14976 results for "cancer studies using rna-seq"

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• Targeted pancancer RNA-Seq

  This dataset contains targeted RNA-Seq generated with the Illumina RNA Pan-Cancer panel. It comprises 2200 cancers from children and adults. The cancers represent a wide range of CNS, solid and haematopoeitic tumours. Samples are from pathology specimens including FFPE, fresh frozen, bone marrow aspirate and peripheral blood.The data is used for tumour driver identification. SNVs, indels and fusion detection.

  Study EGAS50000000700

• The Transcriptomic Landscape of Oncogenic PI3K Reveals Key Functions in Splicing and Gene Expression Regulation

  This study includes RNA-seq of paired tumor biopsies from PIK3CA-mutated breast cancer patients that underwent treatment with PI3Kα inhibitors.

  Study phs002840

• Case-control study with RNA-seq transcriptome between ASD patients and non ASD controls.

  Aim: To elucidate gene expression differences in the cerebellar tissues between ASD and normal control segmented by age. Object: The cerebellar tissues used in our study are from 16 autistic patients and 17 control patients. Method: We investigated the mRNA expression differences using RNA-seq between ASD and controls by age. We further investigated mRNA isoform expressions segmented by age.

  Study JGAS000668

• COVID Response Study 2 (COVRES-2)

  COVID Response Study 2 (COVRES-2) - is a collaborative effort between researchers at Ulster University and the Western Health and Social Care Trust. The study aims to identify temporal immune responses associated with COVID-19 severity. A total of n=20 individuals (10=Hospitalised/10=Non-hospitalised) were recruited following a positive PCR result for COVID-19. Current studies 1. Distinct Omicron longitudinal memory T cell profile and T cell receptor repertoire associated with COVID-19 hospitalisation At 3 months post-infection T Cell Receptor (TCR) sequencing and RNA Sequencing were preformed to investigate immunopathological signatures associated with clinical outcome and recovery. This dataset contains both TCR-seq and RNA-seq data for all participants that took part in this study.

  Dac EGAC50000000594

• Single-cell RNA sequencing of metastatic colorectal cancer organoids treated with cetuximab

  Single-cell RNA sequencing was performed on metastatic colorectal cancer (mCRC) organoids derived from a cetuximab-sensitive patient-derived xenograft (PDX) model. The objective of this analysis was to investigate, at single-cell resolution, the transcriptional changes previously identified in bulk RNA-seq analyses of PDX models exposed to anti-EGFR therapeutic pressure.

  Study EGAS50000001460

• Paired-end RNA-seq analysis of the TERT promoter mutant GBM cell lines

  This dataset contains 9 RNA-seq BAM files. RNA was derived from TERT promoter mutant GBM cell lines and sequenced on an Illumina HiSeq4000 sequencer with paired-end reads and an average read length of 50 base pairs. Reads were aligned with TopHat (v2.0.14) using a GENCODE V19 transcriptome-guided alignment.

  Dataset EGAD00001005435

• ECM-free patient-derived organoids preserve diverse prostate cancer lineages and uncover in vitro-enriched cell types

  Patient-derived organoids (PDOs) hold promise for modeling various cancers, yet their application in prostate cancer (PCa) has been limited due to low culture success and the predominance of non-tumor-like cells. Using 164 PCa samples and optimizing culture conditions, we demonstrate that an extracellular matrix (ECM)-free culture method improves PDO take-rates, fostering cell populations with luminal and PCa-specific characteristics. Single-cell RNA sequencing (scRNA-seq) reveals that these ECM-free PDOs closely mirror their original tumors at the transcriptomic level and maintain a diverse array of tumor cell types, unlike traditional Matrigel-based cultures which predominantly yield benign-like cells. This study introduces a PCa PDO single-cell atlas, providing a valuable resource for advancing prostate cancer research.

  Study EGAS50000000807

• Transcriptomics of Liver and PBMCs in Alcohol-Associated Liver Disease

  This study was designed to characterize the transcriptome of alcohol-associated hepatitis using RNA sequencing (RNA-seq) technology for both liver tissue biopsies and peripheral blood mononuclear cells (PBMCs) extracted from blood samples, in order to identify expressed genes that are markers of the disease. To distinguish genes that differentiate alcohol-associated hepatitis from other inflammatory liver diseases, we compared the RNA-seq data generated from participants with alcohol-associated hepatitis, alcohol-associated cirrhosis, chronic hepatitis C infection, non-alcohol-associated fatty liver disease, and healthy controls. We utilized computational approaches to demonstrate that gene expression data generated from either liver tissue or blood samples can be used to identify a small set of gene biomarkers to distinguish between these liver diseases. The raw RNA-seq data will be available through dbGaP.

  Study phs003112

• Genomic and Genetic Analysis of Brain Tumors and Analysis of Their Clinicopathological Significance

  The treatment outcome of glioma patients remains disappointing, and more studies are needed to improve therapeutic strategies. In particular, it is crucial to understand the mechanisms underlying resistance to treatment and malignant transformation, as well as the intratumoral heterogeneity and interaction of tumor cells and host immunity. Recent advances in omics analysis techniques, such as next-generation sequencing (NGS), have enabled us to obtain omics data more easily and accelerated our ability to integrate this information and precisely characterize cases. In our project, we have already collected more than 100 pairs of human glioma/normal tissue samples, of which some are multiple specimens collected from initial and recurrent tumors, and some include multiple specimens collected from different regions within a single tumor. We used our in-house algorithms, composed of alignment and variant calling programs, to analyze omics data from multiple platforms, such as whole-exome sequencing (WES), RNA sequencing (RNA-seq), and methylation array, with our original cohort of glioma patients. Using several informatics tools, the information about genetic and epigenetic status and expression signatures were analyzed. Those data are expected to provide key information for investigating the mechanisms discussed above. Through this project, we hope to improve strategies for personalized therapy based on comprehensive omics data.

  Study JGAS000004

• Epigenetic Landscape of Human Parathyroids

  The goal of this study is to produce a comprehensive map of the chromatin landscape of human parathyroids. Data from 20 subjects with parathyroid adenoma causing primary hyperparathyroidism were used. For the sequencing platform, Illumina HiSeq 2000 with V3 high-output kits were used. For the data processing pipeline, reads were aligned to hg19 using Bowtie or BWA. For RNA-seq data, transcript levels were estimated per gene to RPKM using Cufflink. For ChIP-seq and ATAC-seq data, enrichment peaks were estimated compared to inputs or background using MACS2 after merging reads from replicates. For DNase-seq, significant peaks compared to background distribution were computed using Hotspot. For Hi-C data, significant interactions were detected using Juicer HiCCUPS and 5 kb resolution for the merged data from replicates. Chromatin states for parathyroids were computed using ChromHMM based on ChIP-seq profiles in parathyroids of H3K4me3, H3K4me1, H3K27ac, H3K36me3, H3K27me3 and H3K9me3 and the emission matrix from 18 states pre-trained in the Roadmap Epigenomics. Gene expression, binding positions of transcription factor, open chromatin regions, 3D-chromatin interaction, and chromatin states are submitted.

  Study phs003302

• GWA Study of Oral Cavity, Pharynx and Larynx Cancers in European, and North and South American Populations - CIDR

  There is striking geographical variation in head and neck cancer (HNC) incidence globally. Some of the highest incidence rates have been observed in Southern and Eastern Europe and South America. The goal of this project is to assess susceptibility loci across and within ancestral backgrounds, using the All of Us array. Ten studies have come together in this initiative.

  Study phs002503

• Evolution of Core Archetypal Phenotypes in High-Grade Serous Ovarian Cancer (HGSOC)

  The evolution of resistance in high-grade serous ovarian cancer (HGSOC) cells in response to chemotherapy is only partially understood. To uncover phenotypic changes associated with chemotherapy resistance, we performed single-cell RNA-sequencing (scRNA-seq) or whole-genome sequencing of 23 total patients, with longitudinal samples collected from 9, treatment-naïve samples from 8 patients, and post-treatment samples from 6 patients. Our study revealed that the HGSOC transcriptome could be described using three core archetypal phenotypes, defined as oncogenic tasks that describe majority of the transcriptional data. We implemented a multi-task learning approach to identify the biological tasks of each archetype and identified metabolism and proliferation, cellular defense response, and DNA repair signaling. The “metabolism and proliferation” archetype evolved during treatment and was enriched in cancer cells from patients that received multiple-lines of treatment and had elevated tumor burden indicated by CA-125 levels. The emergence of archetypes was not consistently linked to specific whole-genome driver mutations. However, archetypes were closely associated with sub-clonal clusters at the single-cell level, indicating that cancer cell sub-populations specialize in particular biological tasks. Our study reveals a defining principle of ovarian tumor evolution using temporal scRNA-seq profiles, identifies the key phenotypic features across patients and links metabolic shifts with a response to chemotherapy.

  Study phs002294

• Comparison of capture-based method for transcriptome profiling of formalin-fixed paraffin embedded tumor samples

  Background: The need for fresh frozen (FF) tissue limits implementing RNA sequencing (RNA-seq) in the clinic. The majority of clinical samples are stored as formalin-fixed, paraffin-embedded (FFPE) tissues. Exome capture platforms have been developed for RNA-seq from FFPE samples. However, these methods have not been systematically compared. Methods: Transcriptomic analysis of 32 FFPE tumor samples from 11 patients was performed using three exome capture-based methods: Agilent SureSelect V6, TWIST NGS Exome, and IDT XGen Exome Research Panel. We compared these methods to TruSeq RNA-seq of fresh frozen (FF-TruSeq) tumor samples from the same patients. We assessed the recovery of clinically relevant biological features. Results: The Spearman's correlation coefficients between the global expression profiles of the three capture-based methods from FFPE and matched FF-TruSeq were high (rho = 0.72-0.9, p < 0.05). A significant correlation between the expression of key immune genes between individual capture-based methods and FF-TruSeq (rho = 0.76-0.88, p < 0.05) was observed. All exome capture-based methods reliably detected outlier expression of actionable genes, including ERBB2, MET, NTRK1, and PPARG. In urothelial cancer samples, the Agilent assay was associated with the highest molecular subtype concordance with FF-TruSeq (Cohen's k = 0.7, p < 0.01). Both Agilent and IDT detected all the clinically relevant fusions that were initially identified in FF-TruSeq. Conclusion: All exome capture-based methods had comparable performance and concordance with FF-TruSeq. By enabling the interrogation of FFPE tumor samples, our findings will enable the implementation of RNA-seq in the clinic to guide precision oncology approaches.

  Study EGAS00001005255

• RNA-seq of multi-regional CRC samples

  BAM files of RNA sequencing (RNA-seq) experiment on multi-regional colorectal cancer (CRC) samples. 58 samples corresponding to 16 patients were sequenced and there is one BAM file for each sample.

  Dataset EGAD00001006164

• Whole Exome Sequencing and RNA Sequencing of High Grade Serous Ovarian Cancer in Black and White Patients

  In this study, we generated new RNA and whole exome sequencing data from high-grade serous ovarian cancer tumor samples of self-identified Black and White individuals. Our RNA-Seq analysis revealed that, while the prevalence of subtypes varied across racial groups, the cluster-specific gene expression patterns were consistent. Despite these differences in subtype prevalence, the subtype-specific survival rates were similar across racial groups. Additionally, we used the WES-Seq data to characterize mutational signatures and their underlying causes in Black high-grade serous ovarian cancer (HGSC) cases, and compared the prevalence and distribution of these signatures with those in White TCGA cases.

  Study phs003632