OEX_Zhang_2008201936
Ontology highlight
ABSTRACT:
TISSUE(S): ['skin']
SUBMITTER: Zhang Yu
PROVIDER: OEX00010415 | NODE | 2020-08-21
REPOSITORIES: NODE
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Project description:We generated gene expression profiles from 498 primary neuroblastomas using RNA-Seq and microarrays. We sought to systematically evaluate the capability of RNA deep-sequencing (RNA-Seq)-based classification for clinical endpoint prediction in comparison to microarray-based ones. The neuroblastoma cohort was randomly divided into training and validation sets, and 360 predictive models on six clinical endpoints were generated and evaluated. While prediction performances did not differ considerably between the two technical platforms, the RNA-Seq data processing pipelines, or feature levels (i.e., gene, transcript, and exon junction levels), RNA-Seq models based on the AceView database performed best on most endpoints. Collectively, our study reveals an unprecedented complexity of the neuroblastoma transcriptome, and provides guidelines for the development of gene expression-based predictive classifiers using high-throughput technologies. Sample clinical characteristics definitions: dataset: Expression data set used for classification training (1) or validation (2) Sex: M = male; F= female age at diagnosis: the age in days at diagnosis mycn status: Amplification status of the MYCN proto-oncogene (amplified = 1, no amplification = 0; no information = N/A) high risk: Clinically considered as high-risk neuroblastoma (yes=1, no= 0) INSS stage: disease stage according to International Neuroblastoma Staging System (INSS) (1, 2, 3, 4 and 4S) class label: Maximally divergent disease courses - unfavorable (= 1): patient died despite intensive chemotherapy, favorable (=0): patient survived without chemotharapy for at least 1000 days post diagnosis; not applicable (N/A) progression: Occurrence of a tumor progression event (yes=1; no=0) death from disease: Occurrence of death from the disease (yes=1; no=0)
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Project description:Purpose No blood biomarkers to detect early OSCC without clinical signs exist - diagnosis is solely based on histology of a visible tumor. Most OSCC patients are diagnosed at advanced stage, which leads to significant morbidity and poor survival. If high-risk patients were identified with blood-based biomarkers before clear clinical manifestations, tumors could be detected and treated at earlier stage. Patients and Methods Serum samples were collected from patients with OSCC at the Department of Otorhinolaryngology – Head and Neck Surgery, Helsinki University Hospital (Finland). Age and gender matched healthy individuals served as controls. Quantitative proteomic analysis with mass spectrometry was performed. Results We quantified 388 proteins in the serum samples. A complete separation between cases and controls was seen based on the protein expression profiles. With statistical analyses, we identified a set of eight proteins that most reliably separated OSCC patients from healthy individuals. Although the tumor stages varied from I to IVa, this set of proteins was able to identify all OSCCs and thus is sensitive for detecting even early tumors. Conclusion We are the first to suggest a set of serum biomarkers to be evaluated further as a diagnostic panel to detect preclinical OSCC in risk patients.
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Project description:We generated gene expression profiles from 498 primary neuroblastomas using RNA-Seq and microarrays. We sought to systematically evaluate the capability of RNA deep-sequencing (RNA-Seq)-based classification for clinical endpoint prediction in comparison to microarray-based ones. The neuroblastoma cohort was randomly divided into training and validation sets, and 360 predictive models on six clinical endpoints were generated and evaluated. While prediction performances did not differ considerably between the two technical platforms, the RNA-Seq data processing pipelines, or feature levels (i.e., gene, transcript, and exon junction levels), RNA-Seq models based on the AceView database performed best on most endpoints. Collectively, our study reveals an unprecedented complexity of the neuroblastoma transcriptome, and provides guidelines for the development of gene expression-based predictive classifiers using high-throughput technologies. Sample clinical characteristics definitions: dataset: Expression data set used for classification training (1) or validation (2) Sex: M = male; F= female age at diagnosis: the age in days at diagnosis mycn status: Amplification status of the MYCN proto-oncogene (amplified = 1, no amplification = 0; no information = N/A) high risk: Clinically considered as high-risk neuroblastoma (yes=1, no= 0) INSS stage: disease stage according to International Neuroblastoma Staging System (INSS) (1, 2, 3, 4 and 4S) class label: Maximally divergent disease courses - unfavorable (= 1): patient died despite intensive chemotherapy, favorable (=0): patient survived without chemotharapy for at least 1000 days post diagnosis; not applicable (N/A) progression: Occurrence of a tumor progression event (yes=1; no=0) death from disease: Occurrence of death from the disease (yes=1; no=0) Gene expression of 498 neuroblastoma samples was quantified by RNA sequencing as well as by microarray analyses in order to understand the neuroblastoma transcriptome and predict clinical endpoints.
2014-10-21 | E-GEOD-49710 | biostudies-arrayexpress