D4796663-c0d5-4c07-b3d8-e121e21d8b61 - samples
Ontology highlight
ABSTRACT:
PROVIDER: EGAD00001008091 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:Although recent advances in high-throughput technology have provided many insights into gastric cancer (GC), few reliable biomarkers for handling diffuse type GC are identified. Here, we aim to identify a signature classifying high risk diffuse type GC. To identify molecular subtypes of diffuse type GC, we generated RNA-seq based transcriptome data, which were generated using normal mucosa and tumor cells from 140 fresh frozen tissues including diffuse type GCs (n = 107). Unsupervised hierarchical cluster analysis of the RNA-seq data revealed three distinct subgroups of GC. Based on these subtypes, we generated a signature reflecting the best characteristics of aggressive diffuse type GC. When estimating prognostic value, the signature showed a strong prediction ability and an independent clinical utility in diffuse type GC patients. Our signature represents a promising diagnostic tool for the identification of diffuse type GC patients that have a high risk of poor clinical behavior.
2020-03-18 | GSE113255 | GEO
Project description:Several ovarian cancer tumors from different anatomical sites and therapy statuses. A GUI to explore the data can be found at: https://visium-shiny-9hzfl.ondigitalocean.app/ Code to run the shiny app (same output as the GUI): https://github.com/kwells4/visium_public_shiny Code to replicate the analysis can be found: https://github.com/kwells4/visium_ovarian_cancer
2022-10-10 | GSE213699 | GEO
Project description:Malaria inflicts the highest rate of morbidity and mortality among the vector-borne diseases. The dramatic bottleneck of parasite numbers that occurs in the gut of the obligatory mosquito vector provides a promising target for novel control strategies. Using single-cell transcriptomics, we analyzed Plasmodium falciparum development in the mosquito gut, from unfertilized female gametes through the first 20 hours post blood feeding, including the zygote and ookinete stages. This study revealed the temporal gene expression of the ApiAP2 family of transcription factors, and of parasite stress genes in response to the harsh environment of the mosquito midgut. Further, employing structural protein prediction analyses we found several upregulated genes predicted to encode intrinsically disordered proteins (IDPs), a category of proteins known for their importance in regulation of transcription, translation and protein-protein interactions. IDPs are known for their antigenic properties and may serve as suitable targets for antibody or peptide-based transmission suppression strategies. In total, this study uncovers the P. falciparum transcriptome from early-to-late parasite development in the mosquito midgut, inside its natural vector, which provides an important resource for future malaria transmission-blocking initiatives. Single-cell data can be visualized interactively via https://mubasher-mohammed.shinyapps.io/shinyapp/ In-house bash, R code scripts and data that were implemented in this study are available on GitHub https://github.com/ANKARKLEVLAB/Single-cell-P.falciparum-midgut .
2023-02-16 | GSE222586 | GEO
Project description:Serum miRNA-based risk prediction for stroke
| PRJNA480974 | ENA
Project description:Despite continual efforts to establish pre-operative prognostic model of gastric cancer by using clinical and pathological parameters, a staging system that reliably separates patients with early and advanced gastric cancer into homogeneous groups with respect to prognosis does not exist. With use of microarray and quantitative RT-PCR technologies, we exploited series of experiments in combination with complementary data analyses on tumor specimens from 161 gastric cancer patients. Various statistical analyses were applied to gene expression data to uncover subgroups of gastric cancer, to identify potential biomarkers associated with prognosis, and to construct molecular predictor of risk from identified prognostic biomarkers.Two subgroups of gastric cancer with strong association with prognosis were uncovered. The robustness of prognostic gene expression signature was validated in independent patient cohort with use of support vector machines prediction model. For easy translation of our finding to clinics, we develop scoring system based on expression of six genes that can predict the likelihood of recurrence after curative resection of tumors. In multivariate analysis, our novel risk score was an independent predictor of recurrence (P=0.004) in cohort of 96 patients, and its robustness was validated in two other independent cohorts. We identified novel prognostic subgroups of gastric cancer that are distinctive in gene expression patterns. Six-gene signature and risk score derived from them has been validated for predicting the likelihood of survival at diagnosis.
2011-07-19 | GSE13861 | GEO
Project description:Heteromeric protein complexes are key macromolecular machines of the cell, but their description remains incomplete. We previously reported an experimental strategy for global characterization of native protein assemblies based on chromatographic fractionation of biological extracts coupled to precision mass spectrometry analysis (CF/MS), but the resulting data can be challenging to process and interpret. Here, we describe EPIC (Elution Profile-based Inference of Complexes), a software toolkit for automated scoring of CF/MS data for large-scale determination of high-confidence physical interaction networks and macromolecular assemblies from diverse biological specimens. As a case study, we used EPIC to map the global interactome of Caenorhabditis elegans, defining 590 putative worm protein complexes linked to diverse biological processes, including assemblies unique to nematodes. The EPIC software is freely available as a Jupyter notebook packaged in a Docker container (https://hub.docker.com/r/baderlab/bio-epic/), and the open source code is available via GitHub (https://github.com/BaderLab/EPIC).
2019-07-16 | PXD011182 | Pride
Project description:Despite continual efforts to establish pre-operative prognostic model of gastric cancer by using clinical and pathological parameters, a staging system that reliably separates patients with early and advanced gastric cancer into homogeneous groups with respect to prognosis does not exist. With use of microarray and quantitative RT-PCR technologies, we exploited series of experiments in combination with complementary data analyses on tumor specimens from 161 gastric cancer patients. Various statistical analyses were applied to gene expression data to uncover subgroups of gastric cancer, to identify potential biomarkers associated with prognosis, and to construct molecular predictor of risk from identified prognostic biomarkers.Two subgroups of gastric cancer with strong association with prognosis were uncovered. The robustness of prognostic gene expression signature was validated in independent patient cohort with use of support vector machines prediction model. For easy translation of our finding to clinics, we develop scoring system based on expression of six genes that can predict the likelihood of recurrence after curative resection of tumors. In multivariate analysis, our novel risk score was an independent predictor of recurrence (P=0.004) in cohort of 96 patients, and its robustness was validated in two other independent cohorts. We identified novel prognostic subgroups of gastric cancer that are distinctive in gene expression patterns. Six-gene signature and risk score derived from them has been validated for predicting the likelihood of survival at diagnosis. 65 primary gastric adenocarcinoma, 6 GIST and 19 surrounding normal fresh frozen tissues were used for microarray. All the tissues were obtained after curative resection after pathologic confirm at Yonsei cancer center(Seoul, Korea). Microarray experiment and data analysis were done at Dept. of systems biology, MDACC DNA microarray (Illumina human V3)
2011-07-18 | E-GEOD-13861 | biostudies-arrayexpress
Project description:We report the DeepMS1, an approach combining deep learning-based retention time, ion mobility, detectability prediction, and linear regression-based scoring for MS1 feature identification.
2024-04-28 | PXD046171 |
Project description:We report the DeepMS1, an approach combining deep learning-based retention time, ion mobility, detectability prediction, and linear regression-based scoring for MS1 feature identification.
2024-02-16 | PXD049457 |
Project description:Abstract Here we present IPSA, an innovative web-based spectrum annotator that visualizes and characterizes peptide tandem mass spectra. A tool for the scientific community, IPSA can visualize peptides collected using a wide variety of experimental and instrumental configurations. Annotated spectra are customizable via a selection of interactive features and can be exported as editable scalable vector graphics to aid in the production of publication-quality figures. Single spectra can be analyzed through provided forms, while multiple peptide spectral matches can be uploaded directly to the server as CSV, MGF, or mzML files. IPSA facilitates the characterization of experimental MS/MS performance through the optional export of fragment ion statistics from one to many peptide spectral matches. This resource is made freely accessible at http://interactivepeptidespectralannotator.com, and the source code is available for inspection at https://github.com/dbrademan/IPSA-dev for custom implementations. This repository contains the raw data, sequence databases, and peptide identifications utilized in the manuscript.
2019-05-15 | PXD011695 | Pride