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Hatfield Josefsen posted an update 6 months, 4 weeks ago
Two major transcription factors – SPI1 and RUNX1 were identified as epigenetically dysregulated, which further modulates 129 downstream genes. Comparison of our observations with the head and neck cancer data from TCGA revealed distinct DNA methylation and gene expression landscapes which might be specific for oropharyngeal cancer. HPV DNA sequences were not detected in any of the tumor samples in RNA-Seq data. The results obtained in this study might provide improved understanding of the disease.Recently, cancer has been characterized as a heterogeneous disease composed of many different subtypes. Early diagnosis of cancer subtypes is an important study of cancer research, which can be of tremendous help to patients after treatment. In this paper, we first extract a novel dataset, which contains gene expression, miRNA expression, and isoform expression of five cancers from The Cancer Genome Atlas (TCGA). Next, to avoid the effect of noise existing in 60, 483 genes, we select a small number of genes by using LASSO that employs gene expression and survival time of patients. Then, we construct one similarity kernel for each expression data by using Chebyshev distance. And also, We used SKF to fused the three similarity matrix composed of gene, Iso, and miRNA, and finally clustered the fused similarity matrix with spectral clustering. In the experimental results, our method has better P-value in the Cox model than other methods on 10 cancer data from Jiang Dataset and Novel Dataset. We have drawn different survival curves for different cancers and found that some genes play a key role in cancer. For breast cancer, we find out that HSPA2A, RNASE1, CLIC6, and IFITM1 are highly expressed in some specific groups. For lung cancer, we ensure that C4BPA, SESN3, and IRS1 are highly expressed in some specific groups. The code and all supporting data files are available from https//github.com/guofei-tju/Uncovering-Cancer-Subtypes-via-LASSO.
Metabolic risk varies according to body mass index (BMI), body fat distribution and ethnicity. In recent years, epigenetics, which reflect gene-environment interactions have attracted considerable interest as mechanisms that may mediate differences in metabolic risk. The aim of this study was to investigate DNA methylation differences in abdominal and gluteal subcutaneous adipose tissues of normal-weight and obese black and white South African women.
Body composition was assessed using dual-energy x-ray absorptiometry and computerized tomography, and insulin sensitivity was measured using a frequently sampled intravenous glucose tolerance test in 54 normal-weight (BMI 18-25 kg/m
) and obese (BMI ≥ 30 kg/m
) women. Global and insulin receptor (
) DNA methylation was quantified in abdominal (ASAT) and gluteal (GSAT) subcutaneous adipose depots, using the Imprint methylation enzyme-linked immunosorbent assay and pyrosequencing.
gene expression was measured using quantitative real-time PCR.
Global DNAst that GSAT rather than ASAT may be a determinant of metabolic risk in black women and provide novel evidence that altered DNA methylation within adipose depots may contribute to ethnic differences in body fat distribution and cardiometabolic risk.
We show small, but significant global and INSR promoter DNA methylation differences in GSAT and ASAT of normal-weight and obese black and white South African women. DNA methylation in ASAT was associated with centralization of body fat in white women, whereas in black women DNA methylation in GSAT was associated with insulin resistance and systemic inflammation. Our findings suggest that GSAT rather than ASAT may be a determinant of metabolic risk in black women and provide novel evidence that altered DNA methylation within adipose depots may contribute to ethnic differences in body fat distribution and cardiometabolic risk.Gene-environment interaction is a key part of evolutionary biology, animal, and plant breeding, and a number of health sciences, like epidemiology and precision medicine. However, bottlenecks in models of gene-environment interaction have recently been made manifest, particularly in the field of medicine and, consequently, specific improvements have been explicitly requested-namely, an implementation of gene-environment interaction satisfactorily disentangled from gene-environment correlation. The present paper meets those demands by providing mathematical developments that implement classical models of genetic effects and bring them up to date with the prospects current available data bestow. These developments are shown to overcome the limitations of previous proposals through the analysis of illustrative examples on disease susceptibility, with special attention paid to precision medicine. Indeed, a number of misconceptions about the application of models of genetic/environmental effects to precision medicine are here identified and clarified. Butyzamide cell line The theory here provided is argued to strengthen, in particular, the methodology required for high-precision characterization of strain virulence in the study of the COVID-19 pandemic.
Bioinformatics provides a valuable tool to explore the molecular mechanisms underlying pathogenesis of hepatocellular carcinoma (HCC). To improve prognosis of patients, identification of robust biomarkers associated with the pathogenic pathways of HCC remains an urgent research priority.
We employed the Robust Rank Aggregation method to integrate nine qualified HCC datasets from the Gene Expression Omnibus. A robust set of differentially expressed genes (DEGs) between tumor and normal tissue samples were screened. Weighted gene co-expression network analysis was applied to cluster DEGs and the key modules related to clinical traits identified. Based on network topology analysis, novel risk genes derived from key modules were mined and biological verification performed. The potential functions of these risk genes were further explored with the aid of miRNA-mRNA regulatory networks. Finally, the prognostic ability of these genes was assessed by constructing a clinical prediction model.
Two key modules sho to uncover the complex biological mechanisms of HCC. More importantly, this novel integrated strategy facilitates identification of risk hub genes as candidate biomarkers for HCC, which could effectively guide clinical treatments.
Analysis of multiple datasets combined with global network information presents a successful approach to uncover the complex biological mechanisms of HCC. More importantly, this novel integrated strategy facilitates identification of risk hub genes as candidate biomarkers for HCC, which could effectively guide clinical treatments.
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