15037 results for "cancer studies using rna-seq"

in 15.83 milliseconds.

• Whole exome sequencing of colorectal cancer

  To elucidate immune status of solid tumors including gastrointenstinal cancer, we performed whole exome sequencing of colorectal cancer 7 patients.

  Study JGAS000279

• Identification of genetic mutations characteristic for recurrence of serous ovarian cancer.

  Establishment of novel therapeutic strategy to overcome intractable cancer by molecular profiling of serous ovarian cancer

  Study JGAS000104

• BHD-associated kidney cancer

  Background Birt-Hogg-Dub? (BHD) syndrome is a hereditary kidney cancer syndrome, which predisposes patients to develop kidney cancer, cutaneous fibrofolliculomas and pulmonary cysts. The responsible gene, FLCN is a tumor suppressor for kidney cancer which plays an important role for energy homeostasis through the regulation of mitochondrial oxidative metabolism. However, how FLCN-deficiency leads to renal tumorigenesis is unclear. Results Whole-exome sequencing analysis of BHD-associated kidney cancer revealed that copy number variations (CNV) of BHD-associated kidney cancer are considerably different from those already reported in sporadic cases. Somatic mutation analysis revealed BHD-associated kidney cancer has quite few common mutations in specific genes; however, chromatin remodeling genes in total were frequently mutated in BHD-associated kidney cancer (17/29 tumors, 59%). Metabolite analysis revealed that glycolysis, pentose phosphate pathway and lipid metabolism were upregulated whereas the rate of beta oxydation and TCA cycle is decreased in BHD-associated kidney cancer compared to normal adjacent kidney. Conclusions BHD-associated kidney cancer displays a unique molecular characteristics which is totally different from that of sporadic kidney cancer, providing the mechanistic insight into tumorigenesis under FLCN deficiency as well as the foundation for the development of novel therapeutics for kidney cancer.

  Study JGAS000115

• Comparison of WGBS, EPIC array, EM-seq, and Nanopore sequencing in assessing DNA methylation marks

  Bisulfite sequencing has long been the default method for analyzing methylation marks due to its single base resolution. However, the associated DNA degradation poses a concern for fragmented samples. Although several methods have been proposed to circumvent this issue, there has yet to be a clear consensus on which method is best suited for detecting methylation. We present an overview of the two mostly used and two alternative approaches: whole-genome bisulfite sequencing (WGBS), Illumina methylation microarray (EPIC), Enzymatic Methyl-sequencing (EM-seq) and third-generation sequencing by Oxford Nanopore Technologies (ONT). We evaluated DNA methylation levels in three human genomes derived from tissue, cell line, and whole blood.

  Study EGAS00001008014

• ABIS_1_MeDIP-seq

  We investigated changes in whole genome DNA-methylation in a group of 48 healthy five-year-old children differentiated by their hair cortisol levels. By combining this novel biomarker for chronic stress with methylated DNA immunoprecipitation sequencing (MeDIP-seq), we found that high cortisol were associated with a genome-wide decrease in DNA-methylation targeted to specific zinc finger transcription factor binding sites, repetitive elements and genes important for neurodevelopment and calcium transport. Large scale hypomethylation of repetitive regions, as well as a deficient calcium transport, have been reported in many stress related diseases, as well as in many cancers and biological aging. Thus our findings may aid in our understanding of how susceptibility to these diseases develops.

  Study EGAS00001001099

• RNA Editing Transcriptome

  Transcriptome sequencing data of tumor and 10 matched normal samples of the EGAS00001000495 project

  Dataset EGAD00001000627

• Colorectal_organoids_and_tumoroids___pulldown

  Targeted capture of cancer gene panel bait set in single cell derived organoids from colon tissue and colorectal cancer from 1 patient.

  Study EGAS00001000869

• EGAD00010000652

  Genotyped samples using Illumina HumanOmni2.5

  Dataset EGAD00010000652

• EGAD00010000096

  DBA case samples using 250K Nsp

  Dataset EGAD00010000096

• EGAD00010000381

  MRCE sample using 300K

  Dataset EGAD00010000381