Tìm thấy 20+ kết quả cho từ khóa "The Cancer Genome Atlas"
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DNA methylation datasets in LUSC were down- loaded from the Cancer Genome Atlas (TCGA) data portal (http://tcga-data.nci.nih.gov). We selected 343 tumor and 39 paired NTL samples, with both DNA methylation data and clinical features information available for performing the correlation analysis. Genome-wide DNA methylation patterns in LUSC. In brief, the DNA methylation levels gradually increased with the CpG sites far away from CGIs.
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of transcription factors distinguish cancer type. We hypothesize that the mRNA co-expression patterns of transcription factors can be used both to learn how they cooperate in networks and to distinguish between cancer types.. We applied Thresher to the RNA-Seq expression data of 486 transcription factors from more than 10,000 samples of 33 kinds of cancer studied in The Cancer Genome Atlas (TCGA).
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Hence, we established and validated an lncRNAs model based on a machine learning technology – support vector machine (SVM) [7] for MSI prediction using the data of The Cancer Genome Atlas (TCGA). Search and collection of gastric cancer (GC) lncRNAs expression series. Features which can reflect 95% information of the whole cohort were selected [9].
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Multiple studies have used RNA-Seq data from The Cancer Genome Atlas (TCGA) to analyze RNA editing events in adult solid tumors and their effects on. However, RNA editing has not been investigated in pediatric malignancies.. However, reanalysis of the same data showed that RNA editing was less fre- quent [10, 16], and most of the previously reported [14]. There is also disagreement regarding the prevalence of non-canonical (non-A-to-I, non-C-to- U) RNA editing .
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International Cancer Genome Consortium (ICGC, https://icgc.org/),the Cancer Genome Atlas (TCGA, http://cancergenome.nih.gov/) and the Clinical Prote- omic Tumor Analysis Consortium (CPTAC, https://. 1C,D), but not significant in the CPTAC- PDAC cohort (p = 0.170, Fig. Table 1 Characteristics of the public datasets used in the study. a Cases included in the analysis. Table 2 Demographic and clinicopathological characteristics of the tumour samples in the APGI cohort.
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After mRNA and protein expression analyses, the rela- tionship between the mRNA expression of MCMs and the clinicopathological parameters of patients, such as individual cancer stages, was assessed by performing The Cancer Genome Atlas (TCGA) database analysis via UALCAN. Furthermore, the Kaplan-Meier plotter database [15, 16] (http://kmplot.com/analysis/) was used to analyze the prognostic value of the mRNA expression of dif- ferent MCMs in cancer patients.
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In this work, we use an integrative approach to identify subtype-specific lncRNA:miRNA:mRNA interactions through which lncR- NAs compete for binding to shared miRNAs in breast cancer.. To find breast cancer subtype-specific interactions, we systemically analyze lncRNA, miRNA, and mRNA expression profiles of breast cancer patients made available through the Cancer Genome Atlas Project (TCGA) [24].
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TNBCtype (https://cbc.app.vumc.org/tnbc/) tool [43] and the TNBC subtype-specific genes [44] were respectively used to identify TNBC subtypes. 1 The workflow for identification of potential diagnostic and prognostic biomarkers in triple-negative breast cancer. Liu et al. et al. TNBC: Triple-negative breast cancer. org/10.1186/s . OS analysis of TNBC and Breast cancer.. R software (version 3.6.1, https://www.r-project.org), The Cancer Genome Atlas (TCGA) http://portal.gdc.cancer.gov.
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In this study, we investigate the main and pair-wise epistatic effects of miRNAs on the pathological stages of colon cancer using datasets from The Cancer Genome Atlas.. Using the multi-step workflow, we identify a set of miRNAs with main and epistatic effect on the pathological stages of colon cancer.
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Cancer is a major threat to human health that leads to millions of deaths each year worldwide (Siegel et al., 2019). Recent improvements in sequencing technologies made the fast and inexpensive mapping of cancer genomes possible (Tucker et al., 2009). This was a major step in cancer research and led to large-scale cancer genome profiling studies such as The Cancer Genome Atlas (TCGA) (Weinstein et al., 2013) and International Cancer Genome Consortium (ICGC) (Zhang et al., 2011).
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Accord- ing to data from gene expression profiling interactive analysis (GEPIA) website based on the Cancer Genome Atlas (TCGA) data, NR1D1 is lowly expressed in ovarian cancer tissues. However, its role in ovarian cancer re- mains unclear.. Abnormal activation of the JAK/STAT3 signaling pathway, which contributes to the pathogenesis and progression of cancers [14–16], was also noted in ovarian cancer [17, 18].
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Methods: In this study, the number of stromal and immune cells in malignant tumor tissues were first estimated by using expression data (ESTIMATE) and cell-type identification with relative subsets of known RNA transcripts (CIBERSORT) to calculate the proportion of infiltrating immune cell and stromal components of colon cancer samples from the Cancer Genome Atlas database. interaction network (PPI), the candidate hub gene serine protease inhibitor family E member 1 (SERPINE1) was found to be associated
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A panel of miRNAs as prognostic markers for African-American patients with triple negative breast cancer. This analysis was performed in relation to survival in the aggregated breast cancer clinical studies extracted from The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases (extraction based on TNBC cases in general, and TNBC cases with known LN status)..
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Methods: First, we extracted the data for SOX14 methylation and expression within cervical cancer from The Cancer Genome Atlas (TCGA) database and analysed them via UALCAN, Wanderer, MEXPRESS and LinkedOmics.. significantly differentiated methylation CpG site and synthesized for methylation-specific PCR (MSP) and quantitative methylation-specific PCR (QMSP) to verify SOX14 methylation in both cervical tissuses and liquid-based cell samples..
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The somatic gene mutations and clinical data of 412 samples from BC patients in the United States and 101 samples from BC patients in China were downloaded from The Cancer Genome Atlas (TCGA) and Inter- national Cancer Genome Consortium (ICGC) online platforms, respectively. cistrome.org/).. The Kruskal-Wallis test was used to analyse the cor- relation between KDM6A mutation status and overall mutation counts..
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In the current study, we aimed to investigate the clinical impact and roles of TRIM27 in the development of RCC.. Methods: The mRNA levels of TRIM27 and Kaplan – Meier survival of RCC were analyzed from The Cancer Genome Atlas database. Real-time PCR and Western blotting were used to measure the mRNA and protein levels of TRIM27 both in vivo and in vitro. Results: We discovered that TRIM27 was elevated in RCC patients, and the expression of TRIM27 was closely correlated with poor prognosis.
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We gathered clinical data for 816 breast cancer patients from the cancer genome atlas project (TCGA). These results suggest that the presence of alteration in top breast cancer cell type pre- dictors can be an indicator for likelihood of survival in breast invasive carcinoma patients.. Next, we checked if the expression signature of the predictors could be used for survival prognosis of TCGA breast cancer patients.
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This could be due to the ethnic differ- ences in the studied populations, barriers to access, dif- ferent tissue histology, and other latent variables. The Integrated genomic analyses of ovarian carcinoma study conducted through the Cancer Genome Atlas (TCGA) Research Network reported a prevalence of 20% BRCA PSVs in high-grade serous ovarian cancer samples.. Other studies reported an even higher overall preva- lence of BRCA PSVs in ovarian cancer patients.
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In recent years, a growing number of studies have generated genome-wide DNA methylation profiles of thousands of cancer samples using Infinium HumanMethylation450 platform, including The Cancer Genome Atlas (TCGA) project, which represents one of the largest efforts to systematically characterize the molecular profiles of cancers [18]. Here we present MethCNA, a comprehensive database for the integrated analysis of DNA methylation and copy number alterations in human cancer.
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Integrating HECW1 expression into the. This study mainly examined the prognostic value of HECT, C2 and WW domain-containing E3 ubiquitin protein ligase 1 (HECW1) expression in ccRCC patients in combination with established clinical indicators.. Methods: The expression level of HECW1 was screened out by data-independent acquisition mass spectrometry (DIA-MS) and analyzed in ccRCC patients from the The Cancer Genome Atlas (TCGA) database and our cohort.