Homo sapiens
Ontology highlight
ABSTRACT:
OTHER RELATED OMICS DATASETS IN: E-GEOD-27489GSE27554
PROVIDER: PRJNA141751 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Screening study to identify diagnostic markers for lung cancer in endobronchial lining fluid
2015-08-17 | E-GEOD-27489 | ExpressionAtlas
Project description:VALIDATION STUDY TO VERIFY DIAGNOSTIC MARKERS FOR LUNG CANCER IN ENDOBRONCHIAL LINING FLUID
| PRJNA141791 | ENA
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: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: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. Fresh frozen specimens of 36 premalignant endobronchial biopsies. Test samples were compared to an external pool of normal male/female reference DNA.
2013-12-03 | E-GEOD-45287 | biostudies-arrayexpress
Project description:Recently, major efforts have been directed toward early detection of lung cancer through low-dose computed tomography (LDCT) scanning. Data from the National Lung Screening Trial (NLST) suggest that yearly screening with thoracic LDCT scanning for high-risk current and former smokers reduces lung cancer mortality by 20% and total mortality by 7%. However, issues including indeterminate nodules detected by LDCT and radiation exposure impact the practicality of LDCT-based screening on a national and global basis. A blood-based biomarker or multiplexed marker panel that could complement LDCT would represent a major advance in implementing lung cancer screening. Efforts to develop blood-based biomarkers for lung cancer early detection using a variety of methodologies are currently ongoing. Proteomic studies have led to the identification of several candidate markers including pro-surfactantproteinB(pro-SFTPB), a target of a lineage-survival oncogene in lung cancer, NKX2-1.Validation studies using blood samples collected at the time of LDCT screening for lung cancer substantiated the performance of pro-SFTPB. Multivariable logistic regression models were used to evaluate the predictive ability of pro-SFTPB. The area under the curve (AUC) values of the full model with and without pro-SFTPB were 0.741 (95% CI, 0.696 to 0.783) and 0.669 (95%CI, 0.620 to 0.717), respectively (difference in AUC, P_.001). Single markers are unlikely to have sufficient performance for implementation in a screening setting, hence the need to explore several discovery platforms to identify markers that provide complementary performance. Metabolomics represents a global unbiased approach to the profiling of small molecules and has been established as a platform for biomarker discovery for a variety of human biofluids and tissues. Here we used an untargeted liquid chromatography/mass spectrometry (MS) metabolomics approach to identify metabolites that distinguish human sera collected before the diagnosis of lung cancer from matched control sera in a prospective cohort of highrisk patients from the Beta-Carotene and Retinol Efficacy Trial (CARET).
2019-07-01 | MSV000084032 | GNPS
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