scholarly journals Altered DNA methylation is associated with aberrant stemness gene expression in early‑stage HNSCC

2019 ◽  
Author(s):  
Takatsugu Suzuki ◽  
Hiroshi Yamazaki ◽  
Kazufumi Honda ◽  
Eijitsu Ryo ◽  
Akihiro Kaneko ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Early Stage

scholarly journals Uncovering Driver DNA Methylation Events in Nonsmoking Early Stage Lung Adenocarcinoma

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Xindong Zhang ◽  
Lin Gao ◽  
Zhi-Ping Liu ◽  
Songwei Jia ◽  
Luonan Chen
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Lung Adenocarcinoma ◽  
Early Stage ◽  
Methylation Data ◽  
Driver Genes ◽  
Differential Network Analysis ◽  
Differential Network ◽  
Aberrant Dna Methylation ◽  
Never Smokers

As smoking rates decrease, proportionally more cases with lung adenocarcinoma occur in never-smokers, while aberrant DNA methylation has been suggested to contribute to the tumorigenesis of lung adenocarcinoma. It is extremely difficult to distinguish which genes play key roles in tumorigenic processes via DNA methylation-mediated gene silencing from a large number of differentially methylated genes. By integrating gene expression and DNA methylation data, a pipeline combined with the differential network analysis is designed to uncover driver methylation genes and responsive modules, which demonstrate distinctive expressions and network topology in tumors with aberrant DNA methylation. Totally, 135 genes are recognized as candidate driver genes in early stage lung adenocarcinoma and top ranked 30 genes are recognized as driver methylation genes. Functional annotation and the differential network analysis indicate the roles of identified driver genes in tumorigenesis, while literature study reveals significant correlations of the top 30 genes with early stage lung adenocarcinoma in never-smokers. The analysis pipeline can also be employed in identification of driver epigenetic events for other cancers characterized by matched gene expression data and DNA methylation data.

Impact of DNA methylation and gene expression profile on treatment initiation and treatment free survival in early stage CLL.

2015 ◽  
Vol 33 (15_suppl) ◽  
pp. e22073-e22073
Author(s):  
Ritu Gupta ◽  
Lata Rani ◽  
Nitin Mathur ◽  
Ajay Gogia ◽  
Durai Sundar ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Treatment Initiation ◽  
Early Stage ◽  
Free Survival

161 Epigenetic Changes in Poultry Due to Reprogramming of the Gut Microbiota at an Early Stage of Embryonic Development

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 85-86
Author(s):  
Aleksandra Dunislawska
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Intestinal Microbiota ◽  
Early Stage ◽  
Cpg Islands ◽  
Bioinformatic Analysis ◽  
Gene Promoters ◽  
In Ovo ◽  
Bioactive Substances ◽  
Rna Molecules

Abstract Epigenetic regulation of the gene expression is an interaction of the external environment with the genetic information. They are potentially heritable changes in the gene expression which does not involve alteration in DNA sequence and can be triggered by microRNA activity and DNA methylation. MicroRNA is fraction of small RNA molecules that have a fundamental impact on gene expression. DNA methylation inhibits DNA transcription by addition of the methyl residues to the cysteine within the CpG islands of the gene promoters. These processes can be modulated by environmental factors, such as intestinal microbiota modification. In poultry, the microbiota can be reprogrammed using in ovo technology at an early stage of embryo development. The intestinal microbiota is therefore stimulated and rearranged by injecting bioactive substances into air chamber of eggs on the day 12 of incubation. We have proved that the administration of lactic acid bacteria strains and galactooligosaccharide in ovo is effective in modulating of the intestinal microbiota. The administration of bioactive compounds has been demonstrated to influence gene expression in immune, intestinal and metabolic tissues. However, it has been noticed that a significant part of genes is silenced. In our experiment after in ovo administration of the substances in different genotypes (chicken broiler and native Polish breed) the range of tissues was collected: liver, caecal tonsils, spleen. By performing the bioinformatic analysis of the expression microarray, silenced genes and active miRNAs were selected. Methylation was analysed using the global and MSP-qPCR method, and analysis of miRNA activity using miRCURY LNA PCR Systems. We confirmed that negative regulation of the gene expression have epigenetic character and its mechanism depends on the genotype and the substance administered in ovo. Epigenetic nature of research is new direction of host-microbiome interaction. Research was financed by grant UMO-2017/25/N/NZ9/01822 funded by National Science Centre (Poland).

DNA Methylation Inhibites ANXA1 Gene Expression in Nasopharyngeal Carcinoma Cell Lines*

2009 ◽  
Vol 36 (10) ◽  
pp. 1319-1326 ◽  
Author(s):  
Shuang-Xiang TAN ◽  
Rui-Cheng HU ◽  
Ai-Guo DAI ◽  
Cen-E TANG ◽  
Hong YI ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Nasopharyngeal Carcinoma ◽  
Cell Lines ◽  
Carcinoma Cell ◽  
Nasopharyngeal Carcinoma Cell ◽  
Carcinoma Cell Lines

An Integrated Feature Selection Algorithm for Cancer Classification using Gene Expression Data

2019 ◽  
Vol 21 (9) ◽  
pp. 631-645 ◽  
Author(s):  
Saeed Ahmed ◽  
Muhammad Kabir ◽  
Zakir Ali ◽  
Muhammad Arif ◽  
Farman Ali ◽  
...  
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Classification Accuracy ◽  
Early Stage ◽  
Small Sample Size ◽  
Feature Selection Method ◽  
Small Sample ◽  
Expression Data ◽  
Base Function

Aim and Objective: Cancer is a dangerous disease worldwide, caused by somatic mutations in the genome. Diagnosis of this deadly disease at an early stage is exceptionally new clinical application of microarray data. In DNA microarray technology, gene expression data have a high dimension with small sample size. Therefore, the development of efficient and robust feature selection methods is indispensable that identify a small set of genes to achieve better classification performance. Materials and Methods: In this study, we developed a hybrid feature selection method that integrates correlation-based feature selection (CFS) and Multi-Objective Evolutionary Algorithm (MOEA) approaches which select the highly informative genes. The hybrid model with Redial base function neural network (RBFNN) classifier has been evaluated on 11 benchmark gene expression datasets by employing a 10-fold cross-validation test. Results: The experimental results are compared with seven conventional-based feature selection and other methods in the literature, which shows that our approach owned the obvious merits in the aspect of classification accuracy ratio and some genes selected by extensive comparing with other methods. Conclusion: Our proposed CFS-MOEA algorithm attained up to 100% classification accuracy for six out of eleven datasets with a minimal sized predictive gene subset.

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