scholarly journals A Sparse and Low-Rank Regression Model for Identifying the Relationships Between DNA Methylation and Gene Expression Levels in Gastric Cancer and the Prediction of Prognosis

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 854
Author(s):  
Yishu Wang ◽  
Lingyun Xu ◽  
Dongmei Ai
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Dna Methylation ◽  
Regression Model ◽  
The Cancer Genome Atlas ◽  
Low Rank ◽  
Expression Levels ◽  
Rank Regression ◽  
Prediction Of Prognosis ◽  
Gene Expression Levels

DNA methylation is an important regulator of gene expression that can influence tumor heterogeneity and shows weak and varying expression levels among different genes. Gastric cancer (GC) is a highly heterogeneous cancer of the digestive system with a high mortality rate worldwide. The heterogeneous subtypes of GC lead to different prognoses. In this study, we explored the relationships between DNA methylation and gene expression levels by introducing a sparse low-rank regression model based on a GC dataset with 375 tumor samples and 32 normal samples from The Cancer Genome Atlas database. Differences in the DNA methylation levels and sites were found to be associated with differences in the expressed genes related to GC development. Overall, 29 methylation-driven genes were found to be related to the GC subtypes, and in the prognostic model, we explored five prognoses related to the methylation sites. Finally, based on a low-rank matrix, seven subgroups were identified with different methylation statuses. These specific classifications based on DNA methylation levels may help to account for heterogeneity and aid in personalized treatments.

scholarly journals Occupational exposure to gases/fumes and mineral dust affect DNA methylation levels of genes regulating expression

2019 ◽  
Vol 28 (15) ◽  
pp. 2477-2485 ◽  
Author(s):  
Diana A van der Plaat ◽  
Judith M Vonk ◽  
Natalie Terzikhan ◽  
Kim de Jong ◽  
Maaike de Vries ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Occupational Exposure ◽  
Health Effects ◽  
Mineral Dust ◽  
Occupational Exposures ◽  
Expression Levels ◽  
Adverse Health Effects ◽  
Never Smokers ◽  
Gene Expression Levels

Abstract Many workers are daily exposed to occupational agents like gases/fumes, mineral dust or biological dust, which could induce adverse health effects. Epigenetic mechanisms, such as DNA methylation, have been suggested to play a role. We therefore aimed to identify differentially methylated regions (DMRs) upon occupational exposures in never-smokers and investigated if these DMRs associated with gene expression levels. To determine the effects of occupational exposures independent of smoking, 903 never-smokers of the LifeLines cohort study were included. We performed three genome-wide methylation analyses (Illumina 450 K), one per occupational exposure being gases/fumes, mineral dust and biological dust, using robust linear regression adjusted for appropriate confounders. DMRs were identified using comb-p in Python. Results were validated in the Rotterdam Study (233 never-smokers) and methylation-expression associations were assessed using Biobank-based Integrative Omics Study data (n = 2802). Of the total 21 significant DMRs, 14 DMRs were associated with gases/fumes and 7 with mineral dust. Three of these DMRs were associated with both exposures (RPLP1 and LINC02169 (2×)) and 11 DMRs were located within transcript start sites of gene expression regulating genes. We replicated two DMRs with gases/fumes (VTRNA2-1 and GNAS) and one with mineral dust (CCDC144NL). In addition, nine gases/fumes DMRs and six mineral dust DMRs significantly associated with gene expression levels. Our data suggest that occupational exposures may induce differential methylation of gene expression regulating genes and thereby may induce adverse health effects. Given the millions of workers that are exposed daily to occupational exposures, further studies on this epigenetic mechanism and health outcomes are warranted.

scholarly journals Epigenetic Variation Analysis Leads to Biomarker Discovery in Gastric Adenocarcinoma

2020 ◽  
Vol 11 ◽  
Author(s):  
Yan Zhang ◽  
Dianjing Guo
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Dna Methylation ◽  
Gastric Adenocarcinoma ◽  
Histone Modifications ◽  
Biomarker Discovery ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Integrated Analysis ◽  
Epigenetic Variation

As one of the most common malignant tumors worldwide, gastric adenocarcinoma (GC) and its prognosis are still poorly understood. Various genetic and epigenetic factors have been indicated in GC carcinogenesis. However, a comprehensive and in-depth investigation of epigenetic alteration in gastric cancer is still missing. In this study, we systematically investigated some key epigenetic features in GC, including DNA methylation and five core histone modifications. Data from The Cancer Genome Atlas Program and other studies (Gene Expression Omnibus) were collected, analyzed, and validated with multivariate statistical analysis methods. The landscape of epi-modifications in gastric cancer was described. Chromatin state transition analysis showed a histone marker shift in gastric cancer genome by employing a Hidden-Markov-Model based approach, indicated that histone marks tend to label different sets of genes in GC compared to control. An additive effect of these epigenetic marks was observed by integrated analysis with gene expression data, suggesting epigenetic modifications may cooperatively regulate gene expression. However, the effect of DNA methylation was found more significant without the presence of the five histone modifications in our study. By constructing a PPI network, key genes to distinguish GC from normal samples were identified, and distinct patterns of oncogenic pathways in GC were revealed. Some of these genes can also serve as potential biomarkers to classify various GC molecular subtypes. Our results provide important insights into the epigenetic regulation in gastric cancer and other cancers in general. This study describes the aberrant epigenetic variation pattern in GC and provides potential direction for epigenetic biomarker discovery.

scholarly journals A Genome-Wide Study of DNA Methylation Patterns and Gene Expression Levels in Multiple Human and Chimpanzee Tissues

PLoS Genetics ◽  
2011 ◽  
Vol 7 (2) ◽  
pp. e1001316 ◽  
Author(s):  
Athma A. Pai ◽  
Jordana T. Bell ◽  
John C. Marioni ◽  
Jonathan K. Pritchard ◽  
Yoav Gilad
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Expression Levels ◽  
Genome Wide ◽  
A Genome ◽  
Methylation Patterns ◽  
Genome Wide Study ◽  
Gene Expression Levels

scholarly journals Gene expression levels of DNA methyltransferase enzymes in Shank3-deficient mouse model of autism during early development

2021 ◽  
Vol 55 (4) ◽  
pp. 234-237
Author(s):  
Annamaria Srancikova ◽  
Alexandra Reichova ◽  
Zuzana Bacova ◽  
Jan Bakos
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Brain Development ◽  
Early Development ◽  
Molecular Mechanisms ◽  
Altered Expression ◽  
Expression Levels ◽  
The Brain ◽  
Deficient Mice ◽  
Gene Expression Levels

Abstract Objectives. The balance between DNA methylation and demethylation is crucial for the brain development. Therefore, alterations in the expression of enzymes controlling DNA methylation patterns may contribute to the etiology of neurodevelopmental disorders, including autism. SH3 and multiple ankyrin repeat domains 3 (Shank3)-deficient mice are commonly used as a well-characterized transgenic model to investigate the molecular mechanisms of autistic symptoms. DNA methyltransferases (DNMTs), which modulate several cellular processes in neurodevelopment, are implicated in the pathophysiology of autism. In this study, we aimed to describe the gene expression changes of major Dnmts in the brain of Shank3-deficient mice during early development. Methods and Results. The Dnmts gene expression was analyzed by qPCR in 5-day-old homo-zygous Shank3-deficient mice. We found significantly lower Dnmt1 and Dnmt3b gene expression levels in the frontal cortex. However, no such changes were observed in the hippocampus. However, significant increase was observed in the expression of Dnmt3a and Dnmt3b genes in the hypothalamus of Shank3-deficient mice. Conclusions. The present data indicate that abnormalities in the Shank3 gene are accompanied by an altered expression of DNA methylation enzymes in the early brain development stages, therefore, specific epigenetic control mechanisms in autism-relevant models should be more extensively investigated.

Prognostic Significance of PD-1, PD-L1 and CD8 Gene Expression Levels in Gastric Cancer

Oncology ◽  
2020 ◽  
Vol 98 (7) ◽  
pp. 501-511
Author(s):  
Shuhei Ito ◽  
Takaaki Masuda ◽  
Miwa Noda ◽  
Qingjiang Hu ◽  
Dai Shimizu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Prognostic Significance ◽  
Expression Levels ◽  
Gene Expression Levels

scholarly journals DNA methylation signatures associated with prognosis of gastric cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jin Dai ◽  
Akihiro Nishi ◽  
Zhe-Xuan Li ◽  
Yang Zhang ◽  
Tong Zhou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Dna Methylation ◽  
Risk Score ◽  
Host Gene ◽  
The Cancer Genome Atlas ◽  
Receptor Interaction ◽  
Host Gene Expression ◽  
Clinical Practices ◽  
Free Interval

Abstract Background Few studies have examined prognostic outcomes-associated molecular signatures other than overall survival (OS) for gastric cancer (GC). We aimed to identify DNA methylation biomarkers associated with multiple prognostic outcomes of GC in an epigenome-wide association study. Methods Based on the Cancer Genome Atlas (TCGA), DNA methylation loci associated with OS (n = 381), disease-specific survival (DSS, n = 372), and progression-free interval (PFI, n = 383) were discovered in training set subjects (false discovery rates < 0.05) randomly selected for each prognostic outcome and were then validated in remaining subjects (P-values < 0.05). Key CpGs simultaneously validated for OS, DSS, and PFI were further assessed for disease-free interval (DFI, n = 247). Gene set enrichment analyses were conducted to explore the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways simultaneously enriched for multiple GC prognostic outcomes. Methylation correlated blocks (MCBs) were identified for co-methylation patterns associated with GC prognosis. Based on key CpGs, risk score models were established to predict four prognostic outcomes. Spearman correlation analyses were performed between key CpG sites and their host gene mRNA expression. Results We newly identified DNA methylation of seven CpGs significantly associated with OS, DSS, and PFI of GC, including cg10399824 (GRK5), cg05275153 (RGS12), cg24406668 (MMP9), cg14719951(DSC3), and cg25117092 (MED12L), and two in intergenic regions (cg11348188 and cg11671115). Except cg10399824 and cg24406668, five of them were also significantly associated with DFI of GC. Neuroactive ligand-receptor interaction pathway was suggested to play a key role in the effect of DNA methylation on GC prognosis. Consistent with individual CpG-level association, three MCBs involving cg11671115, cg14719951, and cg24406668 were significantly associated with multiple prognostic outcomes of GC. Integrating key CpG loci, two risk score models performed well in predicting GC prognosis. Gene body DNA methylation of cg14719951, cg10399824, and cg25117092 was associated with their host gene expression, whereas no significant associations between their host gene expression and four clinical prognostic outcomes of GC were observed. Conclusions We newly identified seven CpGs associated with OS, DSS, and PFI of GC, with five of them also associated with DFI, which might inform patient stratification in clinical practices.

scholarly journals Sodium valproate and 5-aza-2′-deoxycytidine differentially modulate DNA demethylation in G1 phase-arrested and proliferative HeLa cells

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Marina Amorim Rocha ◽  
Giovana Maria Breda Veronezi ◽  
Marina Barreto Felisbino ◽  
Maria Silvia Viccari Gatti ◽  
Wirla M. S. C. Tamashiro ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Sodium Valproate ◽  
Hela Cells ◽  
Dna Methyltransferase ◽  
Dna Demethylation ◽  
G1 Phase ◽  
Expression Levels ◽  
Demethylation Pathway ◽  
Gene Expression Levels

AbstractSodium valproate/valproic acid (VPA), a histone deacetylase inhibitor, and 5-aza-2-deoxycytidine (5-aza-CdR), a DNA methyltransferase 1 (DNMT1) inhibitor, induce DNA demethylation in several cell types. In HeLa cells, although VPA leads to decreased DNA 5-methylcytosine (5mC) levels, the demethylation pathway involved in this effect is not fully understood. We investigated this process using flow cytometry, ELISA, immunocytochemistry, Western blotting and RT-qPCR in G1 phase-arrested and proliferative HeLa cells compared to the presumably passive demethylation promoted by 5-aza-CdR. The results revealed that VPA acts predominantly on active DNA demethylation because it induced TET2 gene and protein overexpression, decreased 5mC abundance, and increased 5-hydroxy-methylcytosine (5hmC) abundance, in both G1-arrested and proliferative cells. However, because VPA caused decreased DNMT1 gene expression levels, it may also act on the passive demethylation pathway. 5-aza-CdR attenuated DNMT1 gene expression levels but increased TET2 and 5hmC abundance in replicating cells, although it did not affect the gene expression of TETs at any stage of the cell cycle. Therefore, 5-aza-CdR may also function in the active pathway. Because VPA reduces DNA methylation levels in non-replicating HeLa cells, it could be tested as a candidate for the therapeutic reversal of DNA methylation in cells in which cell division is arrested.

scholarly journals Association of high-evidence gastric cancer susceptibility loci and somatic gene expression levels with survival

2017 ◽  
Vol 38 (11) ◽  
pp. 1119-1128 ◽  
Author(s):  
Hyuna Sung ◽  
Nan Hu ◽  
Howard H Yang ◽  
Carol A Giffen ◽  
Bin Zhu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Cancer Susceptibility ◽  
Susceptibility Loci ◽  
Expression Levels ◽  
Somatic Gene ◽  
Gene Expression Levels

scholarly journals Development and validation of a novel ten-gene signature predicting prognosis in Hepatocellular carcinoma

2020 ◽  
Author(s):  
Guanbao Zhou ◽  
Genjie Lu ◽  
Liang Yang ◽  
Yangfang Lu
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Hierarchical Clustering ◽  
Risk Score ◽  
Cox Regression ◽  
Gene Signature ◽  
Molecular Biomarkers ◽  
The Cancer Genome Atlas ◽  
Expression Levels ◽  
Gene Expression Levels

Abstract Background: Hepatocellular carcinoma (HCC) is the most common type of liver cancer with relatively poor prognosis. Thus, we aimed to identify novel molecular biomarkers to effectively predict the prognosis of HCC patients and eventually guide treatment. Methods: Prognosis-associated genes were determined by Kaplan-Meier and multivariate Cox regression analyses using the expression and clinical data of 373 HCC patients from The Cancer Genome Atlas (TCGA) database and validated in an independent Gene Expression Omnibus (GEO) dataset. The classification of AML was performed by unsupervised hierarchical clustering of ten gene expression levels. A prognostic risk score was established based on a linear combination of ten gene expression levels using the regression coefficients derived from the multivariate Cox regression models. Results: A total of 183 genes were significantly associated with prognosis in HCC. SLC25A15, RAB8A, GOT2, SORBS2, IL18RAP were top five protective genes, while FHL3, AMD1, DCAF13, UBE2E1, PTDSS2 were top five risk genes in HCC. SLC25A15, GOT2, IL18RAP were significantly down-regulated and DCAF13, PTDSS2 and SORBS2 were significantly up-regulated in the HCC samples and these genes exhibited high accuracy in differentiating HCC tissues from normal liver tissues. Hierarchical clustering analysis of the ten genes discovered three clusters of HCC patients. HCC tumors of cluster1 and 2 were significantly associated with more favourable OS than those of cluster3, cluster2 tumors showed higher pathologic stage than cluster3 tumors. The risk score was predictive of increased mortality rate in HCC patients. Conclusions: The ten-gene signature and the risk score may turn out to be novel molecular biomarkers and stratification of HCC patients to considerably ameliorate the prognostic prediction.

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