A genome-wide search for determinants of survival in 1926 patients with advanced colorectal cancer with follow-up in over 22,000 patients

2021 ◽  
Vol 159 ◽  
pp. 247-258
Author(s):  
Christopher Wills ◽  
Yazhou He ◽  
Matthew G. Summers ◽  
Yi Lin ◽  
Amanda I. Phipps ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Advanced Colorectal Cancer ◽  
Genome Wide ◽  
A Genome ◽  
Genome Wide Search

scholarly journals Epigenome-Wide Study Identified Methylation Sites Associated with the Risk of Obesity

Nutrients ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1984
Author(s):  
Majid Nikpay ◽  
Sepehr Ravati ◽  
Robert Dent ◽  
Ruth McPherson
Keyword(s):  
Quantitative Trait Locus ◽  
Quantitative Trait ◽  
Rare Variants ◽  
Genome Wide ◽  
A Genome ◽  
Genome Wide Search ◽  
Risk Snps ◽  
Trait Locus ◽  
Genomic Regions ◽  
Eqtl Data

Here, we performed a genome-wide search for methylation sites that contribute to the risk of obesity. We integrated methylation quantitative trait locus (mQTL) data with BMI GWAS information through a SNP-based multiomics approach to identify genomic regions where mQTLs for a methylation site co-localize with obesity risk SNPs. We then tested whether the identified site contributed to BMI through Mendelian randomization. We identified multiple methylation sites causally contributing to the risk of obesity. We validated these findings through a replication stage. By integrating expression quantitative trait locus (eQTL) data, we noted that lower methylation at cg21178254 site upstream of CCNL1 contributes to obesity by increasing the expression of this gene. Higher methylation at cg02814054 increases the risk of obesity by lowering the expression of MAST3, whereas lower methylation at cg06028605 contributes to obesity by decreasing the expression of SLC5A11. Finally, we noted that rare variants within 2p23.3 impact obesity by making the cg01884057 site more susceptible to methylation, which consequently lowers the expression of POMC, ADCY3 and DNAJC27. In this study, we identify methylation sites associated with the risk of obesity and reveal the mechanism whereby a number of these sites exert their effects. This study provides a framework to perform an omics-wide association study for a phenotype and to understand the mechanism whereby a rare variant causes a disease.

scholarly journals DNA Methylation at ATP11A cg11702988 Is a Biomarker of Lung Disease Severity in Cystic Fibrosis: A Longitudinal Study

Genes ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 441
Author(s):  
Fanny Pineau ◽  
Davide Caimmi ◽  
Sylvie Taviaux ◽  
Maurane Reveil ◽  
Laura Brosseau ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Dna Methylation ◽  
Clinical Trials ◽  
Lung Disease ◽  
Disease Severity ◽  
Genome Wide ◽  
A Genome ◽  
Lung Disease Severity ◽  
Epigenetic Biomarker

Cystic fibrosis (CF) is a chronic genetic disease that mainly affects the respiratory and gastrointestinal systems. No curative treatments are available, but the follow-up in specialized centers has greatly improved the patient life expectancy. Robust biomarkers are required to monitor the disease, guide treatments, stratify patients, and provide outcome measures in clinical trials. In the present study, we outline a strategy to select putative DNA methylation biomarkers of lung disease severity in cystic fibrosis patients. In the discovery step, we selected seven potential biomarkers using a genome-wide DNA methylation dataset that we generated in nasal epithelial samples from the MethylCF cohort. In the replication step, we assessed the same biomarkers using sputum cell samples from the MethylBiomark cohort. Of interest, DNA methylation at the cg11702988 site (ATP11A gene) positively correlated with lung function and BMI, and negatively correlated with lung disease severity, P. aeruginosa chronic infection, and the number of exacerbations. These results were replicated in prospective sputum samples collected at four time points within an 18-month period and longitudinally. To conclude, (i) we identified a DNA methylation biomarker that correlates with CF severity, (ii) we provided a method to easily assess this biomarker, and (iii) we carried out the first longitudinal analysis of DNA methylation in CF patients. This new epigenetic biomarker could be used to stratify CF patients in clinical trials.

scholarly journals Co-Overexpression of TWIST1-CSF1 Is a Common Event in Metastatic Oral Cancer and Drives Biologically Aggressive Phenotype

Cancers ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 153
Author(s):  
Sabrina Daniela da Silva ◽  
Fabio Albuquerque Marchi ◽  
Jie Su ◽  
Long Yang ◽  
Ludmila Valverde ◽  
...  
Keyword(s):  
Epithelial Mesenchymal Transition ◽  
Inflammatory Cells ◽  
Rank Test ◽  
Mesenchymal Transition ◽  
Genome Wide ◽  
A Genome ◽  
Enhanced Expression ◽  
Advanced Stages

Invasive oral squamous cell carcinoma (OSCC) is often ulcerated and heavily infiltrated by pro-inflammatory cells. We conducted a genome-wide profiling of tissues from OSCC patients (early versus advanced stages) with 10 years follow-up. Co-amplification and co-overexpression of TWIST1, a transcriptional activator of epithelial-mesenchymal-transition (EMT), and colony-stimulating factor-1 (CSF1), a major chemotactic agent for tumor-associated macrophages (TAMs), were observed in metastatic OSCC cases. The overexpression of these markers strongly predicted poor patient survival (log-rank test, p = 0.0035 and p = 0.0219). Protein analysis confirmed the enhanced expression of TWIST1 and CSF1 in metastatic tissues. In preclinical models using OSCC cell lines, macrophages, and an in vivo matrigel plug assay, we demonstrated that TWIST1 gene overexpression induces the activation of CSF1 while TWIST1 gene silencing down-regulates CSF1 preventing OSCC invasion. Furthermore, excessive macrophage activation and polarization was observed in co-culture system involving OSCC cells overexpressing TWIST1. In summary, this study provides insight into the cooperation between TWIST1 transcription factor and CSF1 to promote OSCC invasiveness and opens up the potential therapeutic utility of currently developed antibodies and small molecules targeting cancer-associated macrophages.

Clinical Relevance of Plasma DNA Methylation in Colorectal Cancer Patients Identified by Using a Genome-Wide High-Resolution Array

2014 ◽  
Vol 22 (S3) ◽  
pp. 1419-1427 ◽  
Author(s):  
Pei-Ching Lin ◽  
Jen-Kou Lin ◽  
Chien-Hsing Lin ◽  
Hung-Hsin Lin ◽  
Shung-Haur Yang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Dna Methylation ◽  
High Resolution ◽  
Cancer Patients ◽  
Clinical Relevance ◽  
Plasma Dna ◽  
Genome Wide ◽  
A Genome ◽  
Colorectal Cancer Patients

scholarly journals A Genome-Wide Search For Susceptibility Loci to Human Essential Hypertension

Hypertension ◽  
2000 ◽  
Vol 35 (6) ◽  
pp. 1291-1296 ◽  
Author(s):  
Pankaj Sharma ◽  
Jennie Fatibene ◽  
Franco Ferraro ◽  
Haiyan Jia ◽  
Sue Monteith ◽  
...  
Keyword(s):  
Essential Hypertension ◽  
Susceptibility Loci ◽  
Genome Wide ◽  
A Genome ◽  
Genome Wide Search

scholarly journals A Genome-Wide Study of Cytogenetic Changes in Colorectal Cancer Using SNP Microarrays: Opportunities for Future Personalized Treatment

PLoS ONE ◽  
2012 ◽  
Vol 7 (2) ◽  
pp. e31968 ◽  
Author(s):  
Farzana Jasmine ◽  
Ronald Rahaman ◽  
Charlotte Dodsworth ◽  
Shantanu Roy ◽  
Rupash Paul ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Personalized Treatment ◽  
Genome Wide ◽  
A Genome ◽  
Snp Microarrays ◽  
Cytogenetic Changes ◽  
Genome Wide Study

scholarly journals Genome-wide imaging screen uncovers molecular determinants of arsenite-induced protein aggregation and toxicity

2021 ◽  
Author(s):  
Stefanie Andersson ◽  
Antonia Romero ◽  
Joana Isabel Rodrigues ◽  
Sansan Hua ◽  
Xinxin Hao ◽  
...  
Keyword(s):  
Protein Aggregation ◽  
Translational Control ◽  
Protein Biosynthesis ◽  
Cellular Systems ◽  
Genome Wide ◽  
A Genome ◽  
Toxic Metalloid ◽  
Arsenic Stress

The toxic metalloid arsenic causes widespread misfolding and aggregation of cellular proteins. How these protein aggregates are formed in vivo, the mechanisms by which they affect cells, and how cells prevent their accumulation is not fully understood. To find components involved in these processes, we performed a genome-wide imaging screen and identified yeast deletion mutants with either enhanced or reduced protein aggregation levels during arsenite exposure. We show that many of the identified factors are crucial to safeguard protein homeostasis (proteostasis) and to protect cells against arsenite toxicity. The hits were enriched for various functions including protein biosynthesis and transcription, and dedicated follow-up experiments highlight the importance of accurate transcriptional and translational control for mitigating protein aggregation and toxicity during arsenite stress. Some of the hits are associated with pathological conditions, suggesting that arsenite-induced protein aggregation may affect disease processes. The broad network of cellular systems that impinge on proteostasis during arsenic stress identified in this current study provides a valuable resource and a framework for further elucidation of the mechanistic details of metalloid toxicity and pathogenesis.

Genome-Wide Prediction of Coaxial Helical Stacking Using Random Forests and Covariance Models

2014 ◽  
Vol 23 (03) ◽  
pp. 1460008
Author(s):  
Kevin Byron ◽  
Jason T. L. Wang ◽  
Dongrong Wen
Keyword(s):  
Random Forests ◽  
Life Science ◽  
Science Research ◽  
Genomic Region ◽  
Computational Approach ◽  
Search Method ◽  
Genome Wide ◽  
A Genome ◽  
Genome Wide Search ◽  
Covariance Models

Developing effective artificial intelligence tools to find motifs in DNA, RNA and proteins poses a challenging yet important problem in life science research. In this paper, we present a computational approach for finding RNA tertiary motifs in genomic sequences. Specifically, we predict genomic coordinate locations for coaxial helical stackings in 3-way RNA junctions. These predictions are provided by our tertiary motif search package, named CSminer, which utilizes two versatile methodologies: random forests and covariance models. A coaxial helical stacking tertiary motif occurs in a 3-way RNA junction where two separate helical elements form a pseudocontiguous helix and provide thermodynamic stability to the RNA molecule as a whole. Our CSminer tool first uses a genome-wide search method based on covariance models to find a genomic region that may potentially contain a coaxial helical stacking tertiary motif. CSminer then uses a random forests classifier to predict whether the genomic region indeed contains the tertiary motif. Experimental results demonstrate the effectiveness of our approach.

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