Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJDB14089 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| DRR396224.fastq.gz | Fastqsanger.gz | |||
| DRR396225.fastq.gz | Fastqsanger.gz | |||
| DRR396226.fastq.gz | Fastqsanger.gz | |||
| DRR396227.fastq.gz | Fastqsanger.gz | |||
| DRR396228.fastq.gz | Fastqsanger.gz |
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Project description:Cervical cancer is characterized by a well-defined pre-malignant phase, cervical intraepithelial neoplasia (CIN). Identification of high grade CIN lesions by population-based screening programs and their subsequent treatment has led to a significant reduction of the incidence and mortality of cervical cancer. Cytology-based testing of cervical smears is the most widely used cervical cancer screening method, but is not ideal, as the sensitivity for detection of CIN2 and higher (CIN2+) is only ~55%. Therefore, more sensitive and specific biomarkers for cervical cancer and its precancerous stages are needed.
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Project description:The paper "Metabolomic Machine Learning Predictor for Diagnosis and Prognosis of Gastric Cancer" addresses the need for non-invasive diagnostic tools for gastric cancer (GC). Traditional methods like endoscopy are invasive and expensive. The authors conducted a targeted metabolomics analysis of 702 plasma samples to develop machine learning models for GC diagnosis and prognosis. The diagnostic model, using 10 metabolites, achieved a sensitivity of 0.905, outperforming conventional protein marker-based methods. The prognostic model effectively stratified patients into risk groups, surpassing traditional clinical models.
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Project description:The paper "Metabolomic Machine Learning Predictor for Diagnosis and Prognosis of Gastric Cancer" addresses the need for non-invasive diagnostic tools for gastric cancer (GC). Traditional methods like endoscopy are invasive and expensive. The authors conducted a targeted metabolomics analysis of 702 plasma samples to develop machine learning models for GC diagnosis and prognosis. The diagnostic model, using 10 metabolites, achieved a sensitivity of 0.905, outperforming conventional protein marker-based methods. The prognostic model effectively stratified patients into risk groups, surpassing traditional clinical models.
I have successfully reproduced the diagnosis model from the paper. This machine learning-based system differentiates GC patients from non-GC controls using metabolomics data from plasma samples analyzed by liquid chromatography-mass spectrometry (LC-MS). The model focuses on 10 metabolites, including succinate, uridine, lactate, and serotonin. Employing LASSO regression and a random forest classifier, the model achieved an AUROC of 0.967, with a sensitivity of 0.854 and specificity of 0.926. This model significantly outperforms traditional diagnostic methods and underscores the potential of integrating machine learning with metabolomics for early GC detection and treatment.
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Project description:<h4><strong>BACKGROUND: </strong>The introduction of high-risk human papillomavirus (hrHPV) testing as part of primary cervical screening is anticipated to improve sensitivity, but also the number of women who will screen positive. Reflex cytology is the preferred triage test in most settings but has limitations including moderate diagnostic accuracy, lack of automation, inter-observer variability and the need for clinician-collected sample. Novel, objective and cost-effective approaches are needed.</h4><h4><strong>METHODS: </strong>In this study, we assessed the potential use of an automated metabolomic robotic platform, employing the principle of laser-assisted Rapid Evaporative Ionisation Mass Spectrometry (LA-REIMS) in cervical cancer screening.</h4><h4><strong>FINDINGS: </strong>In a population of 130 women, LA-REIMS achieved 94% sensitivity and 83% specificity (AUC: 91.6%) in distinguishing women testing positive (n = 65) or negative (n = 65) for hrHPV. We performed further analysis according to disease severity with LA-REIMS achieving sensitivity and specificity of 91% and 73% respectively (AUC: 86.7%) in discriminating normal from high-grade pre-invasive disease.</h4><h4><strong>INTERPRETATION: </strong>This automated high-throughput technology holds promise as a low-cost and rapid test for cervical cancer screening and triage. The use of platforms like LA-REIMS has the potential to further improve the accuracy and efficiency of the current national screening programme.</h4><h4><strong>FUNDING: </strong>Work was funded by the MRC Imperial Confidence in Concept Scheme, Imperial College Healthcare Charity, British Society for Colposcopy and Cervical Pathology, National Research Development and Innovation Office of Hungary, Waters corporation and NIHR BRC.</h4>
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Project description:Cervical cancer is the fourth most common type of cancer is women worldwide,and it is a major cause of cancer-related deaths in developing countries. Despite the marked reduction oberved in the rates of this disease as a result of screening programs, it is necessary to develop robust biomarkers that can detect the neoplastic early in HPV-related cervical leisons. We performed comparative mRNA seqeuncing from exfoliative cervical cytology from nine Korean women on the Illumina NovaSeq6000 platforms. The pathological tissues mathced to the corresponding cytological samples were simulanously confirmed and were considered to be endpoint diagnosis. The pathological dianoses consisted of three cases of chronic cervicitis, 2 high-grade squamous intraepithelial leisons(HSILs), 2squamous cell carcinomas in situ(CIS); and 2 invasive quamous cell carcinomas (SQCCs), respectively. Using bioinformatics analyses, differentially expressed genes (DEGs; absolute fold change >1; p<0.05) were applied for Gene Ontology (GO), Gene Set EnrichmentAnalysis (GSEA), and protein-protein interaction (PPI) networks.
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Project description:Genome wide DNA methylation profiling of normal and preinvasive cervical smear samples infected with the human papilloma virus (HPV) The Illumina Infinium 27k Human DNA methylation Beadchip v1.2 was used to obtain DNA methylation profiles across approximately 27,000 CpGs in liquid based cytology (LBC) cervical smear samples. Samples included 19 normal smears without HPV, 11 normal smears with HPV infection, and 18 smears with HPV and exhibiting dysplasia. email: a.teschendorff@ucl.ac.uk Keywords: DNA methylation
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Project description:Epigenetic modifications, such as aberrant DNA promoter methylation is frequently observed in cervical cancer. Identification of hypermethylated regions maybe useful for discrimination between normal cervical epithelium and high-grade cervical intraepithelial neoplasia (CIN2/3) or worse may improve current cervical cancer population-based screening programs. In this study, the DNA methylome of high-grade CIN lesions were characterised using genome-wide methylation screening to identify potential biomarkers for early diagnosis of cervical neoplasia. Methyl-DNA Immunoprecipitation (MeDIP) combined with DNA microarray was used to compare DNA methylation profiles of epithelial cells derived from high-grade CIN lesions with normal cervical epithelium resulting in the identification of hypermethylated differentially methylated regions (DMRs). Validation of 9 selected DMRs by MSP or BSP in cervical tissue revealed methylation in 63.2-94.7% high-grade CIN and in 59.3-100% cervical carcinomas. QMSP for the two most significant high-grade CIN-specific methylation markers was applied exploring test performance in a large series of cervical scrapings. Frequency and relative level of methylation were significantly different between normal and cancer samples (p<0.001). Clinical validation of both markers in cervical scrapings from patients referred with an abnormal cervical smear, confirmed that frequency and relative level of methylation were related with increasing severity of the underlying CIN lesion (p<0.001) and the ROC analysis was discriminative (p<0.005). These possible methylation markers represent COL25A1 and KATNAL2 promoters and their observed increased methylation upon progression is in agreement with their biological function (cytoskeleton regulation). In conclusion, our newly identified hypermethylated DMRs represent specific DNA methylation patterns in high-grade CIN lesions and could be potential biomarkers for early detection. Epigenetic modifications, such as aberrant DNA promoter methylation is frequently observed in cervical cancer. Identification of hypermethylated regions maybe useful for discrimination between normal cervical epithelium and high-grade cervical intraepithelial neoplasia (CIN2/3) or worse may improve current cervical cancer population-based screening programs. In this study, the DNA methylome of high-grade CIN lesions were characterised using genome-wide methylation screening to identify potential biomarkers for early diagnosis of cervical neoplasia. Methyl-DNA Immunoprecipitation (MeDIP) combined with DNA microarray was used to compare DNA methylation profiles of epithelial cells derived from high-grade CIN lesions with normal cervical epithelium resulting in the identification of hypermethylated differentially methylated regions (DMRs). Validation of 9 selected DMRs by MSP or BSP in cervical tissue revealed methylation in 63.2-94.7% high-grade CIN and in 59.3-100% cervical carcinomas. QMSP for the two most significant high-grade CIN-specific methylation markers was applied exploring test performance in a large series of cervical scrapings. Frequency and relative level of methylation were significantly different between normal and cancer samples (p<0.001). Clinical validation of both markers in cervical scrapings from patients referred with an abnormal cervical smear, confirmed that frequency and relative level of methylation were related with increasing severity of the underlying CIN lesion (p<0.001) and the ROC analysis was discriminative (p<0.005). These possible methylation markers represent COL25A1 and KATNAL2 promoters and their observed increased methylation upon progression is in agreement with their biological function (cytoskeleton regulation). In conclusion, our newly identified hypermethylated DMRs represent specific DNA methylation patterns in high-grade CIN lesions and could be potential biomarkers for early detection. MeDIP with subsequent microarray analysis was performed on DNA isolated from frozen macrodissected epithelial tissue of CIN3 lesions (n=15) and normal cervices (n=10).
2012-11-01 | E-GEOD-36319 | biostudies-arrayexpress