, as well as the correlation of imaging and histopathology of our recipients. Additionally, we performed a microarray analysis to identify different miRNA profiles of patients with and without HCC recurrence after LT. Single assay stem-loop real-time PCR (Q-RT-PCR) was used for validation of the results. Results: During the study period, we performed 92 LT in patients with HCC (22 women, 70 men). Twenty-two (23.9%) patients developed a recurrent HCC after LT. Our subgroup with tumor recurrence after LT, presented with a mean disease-free survival of 10 months (3-55 months) and an overall survival of 25.5 months (4-77 months). Milan criteria, AFP levels and pathologic grading had an influence on the tumor recurrence. Performing miRNA analysis, we could identify significant upregulation of 8 miRNAs and downregulation of another 5 miRNAs in patients with tumor recurrence. Consecutively, array data were successfully validated using Q-RT-PCR. Multivariate Cox regression, ROC analysis and Kaplan-Meier showed that a score consisting of two miRNAs and Milan criteria are an independent predictor for tumor recurrence-free survival. Conclusions: Despite careful selection of patients, an early recurrence of HCC after LT cannot be avoided completely. Reliable prognostic markers related to tumor biology are still missing. Analysis and validation of specific miRNAs combined with radiological parameters might lead to a promising strategy for the prediction of tumor recurrence, but prospective studies have to follow. 8 macrodissected hepatocellular carcinoma (recurrent HCC) and 10 macrodissected hepatocellular carcinoma (non-recurrent HCC).
2016-03-01 | E-GEOD-64989 | biostudies-arrayexpress
Project description:Purpose: Clinicopathologic features and biochemical recurrence are sensitive, but not specific, predictors of metastatic disease and lethal prostate cancer. We hypothesize that a genomic expression signature detected in the primary tumor represents true biological potential of aggressive disease and provides improved prediction of early prostate cancer metastasis. Methods: A nested case-control design was used to select 639 patients from the Mayo Clinic tumor registry that underwent radical prostatectomy between 1987 and 2001. A genomic classifier (GC) was developed by modeling differential RNA expression using 1.4 million feature high-density expression arrays of men enriched for rising PSA after prostatectomy, including 213 that experienced early clinical metastasis after biochemical recurrence. A training set was used to develop a random forest classifier of 22 markers to predict for cases - men with early clinical metastasis after rising PSA. Performance of GC was compared to prognostic factors such as Gleason score and previous gene expression signatures in a withheld validation set. Results: Expression profiles were generated from 545 unique patient samples, with median follow-up of 16.9 years. GC achieved an area under the receiver operating characteristic curve of 0.75 (0.67 - 0.83) in validation, outperforming clinical variables and gene signatures. GC was the only significant prognostic factor in multivariable analyses. Within Gleason score groups, cases with high GC scores experienced earlier death from prostate cancer and reduced overall survival. The markers in the classifier were found to be associated with a number of key biological processes in prostate cancer metastatic disease progression. Conclusion: A genomic classifier was developed and validated in a large patient cohort enriched with prostate cancer metastasis patients and a rising PSA that went on to experience metastatic disease. This early metastasis prediction model based on genomic expression in the primary tumor may be useful for identification of aggressive prostate cancer. 545 formalin-fixed paraffin-embedded (FFPE) tissue samples from primary prostate cancer obtained from Radical Prostatectomy.
2013-07-01 | E-GEOD-46691 | biostudies-arrayexpress
Project description:Current protocols for the screening of prostate cancer cannot accurately discriminate clinically indolent tumors from more aggressive ones. One reliable indicator of outcome has been the determination of organ-confined versus nonorgan-confined disease but even this determination is often only made following prostatectomy. This underscores the need to explore alternate avenues to enhance outcome prediction of prostate cancer patients. Fluids that are proximal to the prostate, such as expressed prostatic secretions (EPS), are attractive sources of potential prostate cancer biomarkers as these fluids likely bathe the tumor. Direct-EPS samples from 16 individuals with extracapsular (n = 8) or organ-confined (n = 8) prostate cancer were used as a discovery cohort, and were analyzed in duplicate by a nine-step MudPIT on a LTQ-Orbitrap XL mass spectrometer. A total of 624 unique proteins were identified by at least two unique peptides with a 0.2% false discovery rate. A semiquantitative spectral counting algorithm identified 133 significantly differentially expressed proteins in the discovery cohort. Integrative data mining prioritized 14 candidates, including two known prostate cancer biomarkers: prostate-specific antigen and prostatic acid phosphatase, which were significantly elevated in the direct-EPS from the organ-confined cancer group. These and five other candidates (SFN, MME, PARK7, TIMP1, and TGM4) were verified by Western blotting in an independent set of direct-EPS from patients with biochemically recurrent disease (n = 5) versus patients with no evidence of recurrence upon follow-up (n = 10). Lastly, we performed proof-of-concept SRM-MS-based relative quantification of the five candidates using unpurified heavy isotope-labeled synthetic peptides spiked into pools of EPS-urines from men with extracapsular and organ-confined prostate tumors. This study represents the first efforts to define the direct-EPS proteome from two major subclasses of prostate cancer using shotgun proteomics and verification in EPS-urine by SRM-MS.
2014-01-30 | PXD000400 | Pride