JiWenAEL RNA-Seq Dataset - samples
Ontology highlight
ABSTRACT:
PROVIDER: EGAD00001003412 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:RNA-seq dataset for MPNST patient tissue samples
| PRJNA593503 | ENA
Project description:In this study, mutations present in a series of human melanomas (stage IV disease) will be determined, using autologous blood cells to obtain a reference genome. From each of the samples that are analyzed, tumour-infiltrating T lymphocytes have also been isolated. This offers a unique opportunity to determine which (fraction of) mutations in human cancer leads to epitopes that are recognized by T cells. The resulting information is likely to be of value to understand how T cell activating drugs exert their action.
| S-ECPF-ERAD-61 | biostudies-other
Project description:CTCF ChIP-seq of 39 primary samples derived from human acute leukemias, namely AML, T-ALL and mixed myeloid/lymphoid leukemias with CpG Island Methylator Phenotype (CIMP). Due to patient confidentiality considerations, the raw data files for this dataset have been deposited to the EGA controlled-access archive under the accession numbers EGAS00001007094 (study); EGAD00001011059 (dataset).
2023-08-31 | E-MTAB-13117 | biostudies-arrayexpress
Project description:This dataset contains bulk RNA sequencing data from primary tumor biopsy samples of patients with prostate adenocarcinoma. TPM-normalized expression values are provided along with sample metadata. The data were generated to support transcriptomic profiling of human prostate tumors and are related to a companion single-cell RNA-seq dataset from the same cohort.
2025-11-19 | GSE297742 | GEO
Project description:H3K27ac ChIP-seq of 79 primary samples derived from human acute leukemias, namely AML, T-ALL and mixed myeloid/lymphoid leukemias with CpG Island Methylator Phenotype (CIMP). In addition, 4 samples derived from CD34+ cord blood cells of healthy donors were included. Due to patient confidentiality considerations, the raw data files for this dataset have been deposited to the EGA controlled-access archive under the accession numbers EGAS00001007094 (study); EGAD00001011060 (dataset).
2023-08-31 | E-MTAB-13119 | biostudies-arrayexpress
Project description:Description not provided
| EGAD00001006142 | EGA
Project description:BackgroundGlioblastoma (GBM) is the most common primary malignant brain tumor in adults. For patients with GBM, the median overall survival (OS) is 14.6 months and the 5-year survival rate is 7.2%. It is imperative to develop a reliable model to predict the survival probability in new GBM patients. To date, most prognostic models for predicting survival in GBM were constructed based on bulk RNA-seq dataset, which failed to accurately reflect the difference between tumor cores and peripheral regions, and thus show low predictive capability. An effective prognostic model is desperately needed in clinical practice.MethodsWe studied single-cell RNA-seq dataset and The Cancer Genome Atlas-glioblastoma multiforme (TCGA-GBM) dataset to identify differentially expressed genes (DEGs) that impact the OS of GBM patients. We then applied the least absolute shrinkage and selection operator (LASSO) Cox penalized regression analysis to determine the optimal genes to be included in our risk score prognostic model. Then, we used another dataset to test the accuracy of our risk score prognostic model.ResultsWe identified 2128 DEGs from the single-cell RNA-seq dataset and 6461 DEGs from the bulk RNA-seq dataset. In addition, 896 DEGs associated with the OS of GBM patients were obtained. Five of these genes (LITAF, MTHFD2, NRXN3, OSMR, and RUFY2) were selected to generate a risk score prognostic model. Using training and validation datasets, we found that patients in the low-risk group showed better OS than those in the high-risk group. We validated our risk score model with the training and validating datasets and demonstrated that it can effectively predict the OS of GBM patients.ConclusionWe constructed a novel prognostic model to predict survival in GBM patients by integrating a scRNA-seq dataset and a bulk RNA-seq dataset. Our findings may advance the development of new therapeutic targets and improve clinical outcomes for GBM patients.
| S-EPMC9120724 | biostudies-literature
Project description:RNA-seq dataset for MPNST cell lines
| PRJNA593502 | ENA
Project description:RNA-Seq dataset as part of HGSVC3
| PRJEB75192 | ENA