Detection and validation of RNA H19-induced UHMK1 expression as a clinical pancreatic cancer progression marker
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PROVIDER: PRJNA734157 | ENA |
REPOSITORIES: ENA
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Project description:Long non-coding RNA H19
2021-12-31 | GSE175865 | GEO
Project description:Optimization and Clinical Validation of a Pathogen Detection Microarray
| PRJNA99799 | ENA
Project description:In order to evaluate the performance of CNV detection in next-generation sequencing platform in varied sample types, we employed chromosomal microarray analysis (CMA) for validation of the samples with NGS-based detection results (NCBI Sequence Read Archive with accession number SRA296708). Besides array Comparative Genomics Hybridization (aCGH, Agilent) , we used a commerical SNP-array (Illumina) including early abortus, induced termination, prenatal samples and postnatal samples. CMA results were compared with NGS-based detection results. 100% consistency was obtained between NGS-based approach and CMA in pathogenic or likely pathogenic CNVs detection.
2015-10-01 | GSE73190 | GEO
Project description:A key requirement of liquid chromatography-mass spectrometry (LC-MS)-based allergenic food protein analysis methods is to use protein marker peptides with good analytical performances in LC-MS analysis of commercial processed foods. In this study, we developed a multistage walnut protein marker peptide selection strategy involving marker peptide discovery and verification and LC-MS validation of chemically equivalent stable isotope-labeled peptides. This strategy proposed three walnut protein marker peptides, including two new marker peptides. Our LC-MS-based walnut protein analysis method using the three stable isotope-labeled peptides showed acceptable linearity (R2 >0.99), matrix effects (coefficient of variation <±15%), sensitivity (limit of detection >0.3 pg/μL, limit of quantification >0.8 pg/μL), recovery (85.1–103.4%), accuracy, and precision (coefficient of variation <10%). In conclusion, our multistage marker peptide selection strategy effectively selects specific protein marker peptides for sensitive detection and absolute quantification of walnut proteins in LC-MS analysis of commercial processed foods.
2023-07-31 | PXD043010 | Pride
Project description:This SuperSeries is composed of the following subset Series: GSE24626: Validation data for Lawrence Livermore Microbial Detection Array version 2 GSE24699: Validation data for Lawrence Livermore Microbial Detection Array version 1 Refer to individual Series
2010-10-15 | E-GEOD-24700 | biostudies-arrayexpress
Project description:This series includes microarrays from 36 patient samples and 2 cell-culture controls, used to optimize and validate the pathogen detection microarray (Wong, et. al. 2007) Keywords: viral pathogen detection
2007-05-29 | GSE7779 | GEO
Project description:In order to evaluate the performance of CNV detection in next-generation sequencing platform in varied sample types, we employed chromosomal microarray analysis (CMA) for validation of the samples with NGS-based detection results (NCBI Sequence Read Archive with accession number SRA296708). Besides snp-array, we used a customized array Comparative Genomics Hybridization (aCGH, Agilent) approach for a cohort of clinical samples including early abortus, induced termination, prenatal samples and postnatal samples. CMA results were compared with NGS-based detection results. 100% consistency was obtained between NGS-based approach and CMA in pathogenic or likely pathogenic CNVs detection.
2015-10-01 | GSE72891 | GEO
Project description:Introduction: Serous ovarian cancer is the leading cause of gynecological cancers, with a 5-year survival rate below 45% due in part to the nonspecific symptoms and lack of accurate screening for early detection. In comparison, patients diagnosed at an early stage have a five-year survival rate of 92%, demonstrating the urgent need for biomarkers for the early detection of disease. Serum from patients with serous ovarian cancer contain antibodies to tumor antigens that are potential biomarkers for early detection. The purpose of this study is to identify a panel of novel serum autoantibody (AAb) biomarkers for the early diagnosis of serous ovarian cancer. Methods: To detect AAb we probed high-density programmable protein microarrays (NAPPA) containing 10,247 antigens with sera from patients with serous ovarian cancer (n = 30 cases/ 30 healthy controls) and measured bound IgG. We identified 735 promising tumor antigens using cutoff values of 10% sensitivity at 95% specificity and K-value>0.8, as well as visual analysis and evaluated these with an independent set of serous ovarian cancer sera (n = 30 cases/ 30 benign disease controls/ 30 heathy controls). Thirty-nine potential tumor autoantigens were identified with sensitivities ranging from 3 to 39.7% sensitivity at 95% specificity and were retested using an orthogonal programmable ELISA assay. A total of 13 potential tumor antigens were identified for further validation using an independent ovarian cancer sera set (n = 44 cases/ 34 healthy controls). Sensitivities at 95% specificity were calculated and a serous ovarian cancer classifier was constructed. In addition, we evaluated a longitudinal study using blinded serous pre-diagnostic ovarian cancer sera (n = 9 cases/ 90 controls) to examine the value of three (CTAG1, CTAG2, and p53) of these AAb in comparison to CA 125. Results: We identified 11-AAbs (ICAM3, CTAG2, p53, STYXL1, PVR, POMC, NUDT11, TRIM39, UHMK1, KSR1, and NXF3) that distinguished serous ovarian cancer cases from healthy controls with a combined 45% sensitivity at 100% specificity. In our longitudinal analysis, p53- and CTAG-AAb were detected up to 9 months prior to ovarian cancer diagnosis and increased with CA 125 levels. Conclusion: These are potential circulating biomarkers for the early detection of serous ovarian cancer, and warrant confirmation in larger clinical cohorts. In addition, p53- and CTAG1/2-AAb are detected in a subset of women with ovarian cancer up to 9 months prior to clinical diagnosis. Their utility as a biomarker for early detection, beyond CA 125, warrant further investigation.
2017-06-01 | GSE79516 | GEO
Project description:Background: Cowpea (Vigna unguiculata L. Walp) is an important food and fodder legume of the semiarid tropics and subtropics worldwide, especially in sub-Saharan Africa. High density genetic linkage maps are needed for marker assisted breeding but are not available for cowpea. A single feature polymorphism (SFP) is a microarray-based marker which can be used for high throughput genotyping and high density mapping. Results: Here we report detection and validation of SFPs in cowpea using a readily available soybean (Glycine max) genome array. Robustified projection pursuit (RPP) was used for statistical analysis using RNA as a surrogate for DNA. Using a 15% outlying score cut-off, 1058 potential SFPs were enumerated between two parents of a recombinant inbred line (RIL) population segregating for several important traits including drought tolerance, Fusarium and brown blotch resistance, grain size and photoperiod sensitivity. Sequencing of 25 putative polymorphism-containing amplicons yielded a SFP probe set validation rate of 68%. Conclusions: We conclude that the Affymetrix soybean genome array is a satisfactory platform for identification of some 1000's of SFPs for cowpea. This study provides an example of extension of genomic resources from a well supported species to an orphan crop. Presumably, other legume systems are similarly tractable to SFP marker development using existing legume array resources. Keywords: Polymorphism discovery, array based genotyping
2008-02-21 | GSE10284 | GEO
Project description:Validation of a previously established gene expression profile for detection of lymph node metastasis (ArrayExpress E-UMCU-11).
2012-10-10 | GSE30788 | GEO