12054 results for "cancer rna-seq"

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• Microvascular Permeability, Inflammation, and Lesion Physiology in Endometriosis: A Microphysiological Systems Approach

  The goal of the study is to identify novel molecular pathways involved in the etiology of endometriosis and to develop in vitro model systems to enable the study of the mechanisms regulating reproductive function. Using a physiomimetic modeling approach, primary tissue samples of the human endometrium derived from patients diagnosed with and without endometriosis and/or endometriotic diseases (e.g. adenomyosis) were used for secondary analysis using multi-omic systems biology analysis and to build tissue engineered models. We performed single cell RNA sequencing (scRNA-Seq, N=6)) to study the cellular and transcriptomic composition of the endometrial tissue microenvironment and proteomics (LC-MS/MS) to study protein-level signaling pathways and the matrisome of the native endometrium. Systems biology analysis of these datasets helped identified a putative dysregulated inflammatory signaling pathway in endometriosis patients compared to controls. Moreover, subsets from these scRNA-Seq and proteomic datasets were used to aid the design of a synthetic extracellular matrix hydrogel as an in vitro platform for the culture of endometrial cells. Tissue-engineered synthetic PEG hydrogel enables the simultaneous co-culture of endometrial epithelial organoids and stromal fibroblasts in a fully defined, 3D, and tunable environment. We demonstrated that the in vitro co-culture model recapitulates key phenotypic processes observed across the human menstrual cycle in response to sex hormone stimulation through both biochemical and transcriptomic (bulk RNA-sequencing) analysis. This model will enable future mechanistic studies that examine origins of endometriotic disease phenotypes. The transcriptomic data generated for this study including the scRNA-Seq data from primary human endometrial tissues and the bulk RNA-Seq datasets from the experimental co-culture models is available through dbGAP.

  Study phs003326

• p200503_fn2

  RNA-Sequencing data of Fusobacterium nucleatum (Fna 71) treated CRC patient derived (CAF180, CAF181) and CAF05 cancer associated fibroblasts (CAFs).

  Study EGAS50000001121

• Single cell RNAseq and TCRseq data from tumor and blood samples from 4 patients with muscle invasive bladder cancer

  For this dataset we performed single cell RNAseq paired with single cell TCR-seq on tumor and blood samples from 4 patients. This dataset contains 4 tumor samples as well as 4 blood samples. Each sample is made up of 2 sets of paired fastq files. The first pair contains reads corresponding to RNA transcripts (_Transcripts in file name), while the second pair contain reads corresponding to TCRs (_VDJ in file name). Sequenced on the Illumina NovaSeq6000 platform in a paired-end run using an SP flow cell (v1.5, 300 cycles).

  Dataset EGAD50000001381

• ChIP-seq in colorectal cancer and paired adjacent normal mucosa

  Chip-seq samples for 20 colorectal patients with paired adjacent normal mucosa to characterize H3K27ac and H3K4me1.

  Study EGAS00001005303

• FBSeq: RNA sequencing of human fetal brain.

  RNA sequencing of 120 human fetal brains (aged 12-19 post-conception weeks) was performed as part of a Medical Research Council (U.K.) funded project (MR/L010674/2) to investigate genetic effects on gene expression in the developing human brain and their role in neuropsychiatric disorders. Human fetal brain tissue from elective terminations of pregnancy was provided by the Human Developmental Biology Resource (HDBR) (http://www.hdbr.org), with ethical approval (#08/H0712/34), and with consent for use of fetal material for medical research provided by female donors. Total RNA was extracted from half of the available brain tissue from each fetus using Tri-Reagent and treated with TURBO DNase. RNA-Seq libraries were prepared using 1µg of purified total RNA and the Illumina TruSeq Stranded Total RNA Library Prep kit, following ribosomal RNA depletion. Libraries were sequenced on the Illumina HiSeq 2500 or HiSeq 4000 systems, generating at least 50 million read pairs per sample.

  Study EGAS00001003214

• DNA methylation database for gynecological cancer detection, classification and assay development

  DNA methylation changes are associated with cancer the best predictor of the presence of cancer. In order to detect and classify gynecological cancer, we performed MeD-seq to generate DNA methylation profiles of vulvar, cervical, endometrial, fallopian tube and ovarian cancers and their associated healthy tissues. DNA methylation profiles were used to find general cancer, regional cancer and specific cancer biomarkers that can be used for cancer detection, classification and treatment monitoring.

  Study EGAS50000000417

• Exploring Genomic Mutations in Gastric Cancer among Japanese Populations

  We conducted a comprehensive analysis of whole-genome and RNA sequences obtained from biopsy samples of 65 patients with advanced gastric cancer prior to chemotherapy treatment.

  Study JGAS000754

• Non-coding RNAs in breast cancer

  Using RNA CaptureSeq we annotated non-coding RNAs transcribed from genome intervals surrounding breast cancer risk signals in a range of mammary-derived tissue and cell lines.

  Study EGAS00001003353

• Sequencing of patient tissue and patient derived organoids in colon cancer

  Multiome (ATAC + RNA) sequencing of cancerous primary tissue and cancer organoids derived from the same patient. Nanopore and whole exome sequencing of selected samples.

  Study EGAS00001008067

• Tumor detection by analysis of both symmetric- and hemi-methylation of plasma cell-free DNA

  Aberrant DNA methylation plays a critical role in tumorigenesis. While DNA methylation has been used for cancer detection and classification, DNA hemi-methylation, a novel epigenetic mark has not been analyzed extensively in cancer epigenomes. Here we report a strand-specific (ss) sequencing method (MeDIP-Seq) for plasma cell free (cf) DNA (sscf-MeDIP-Seq), which can analyze both symmetrically methylated DNA regions (DMRs) as well as hemi-methylated regions (DHMRs). Using the sscf-MeDIP-Seq method, we analyzed plasma cfDNA methylomes of 271 samples from subjects with liver cancer and brain cancer and from individuals without cancer (controls). Among them, 215 samples were chosen as the discovery cohort for the identification of DMRs and DHMRs specific in each subject group and for the training of machine learning models of multi-cancer detection (MCD) using DMRs, DHMRs and DMRs+DHMRs as inputs. These models were then used to predict the 56 samples in the validation cohort. We found that models trained with DMRs+DHMRs as inputs in general outperformed models trained with DMRs or DHMRs alone, with AUROC being 0.971, 0.981, and 0.99 in predicting control, liver and brain cancer samples in the validation cohort, respectively, by the DMR+DHMR-trained models.

  Study phs003462