Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA381920 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Methylome analysis of different histological thyroid lesions and clinical features, aiming to better understand the DNA methylation deregulation of TC and to identify a prognostic epigenetic signature in well differentiated thyroid carcinomas.
2017-10-20 | GSE97466 | GEO
Project description:By characterizing at the genome-wide level the DNA methylation patterns of the largest series of well-differentiated thyroid tumors described to date, we provide novel insights into the biology underlying on the one hand the histological heterogeneity, and on the other differential patient outcomes of this disease. We describe distinct subtype- and mutational-specific methylation profiles as well as novel markers associated with recurrence-free survival, which could provide an improved classification of patients. Bisulphite converted DNA from the 83 primary thyroid tumor samples and 8 adjacent normal tissue samples were hybridized to the Illumina Infinium 27k Human Methylation Beadchip v1.2
2014-07-31 | E-GEOD-51090 | biostudies-arrayexpress
Project description:By characterizing at the genome-wide level the DNA methylation patterns of the largest series of well-differentiated thyroid tumors described to date, we provide novel insights into the biology underlying on the one hand the histological heterogeneity, and on the other differential patient outcomes of this disease. We describe distinct subtype- and mutational-specific methylation profiles as well as novel markers associated with recurrence-free survival, which could provide an improved classification of patients.
2014-07-31 | GSE51090 | GEO
Project description:A novel approach of reference-free deconvolution of large-scale DNA methylation data enabled to develop a machine learning classifier based on CpG sites, specific for Latent Methylation Components (LMC), that allowed for patient allocation to prognostic clusters. DNA methylation data was processed using reference-free analyses (MeDeCom) and reference-based computational tumor deconvolution (MethylCIBERSORT, LUMP). These results demonstrate that LMC-based segregation of large-scale DNA methylation data is a promising tool for classifier development and treatment response estimation in cancer patients under targeted immunotherapy.
2021-07-22 | GSE175699 | GEO
Project description:Using a genome-wide DNA methylation profiling of 186 cervical samples from women with different CIN grades and well-characterized HPV genotyping, we identified novel methylation markers of epigenetic changes that discriminate accurately between clinically significant and transient cervical disease. In particular, a 2-gene DNA methylation classifier (ATP10A and HAS1) showed a promising ability to discriminate among pre-invasive cervical lesion grades. The identified markers are excellent candidates for future diagnostic or prognostic assays in cervical cancer screening.
2020-01-30 | GSE143752 | GEO
Project description:The 2021 WHO Classification of Tumors of the Central Nervous System includes several tumor types and subtypes for which the diagnosis is at least partially reliant on utilization of whole genome methylation profiling. The current approach to array DNA methylation profiling utilizes a reference library of tumor DNA methylation data, and a machine learning-based tumor classifier. This approach was pioneered and popularized by the German Cancer Research Network (DKFZ) and University Hospital Heidelberg. This research group has kindly made their classifier for central nervous system tumors freely available as a research tool via a web-based portal. However, this classifier is not maintained in a clinical testing environment. Therefore, we validated our own DNA methylation-based classifier of central nervous system tumors. We validated our classifier using the same training and validation datasets as the DKFZ group. In addition, we performed a validation of samples tested in our own laboratory and compared the performance of both classifiers. Using the validation data set, our classifier’s performance showed high concordance (92%) and comparable accuracy (specificity 94.0% v. 84.9% for DKFZ, sensitivity 88.6% v. 94.7% for DKFZ). Receiver operator curve showed areas under the curve of 0.964 v. 0.966 for NM and DKFZ classifiers, respectively. Our classifier performed comparably well with samples tested in our own laboratory and is currently offered for clinical testing.
2022-03-19 | GSE198855 | GEO
Project description:Background: With the incidence of papillary thyroid carcinoma rising worldwide, the decision about lobectomy versus total thyroidectomy will become relevant for an increasing number of patients. There exists no reliable biomarker for metastatic potential or tendency of recurrence that could assist in the risk stratification of patients. Objective: To develop a gene expression classifier for metastatic potential by measuring RNA expression in the primary tumor at the time of cancer surgery. Further, to investigate the ability of the gene expression classifier to identify metastatic and recurrent cases. Method: Genome-wide expression analyses. The development cohort consisted of freshly frozen tissues from 38 patients collected between the years 1986 and 2009. Validation was performed on a consecutive cohort of formalin fixated paraffin embedded surgical tissue specimens from 183 patients treated at Odense and Aarhus University Hospitals. Results: A 17 gene classifier (ADAMTS1, ANTXR1, C7, CXCL12, EBF1, FBLN2, FOSL2, GGT5, GPR124, JAM3, LRIG1, NDRG1, PRRX1, ROBO1, SORL1, TCF4, and ZEB1) was identified based on the expression values of these genes in the groups with and without metastasis in the development cohort. The 17 gene classifier for regional and/or distant metastasis identified was tested against the clinical status in the validation cohort. Sensitivity was 51.6% (95% CI 41.2%-61.8) and specificity 61.6 % (95% CI 50.5%-71.9%). Further, the Kaplan-Meyer method was used to estimate whether the classifier was useful as a prognostic marker for recurrences. Log-rank testing failed to identify any significance (p=0.32). Conclusion: A 17 gene classifier for metastatic potential was developed, and the results showed a clear biological difference between groups. However, through validation, the prognostic significance of this classifier could not be shown in identifying metastatic cases or in the ability of dichotomizing patients according to risk of recurrence after primary treatment. 38 patients with papillary thyroid carcinoma from a consecutive cohort of patients, no replicates
2015-01-20 | E-GEOD-65074 | biostudies-arrayexpress
Project description:Poorly differentiated thyroid carcinomas (PDTC) represent a heterogeneous, aggressive entity, presenting features that suggest a progression from well-differentiated carcinomas. To elucidate the mechanisms underlying such progression and identify novel therapeutical targets, we assessed the genome-wide expression in normal thyroid tissues, well-differentiated thyroid carcinomas and PDTC. RNA were extracted from 2 normal thyroid tissues taken from the opposite lobe of thyroid tumors, and 24 thyroid carcinomas: 5 PDTC, 7 classic papillary thyroid carcinomas (cPTC), 8 follicular variants of PTC (fvPTC) and 4 follicular thyroid carcinomas (FTC). All samples were obtained at time of surgery and immediately frozen in liquid nitrogen. We also hybridized a commercial pool of human thyroid total RNA (BD Bioscience). PTC were screened for BRAF mutations and rearrangements of RET/PTC and, in addition, follicular variants were also analyzed for RAS mutations and PAX8-PPARG rearrangements. FTC were screened for RAS and PAX8-PPARG rearrangements. PDTC were analyzed for BRAF, RAS and PAX8-PPARG genes.
2013-12-10 | E-GEOD-53157 | biostudies-arrayexpress
Project description:DNA methylation profile in well-differentiated cancer uncoves another source of diagnostic and prognostic markers
| PRJNA221151 | ENA
Project description:Poorly differentiated thyroid carcinomas (PDTC) represent a heterogeneous, aggressive entity, presenting features that suggest a progression from well-differentiated carcinomas. To elucidate the mechanisms underlying such progression and identify novel therapeutical targets, we assessed the genome-wide expression in normal thyroid tissues, well-differentiated thyroid carcinomas and PDTC.
2013-12-10 | GSE53157 | GEO