Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA323612 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Histological classification of gliomas guides treatment decisions. Because of the high interobserver variability, we aimed to improve classification by performing gene expression profiling on a large cohort of glioma samples of all histological subtypes and grades. The seven identified intrinsic molecular subtypes are different from histological subgroups and correlate better to patient survival. Our data indicate that distinct molecular subgroups clearly benefit from treatment. Specific genetic changes (EGFR amplification, IDH1 mutation, 1p/19q LOH) segregate in -and may drive- the distinct molecular subgroups. Our findings were validated on three large independent sample cohorts (TCGA, REMBRANDT, and GSE12907). We provide compelling evidence that expression profiling is a more accurate and objective method to classify gliomas than histology. 276 glioma samples of all histology, 8 control samples
2010-04-26 | E-GEOD-16011 | biostudies-arrayexpress
Project description:Microarray analysis was used to determine the expression of 12,000 genes in a set of 50 gliomas, 28 glioblastomas and 22 anaplastic oligodendrogliomas. Supervised learning approaches were used to build a two-class prediction model based on a subset of 14 glioblastomas and 7 anaplastic oligodendrogliomas with classic histology. A 20-feature k-nearest neighbor model correctly classified 18 of the 21 classic cases in leave-one-out cross-validation when compared with pathological diagnoses. This model was then used to predict the classification of clinically common, histologically nonclassic samples. When tumors were classified according to pathology, the survival of patients with nonclassic glioblastoma and nonclassic anaplastic oligodendroglioma was not significantly different (P = 0.19). However, class distinctions according to the model were significantly associated with survival outcome (P = 0.05). This class prediction model was capable of classifying high-grade, nonclassic glial tumors objectively and reproducibly. Moreover, the model provided a more accurate predictor of prognosis in these nonclassic lesions than did pathological classification. These data suggest that class prediction models, based on defined molecular profiles, classify diagnostically challenging malignant gliomas in a manner that better correlates with clinical outcome than does standard pathology. louis-00379 Assay Type: Gene Expression Provider: Affymetrix Array Designs: HG_U95Av2 Organism: Homo sapiens (ncbitax) Material Types: total_RNA, synthetic_RNA, organism_part, whole_organism Disease States: Classic anaplastic oligodendroglioma, Non-classic glioblastoma, Classic glioblastoma, Non-classic anaplastic oligodendroglioma
2016-05-28 | E-GEOD-82009 | biostudies-arrayexpress
Project description:Histological classification of gliomas guides treatment decisions. Because of the high interobserver variability, we aimed to improve classification by performing gene expression profiling on a large cohort of glioma samples of all histological subtypes and grades. The seven identified intrinsic molecular subtypes are different from histological subgroups and correlate better to patient survival. Our data indicate that distinct molecular subgroups clearly benefit from treatment. Specific genetic changes (EGFR amplification, IDH1 mutation, 1p/19q LOH) segregate in -and may drive- the distinct molecular subgroups. Our findings were validated on three large independent sample cohorts (TCGA, REMBRANDT, and GSE12907). We provide compelling evidence that expression profiling is a more accurate and objective method to classify gliomas than histology.
2010-04-26 | GSE16011 | GEO
Project description:Microarray analysis was used to determine the expression of 12,000 genes in a set of 50 gliomas, 28 glioblastomas and 22 anaplastic oligodendrogliomas. Supervised learning approaches were used to build a two-class prediction model based on a subset of 14 glioblastomas and 7 anaplastic oligodendrogliomas with classic histology. A 20-feature k-nearest neighbor model correctly classified 18 of the 21 classic cases in leave-one-out cross-validation when compared with pathological diagnoses. This model was then used to predict the classification of clinically common, histologically nonclassic samples. When tumors were classified according to pathology, the survival of patients with nonclassic glioblastoma and nonclassic anaplastic oligodendroglioma was not significantly different (P = 0.19). However, class distinctions according to the model were significantly associated with survival outcome (P = 0.05). This class prediction model was capable of classifying high-grade, nonclassic glial tumors objectively and reproducibly. Moreover, the model provided a more accurate predictor of prognosis in these nonclassic lesions than did pathological classification. These data suggest that class prediction models, based on defined molecular profiles, classify diagnostically challenging malignant gliomas in a manner that better correlates with clinical outcome than does standard pathology.
2016-05-28 | GSE82009 | GEO
Project description:Intrinsic Gene Expression Profiles of Gliomas are a Better Predictor of Survival than Histology
| PRJNA115435 | ENA
Project description:Molecular profiling of cerebral gliomas distinguishes biologically distinct tumor groups and provides prognostically relevant information beyond histological classification and IDH1/2 mutation status. We performed microarray-based genome- and transcriptome-wide molecular profiling of primary tumor samples from 137 patients with cerebral gliomas, 61 WHO grade II and 76 WHO grade III tumors.
2015-04-30 | GSE61374 | GEO
Project description:The outcome of patients with anaplastic gliomas varies considerably depending on histology and single molecular markers such as codeletion of 1p/19q and mutations of the isocitrate dehydrogenase (IDH) gene. Whether a molecularly-based classification of anaplastic gliomas based on large scale genomic or epigenomic analyses is superior to histopathology, may reflect distinct biological subtypes, predict outcome and guide therapy decisions had yet to be determined. Epigenome-wide DNA methylation analysis, which also allows for the detection of copy-number aberrations, was performed in a cohort of 228 patients with anaplastic gliomas (astrocytomas, oligoastrocytomas and oligodendrogliomas), including 115 patients of the NOA-04 trial. We further compared these tumors with a group of 55 glioblastomas. Unsupervised clustering demonstrated two main groups based on IDH mutation status: CpG island methylator phenotype (CIMP) positive (77.5%) or negative (22.5%). CIMP+ (IDH mutant) tumors showed a further separation based on copy-number status of chromosome arms 1p and 19q, but not based on histopathology. CIMP- (IDH wild type) tumors on the other hand showed hallmark copy-number alterations of glioblastomas. These tumors clustered together with CIMP- glioblastomas without forming separate groups based on WHO grade. There was no Tumor classification based on CIMP and 1p/19q status was significantly associated with survival allowing a better prediction of outcome than the current histopathological classification alone: Patients with CIMP+ tumors with 1p/19q codeletion had the best prognosis, followed by patients with CIMP+ but intact 1p/19q status. Patients with CIMP- anaplastic gliomas had the worst prognosis. Collectively, our data suggest that anaplastic gliomas can be grouped into three molecular subtypes with clear association to underlying biology and clinical outcome based on IDH and 1p/19q status. The data do not provide a molecular basis for the diagnosis of anaplastic oligoastrocytoma. We investigated a subset of 228 anaplastic gliomas using the Illumina 450k methylation array.
2014-06-06 | E-GEOD-58218 | biostudies-arrayexpress
Project description:Histological grading is the key factors affecting the prognosis of patients and instructive in guiding treatment and assessing recurrence in non-functional pancreatic neuroendocrine tumor (NF-Pan-NET). Approximately one-third of patients without copy number variation (CNV) alteration and the prognosis of these patients are better than that of patients with CNV alteration. Tumor classification based on CNV also showed significant value in evaluating prognosis. However, the difference between CNV and histological grading is unclear. Here, at single cell level, we analyzed the heterogeneity of tumor cells according to two classification criteria, genomic instability (including CNV alteration and tumor mutation burden) and histological grading. We found that the classification basis on genomic instability of the bulk tissues was better than histological grading in distinguishing tumor cells at scRNA-seq. We revealed that the activated core pathways of tumor cells were significantly different under different histological gradings and genomic instability patterns. In particular, patients with liver metastases had specific activation pathways. Deciphering the differences of the tumor microenvironment through single-cell sequencing, we found that tip cells, lymphatic endothelial cells, macrophages, CD1A+ dendritic cell, Treg, MAIT, ILC and CAFs might patriciate in the process of hepatic metastases, which will facilitate the understanding of the patterns to decode the malignant potential and of NF-Pan-NET.
2024-02-27 | GSE256136 | GEO
Project description:Kynureninase is a member of a large family of catalytically diverse but structurally homologous pyridoxal 5'-phosphate (PLP) dependent enzymes known as the aspartate aminotransferase superfamily or alpha-family. The Homo sapiens and other eukaryotic constitutive kynureninases preferentially catalyze the hydrolytic cleavage of 3-hydroxy-l-kynurenine to produce 3-hydroxyanthranilate and l-alanine, while l-kynurenine is the substrate of many prokaryotic inducible kynureninases. The human enzyme was cloned with an N-terminal hexahistidine tag, expressed, and purified from a bacterial expression system using Ni metal ion affinity chromatography. Kinetic characterization of the recombinant enzyme reveals classic Michaelis-Menten behavior, with a Km of 28.3 +/- 1.9 microM and a specific activity of 1.75 micromol min-1 mg-1 for 3-hydroxy-dl-kynurenine. Crystals of recombinant kynureninase that diffracted to 2.0 A were obtained, and the atomic structure of the PLP-bound holoenzyme was determined by molecular replacement using the Pseudomonas fluorescens kynureninase structure (PDB entry 1qz9) as the phasing model. A structural superposition with the P. fluorescens kynureninase revealed that these two structures resemble the "open" and "closed" conformations of aspartate aminotransferase. The comparison illustrates the dynamic nature of these proteins' small domains and reveals a role for Arg-434 similar to its role in other AAT alpha-family members. Docking of 3-hydroxy-l-kynurenine into the human kynureninase active site suggests that Asn-333 and His-102 are involved in substrate binding and molecular discrimination between inducible and constitutive kynureninase substrates.
| S-EPMC2531291 | biostudies-literature