12312 results for "cancer rna-seq"

in 12.33 milliseconds.

• Chromatin accessibility of clear cell renal cell carcinoma

  To investigate chromatin accessibility in ccRCC, we performed ATAC-seq in 3 ccRCC cell lines (786-O, A498, UMRC-2), and 1 normal kidney epithelium cell line (HK-2), cultured under normoxic (20% O2) and hypoxic (0.05% O2) conditions

  Study EGAS50000001325

• Healthy control cfMeDIP-seq

  This dataset comprises 46 raw fastq files derived from 23 plasma cfDNA samples from 23 healthy (CTL) cancer-free donors. For each sample, 10ng of cfDNA was used for library preparation utilizing the cfMeDIP-seq technology based on the 5mC antibody-immunoprecipitation strategy. Libraries were validated via Bioanalyzer trace analysis and sequenced on the Illumina NovaSeq 6000 platform with paired-end 150-bp model for ~100 million reads per sample.

  Dataset EGAD50000000652

• scRNA-seq of patient-derived PDAC organoids and matched CAFs

  We established direct three-dimensional (3D) co-cultures of primary PDAC organoids and patient-matched CAFs. Single-cell RNA sequencing was performed for three organoid/CAF pairs in mono- and co-culture to uncover transcriptional changes induced by tumor-stroma interaction. Single-cell RNA sequencing data evidenced induction of a pro-inflammatory phenotype in CAFs in co-cultures. Organoids showed increased expression of genes associated with epithelial-to-mesenchymal transition (EMT) in co-cultures and several potential receptor-ligand interactions related to EMT were identified, supporting a key role of CAF-driven induction of EMT in PDAC chemoresistance.

  Study EGAS00001006661

• ICARUS-BREAST01-RNAseq

  Efficacy, safety and biomarker analysis of ICARUS-BREAST01: a phase 2 Study of Patritumab Deruxtecan in patients with HR+/HER2- advanced breast cancer. RNA-squencing is available for 44 samples comprising 22 samples collected at entry into the trial and 22 samples collected during treatment (cycle 1 day 3, cycle 1 day 19, or cycle 2 day 3) from 22 patients.

  Study EGAS50000000543

• Clinical Activity of Combined Telomerase Vaccination and Pembrolizumab in Advanced Melanoma: Results from a Phase I Trial

  Thirty patients with advanced melanoma were recruited to a clinical trial investigating the therapeutic cancer vaccine, UV1, in combination with pembrolizumab. Whole exome sequencing was performed on melanoma biopsies to determine tumor mutational burden and association with response to therapy. RNA sequencing of matched tumor biopsies to evaluate gene expression profiles at baseline and post-treatment.

  Study EGAS00001007210

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  Tips on how to search Tip 1: Use simple and clear keywords No matter what you are searching: a study, a dataset or a committee; the search will look recursively in all the fields of all the entities in the database. Thus, there is no need to specify which type of data you are looking for. Try to use single and specific words and avoid long and complex expressions. Try it out: cancer rna-seq instead of cancer studies using RNA-seq. Tip 2: Narrow your results using search operators Search operators give you more control over the results. They manage the way keywords are used to restrict the search. By default, the search engine applies an OR operator when a space is inserted between two words. Include AND (capitalised) between the keywords in your search in order to show only pages containing both terms. Try it out: Search all the studies based on transcriptomic data, including those ones that use RNA sequencing to obtain this data. Tip 3: Do not worry about details The search engine automatically checks for the most common spelling mistakes of given words and suggests the use of the correct version. It also ignores capital letters and most punctuation so none of these mistakes will hamper the accuracy of your search. Besides, when using a combination of words, the search engine will suggest similar combinations with a higher number of results. Tip 4: Use words that are likely to appear within the text Unfortunately, our search engine is not able to interpret words yet. Therefore, words which do not appear in the body text will not return any result despite them denoting a group of present terms. For instance, if we are looking for studies focused on neurological diseases, using neurological as a keyword would not be the best option. Instead, you should use the name of each neurological disease such as bipolar disorder, schizophrenia or depression. In the same fashion, incomplete words do not return any result. Please include an asterisk (*) before and/or after the incomplete word in order to allow for partial matching. Try it out: canc* instead of canc(without wildcard). Also you can try out *oma, or even *geneti* Tip 5: Narrow down your search by filtering the results You can filter the results by the following options: general, study, and dataset. Under these categories, you will find more sub-filters to refine your search. Make sure that you click on Apply in order to filter the results. You can clear the filtering anytime. Advanced Searches Additional example queries: Search for DUO:0000005 and word lung example query Or write: DUO:0000005 AND lung Search for an exome sequencing study in dbGaP example query Or write: study_type: "exome sequencing" AND repository:dbGaP Search for an exome sequencing study in EGA example query Or write: study_type: "exome sequencing" AND repository:ega Search for a dataset that is at the EGA beacon and with type "Whole genome sequencing" and with number of samples between 100 and 2000 and with technology "Illumina HiSeq 2000" example query Or write: is_in_beacon:true AND dataset_types:"Whole genome sequencing" AND samples:[100 TO 2000] AND dataset_technologies:"Illumina HiSeq 2000" Search for PMIDS example query Or write: pmids:32025007

  Documentation discovery/metadata/search-box

• Single cell transcriptomic landscape of pediatric B-cell acute lymphoblastic leukemia: dissection of transcriptional heterogeneity and B-cell developmental state

  Introduction: Genomic analyses of bulk ALL samples have improved our understanding of the genetic basis and risk stratification of B-ALL, but do not directly examine intratumor heterogeneity or enable inference of leukemia developmental state and cell of origin. Methods: We profiled 89 B-ALL samples by single-cell RNA-seq (scRNA-seq) (10X Genomics 5’v2) and compared them to a scRNA-seq reference map of normal human B-cell development. Results: Analysis of heterogeneity of inferred DNA copy number alterations at single cell level showed that aneuploid ALL with near haploidy and hyperdiploid harbor chromosomal losses or gains in all blasts, consistent with early, synchronous origin rather than sequential accumulation. Intra-sample heterogeneity was driven by five gene expression signatures: cell cycle, metabolism, differentiation, inflammation, refining leukemic subtyping. An atlas of B cell development was established to map B-ALL samples to specific developmental states. Hyperdiploid samples resembled pro-B cells, while MEF2D-r and TCF3::PBX1 showed high pre-B abundance. Patients with high early lymphoid abundance had poorer outcomes. Conclusions: Understanding variation in transcriptional programs and developmental states of B-ALL blasts by sc transcriptome refines existing clinical and genomic classifications and provides novel prognostic markers.

  Study EGAS00001007512

• Raw sequencing of single-cell RNA-seq data of a phase II clinical trial (NCT03419481)

  This is a single-arm Phase II trial of pembrolizumab in patients with hepatitis B virus-related hepatocellular carcinoma with parallel study on baseline and serial change in the immune environment.

  Study EGAS50000001315

• Expressed fusion transcripts in rare bone tumours

  mRNA-Seq on total RNA from primary osteoblastomas and phosphaturic mesenchymal tumours, focussing on fusion transcript expression

  Dataset EGAD00001000990

• Human T-bet governs innate and innate-like adaptive IFN-g immunity against mycobacteria.

  We report a patient with mycobacterial disease due to inherited deficiency of the transcription factor T-bet. PBMCs from the patient and his heterozygous father were analyzed with scRNA-seq. these represent 2 single cell RNA samples generated using the 0xGenomics technology and being processed through cell ranger.

  Dataset EGAD00001006623