Ontology highlight
ABSTRACT:
OTHER RELATED OMICS DATASETS IN: E-GEOD-27489GSE27554
PROVIDER: PRJNA138413 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Screening study to identify diagnostic markers for lung cancer in endobronchial lining fluid
2025-01-24 | E-GEOD-27489 | ExpressionAtlas
Project description:VALIDATION STUDY TO VERIFY DIAGNOSTIC MARKERS FOR LUNG CANCER IN ENDOBRONCHIAL LINING FLUID
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Project description:SCREENING STUDY TO IDENTIFY DIAGNOSTIC MARKERS FOR LUNG CANCER IN ENDOBRONCHIAL LINING FLUID
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Project description:Multiple Salivary Biomarkers for Early Detection of Pancreatic Cancer
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Project description:We investigated whether biomarker analysis in endobronchial epithelial lining fluid (ELF) collected by bronchoscopic microsampling may be useful for a definitive preoperative diagnosis. Therefore we compared ELF samples close to nodule and from the contralateral site from patients with malignant or benign diagnosis. ELF samples have been derived from early stage NSCLC patients and controls. Wilcox Test was performed to identify differentially expressed genes associated to a malignant diagnosis. key words: disease subtype analysis
2011-12-31 | E-GEOD-27553 | biostudies-arrayexpress
Project description:We investigated whether biomarker analysis in endobronchial epithelial lining fluid (ELF) collected by bronchoscopic microsampling may be useful for a definitive preoperative diagnosis. Therefore we compared ELF samples close to nodule and from the contralateral site from patients with malignant or benign diagnosis. ELF samples have been derived from early stage NSCLC patients and controls. LIMMA analysis was performed to identify differentially expressed genes associated to a malignant diagnosis. keywords: disease subtype analysis
2011-12-31 | E-GEOD-27489 | biostudies-arrayexpress
Project description:We investigated whether biomarker analysis in endobronchial epithelial lining fluid (ELF) collected by bronchoscopic microsampling may be useful for a definitive preoperative diagnosis. Therefore we compared ELF samples close to nodule and from the contralateral site from patients with malignant or benign diagnosis.
2011-12-31 | GSE27553 | GEO
Project description:We investigated whether biomarker analysis in endobronchial epithelial lining fluid (ELF) collected by bronchoscopic microsampling may be useful for a definitive preoperative diagnosis. Therefore we compared ELF samples close to nodule and from the contralateral site from patients with malignant or benign diagnosis.
2011-12-31 | GSE27489 | GEO
Project description:Individuals who present with premalignant endobronchial lesions are considered at high risk of lung cancer. Nonetheless, premalignant lesions behave erratically and only a minority progresses towards lung cancer. Therefore, biomarkers need to be discovered that can aid in assessing an individual’s risk for subsequent cancer to better tailor treatment choices and avoid unnecessary follow-up procedures. We recently proposed a classifier of DNA copy number alterations (CNAs) at 3p26.3-p11.1, 3q26.2-29, and 6p25.3-24.3 as risk predictor for endobronchial cancer. The current study was set out to validate the classifier among an independent series of premalignant endobronchial lesions with various histological grades. A series of 36 endobronchial premalignant lesions (8 squamous metaplasia, and 28 various grades of dysplasia) identified during autofluorescence bronchoscopy of 12 case subjects who had carcinoma in situ or carcinoma (≥CIS) during follow-up bronchoscopy at the initial site and 24 control subjects who remained cancer-free, was subjected to array Comparative Genomic Hybridization (arrayCGH). DNA copy number profiles were related to lesion outcome. Prediction accuracy of the previously defined molecular classifier to predict endobronchial cancer in this series was determined. Unsupervised hierarchical clustering analysis revealed a significant association between cluster assignment and lesion outcome (p< 0.001), independent of histological grade, with quiescent profiles in controls (24/24) and aberrant profiles in the majority of cases (9/12). Our pre-defined classifier demonstrated 92% accuracy for predicting cancer outcome in the current sample series. Our validated classifier holds great promise for stratification of patients with premalignant endobronchial lesions for risk of subsequent cancer.
2013-12-03 | GSE45287 | GEO
Project description:Early detection of small cell lung cancer crucially demands highly reliable markers. Growing evidence suggests that extracellular vesicles carry tumor cell-specific cargo suitable as protein markers in cancer. Therefore, we isolated plasma-derived microvesicles from newly diagnosed small cell lung cancer patients and investigated proteome dynamics of these microvesicles aiming at improving the detection of small cell lung cancer. A total of 1,223 proteins were initially identified. After data processing and statistical analysis, several proteins were found to be differentially expressed in comparing small cell lung cancer patients and healthy individuals. Furthermore, our data may indicate involvement of complement activation, integrin-mediated signaling, cell adhesion- and migration, and blood coagulation in small cell lung cancer pathogenesis.
2022-04-04 | PXD028944 | Pride