scholarly journals Development and Validation of a Prognostic Nomogram for Gastric Cancer Based on DNA Methylation-Driven Differentially Expressed Genes

2020 ◽  
Vol 16 (7) ◽  
pp. 1153-1165 ◽  
Author(s):  
Yi Bai ◽  
Chunlian Wei ◽  
Yuxin Zhong ◽  
Yamin Zhang ◽  
Junyu Long ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Dna Methylation ◽  
Differentially Expressed Genes ◽  
Differentially Expressed ◽  
Development And Validation

Development and Validation of a Prognostic Nomogram for Gastric Cancer Based on DNA Methylation-Driven Genes

2019 ◽  
Author(s):  
Yi Bai ◽  
Chunlian Wei ◽  
Yuxin Zhong ◽  
Junyu Long ◽  
Shan Huang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Dna Methylation ◽  
Development And Validation

scholarly journals Gene expression and DNA methylation analyses suggest that two immune related genes are prognostic factors of colorectal cancer

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Xiao-Liang Xing ◽  
Zhi-Yong Yao ◽  
Chaoqun Xing ◽  
Zhi Huang ◽  
Jing Peng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Risk Assessment ◽  
Dna Methylation ◽  
Differentially Expressed Genes ◽  
Risk Score ◽  
Immune Cells ◽  
Assessment Model ◽  
Differentially Expressed ◽  
Risk Assessment Model

Abstract Background Colorectal cancer (CRC) is the second most prevalent cancer, as it accounts for approximately 10% of all annually diagnosed cancers. Studies have indicated that DNA methylation is involved in cancer genesis. The purpose of this study was to investigate the relationships among DNA methylation, gene expression and the tumor-immune microenvironment of CRC, and finally, to identify potential key genes related to immune cell infiltration in CRC. Methods In the present study, we used the ChAMP and DESeq2 packages, correlation analyses, and Cox regression analyses to identify immune-related differentially expressed genes (IR-DEGs) that were correlated with aberrant methylation and to construct a risk assessment model. Results Finally, we found that HSPA1A expression and CCRL2 expression were positively and negatively associated with the risk score of CRC, respectively. Patients in the high-risk group were more positively correlated with some types of tumor-infiltrating immune cells, whereas they were negatively correlated with other tumor-infiltrating immune cells. After the patients were regrouped according to the median risk score, we could more effectively distinguish them based on survival outcome, clinicopathological characteristics, specific tumor-immune infiltration status and highly expressed immune-related biomarkers. Conclusion This study suggested that the risk assessment model constructed by pairing immune-related differentially expressed genes correlated with aberrant DNA methylation could predict the outcome of CRC patients and might help to identify those patients who could benefit from antitumor immunotherapy.

scholarly journals Construction of Prognostic Prediction Model for Stomach Adenocarcinoma Based on the TCGA Database.

2020 ◽  
Author(s):  
Zhengzhong Gu ◽  
Xiaohan Cui ◽  
Xudong Wang
Keyword(s):  
Gastric Cancer ◽  
Extracellular Matrix ◽  
Protein Kinase ◽  
Network Analysis ◽  
Differentially Expressed Genes ◽  
Differentially Expressed ◽  
Stomach Adenocarcinoma ◽  
Matrix Organization ◽  
Prognostic Prediction ◽  
New Biomarkers

Abstract Background: Prognostic prediction models have been developed to detect new biomarkers of gastric cancer (GC). The identification of new biomarkers could provide theoretical foundations for the application of molecular targeted therapy in advanced GC. The aim of this study was to construct a prognostic prediction model for stomach adenocarcinoma (STAD) based on The Cancer Genome Atlas (TCGA) database. Methods: First, we used the "limma" package to screen differentially expressed genes (DEGs) based on TCGA database. Gene ontology (GO) analysis was performed using the "ClusterProfiler" package. The interactions between proteins and the relationships between differentially expressed genes and clinical features were analyzed by protein-protein interaction (PPI) network analysis and weighted gene coexpression network analysis (WGCNA), respectively. Then, gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to identify differentially enriched pathways. The GenVisR package and CIBERSORT were used to identify mutations and assess immune infiltration. Finally, the expression of COL3A1 in STAD tissues was verified by reverse transcription quantitative polymerase chain reaction (RT-qPCR) and western blotting.Results: Six differentially expressed genes were screened out, namely, COL3A1, ADAMTS12, BGN, FNDC1, AEBP1 and HTRA3. The enrichment results showed that differentially expressed genes were involved in multiple pathways in STAD, such as those related to the extracellular matrix, extracellular structure organization, and extracellular matrix organization. The differentially expressed genes were related to immune infiltration via the mitogen-activated protein kinase (MAPK) and phosphatidylinositol 3-kinase/protein kinase B (PI3K/AKT) pathways. The western blotting and RT-qPCR results suggested that COL3A1 was overexpressed in STAD tissues compared with normal tissues.Conclusion: COL3A1, ADAMTS12, BGN, FNDC1, AEBP1 and HTRA3 could play important roles in the tumorigenesis and progression of STAD via various pathways, including those involving the extracellular matrix, extracellular structure organization, and extracellular matrix organization. COL3A1, ADAMTS12, BGN, FNDC1, AEBP1, and HTRA3 act as oncogenes in most cancers and may be biomarkers. Additionally, the identification of COL3A1 as a candidate biomarker provides a direction for further research on the role of tumor immunity in gastric cancer.

scholarly journals New differentially expressed genes and differential DNA methylation underlying refractory epilepsy

Oncotarget ◽  
2016 ◽  
Vol 7 (52) ◽  
pp. 87402-87416 ◽  
Author(s):  
Xi Liu ◽  
Shu Ou ◽  
Tao Xu ◽  
Shiyong Liu ◽  
Jinxian Yuan ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Differentially Expressed Genes ◽  
Refractory Epilepsy ◽  
Differentially Expressed

Data mining of microarray for differentially expressed genes in liver metastasis from gastric cancer

2010 ◽  
Vol 4 (2) ◽  
pp. 247-253
Author(s):  
Ling Xu ◽  
Feng Wang ◽  
Xuan-Fu Xu ◽  
Wen-Hui Mo ◽  
Rong Wan ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Data Mining ◽  
Liver Metastasis ◽  
Differentially Expressed Genes ◽  
Differentially Expressed

DNA-methylation analysis of differentially expressed genes in fibroblast cell-lines, derived from monozygotic twins discordant for Parkinson’s disease

2020 ◽  
pp. 73-74
Author(s):  
И.Н. Рыболовлев ◽  
И.Н. Власов ◽  
А.Х. Алиева ◽  
П.А. Сломинский ◽  
М.И. Шадрина
Keyword(s):  
Parkinson’S Disease ◽  
Dna Methylation ◽  
Parkinson's Disease ◽  
Cell Lines ◽  
Differentially Expressed Genes ◽  
Regulation Of Gene Expression ◽  
Monozygotic Twins ◽  
Fibroblast Cell ◽  
Differentially Expressed ◽  
Pathogenesis Related

Болезнь Паркинсона (БП) является многофакторным гетерогенным нейродегенеративным заболеванием. Поскольку этиопатогенез БП недостаточно изучен, кроме поиска и анализа изменений на уровне ДНК, необходимо распространить фокус исследований на другие уровни: транскриптом и метилом. Изменения на уровне эпигенома можно исследовать у лиц с идентичной генетической конституцией, такой «моделью» являются дискордантные по этому заболеванию монозиготные близнецы. В исследовании приняло участие 3 пары фенотипически и генотипически монозиготных близнецов русского происхождения; В исследовании приняло участие 3 пары фенотипически и генотипически монозиготных близнецов русского происхождения. БП была уточнена у одного из каждой пары близнецов; длительность течения болезни у близнеца с БП составило по меньшей мере 7 лет.; длительность течения болезни у близнеца с БП составила по меньшей мере 7 лет. Были проанализированы метиломы крови и отобраны точки варьирующегося метилирования. Нами было найдено 8 дифференциально экспрессирующихся генов, которые могут быть дифференциально метилированы. Были выявлены различия между здоровым близнецом и близнецом с БП по уровню метилирования ДНК для ряда этих генов в клеточных линиях фибробластов. Полученные нами данные могут указывать на участие процесса ДНК-метилирования в регуляции транскрипции кандидатных генов-участников патогенеза БП. In recent years it has been convincingly demonstrated that genetic factors play an important role in progression of Parkinson’s disease (PD). Since the etiology of PD has not been elucidated completely yet, it is crucial to shift focus of the research to the broader areas - to dive into investigations of methylome and transcriptome. Epigenetic regulation of gene expression may take part in pathogenesis of PD. Changes in epigenome can be conveniently investigated in case of individuals with almost identical genetic makeup, and monozygotic twins discordant for PD may be such “model”. 3 pairs phenotypically and genotypically monozygous twins of Russian ancestry were enrolled in the study. PD was diagnosed in one of each pair. The disease duration was at least 7 years. Data on blood methylomes was analyzed. Points of variable methylation in blood methylomes were selected. With this approach, 8 differentially expressed genes were found that also may be differentially methylated. Changes in methylation level for some of this genes were found in monozygotic twins discordant for PD fibroblasts cell-lines between healthy and afflicted siblings. Acquired data might suggest participation of DNA-methylation in transcription regulation of PD pathogenesis-related candidate genes.

scholarly journals Identification of differentially-expressed genes by DNA methylation in cervical cancer

2015 ◽  
Vol 9 (4) ◽  
pp. 1691-1698 ◽  
Author(s):  
HEUN-SIK LEE ◽  
JUN HO YUN ◽  
JUNGHEE JUNG ◽  
YOUNG YANG ◽  
BONG-JO KIM ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Dna Methylation ◽  
Differentially Expressed Genes ◽  
Differentially Expressed

scholarly journals Bioinformatics analysis for the identification of differentially expressed genes and related signaling pathways in H. pylori-CagA transfected gastric cancer cells

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11203
Author(s):  
Dingyu Chen ◽  
Chao Li ◽  
Yan Zhao ◽  
Jianjiang Zhou ◽  
Qinrong Wang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Helicobacter Pylori ◽  
Differentially Expressed Genes ◽  
Pathway Analysis ◽  
Kegg Pathway ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Ags Cells ◽  
Kegg Pathway Analysis ◽  
Key Genes

Aim Helicobacter pylori cytotoxin-associated protein A (CagA) is an important virulence factor known to induce gastric cancer development. However, the cause and the underlying molecular events of CagA induction remain unclear. Here, we applied integrated bioinformatics to identify the key genes involved in the process of CagA-induced gastric epithelial cell inflammation and can ceration to comprehend the potential molecular mechanisms involved. Materials and Methods AGS cells were transected with pcDNA3.1 and pcDNA3.1::CagA for 24 h. The transfected cells were subjected to transcriptome sequencing to obtain the expressed genes. Differentially expressed genes (DEG) with adjusted P value < 0.05, — logFC —> 2 were screened, and the R package was applied for gene ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The differential gene protein–protein interaction (PPI) network was constructed using the STRING Cytoscape application, which conducted visual analysis to create the key function networks and identify the key genes. Next, the Kaplan–Meier plotter survival analysis tool was employed to analyze the survival of the key genes derived from the PPI network. Further analysis of the key gene expressions in gastric cancer and normal tissues were performed based on The Cancer Genome Atlas (TCGA) database and RT-qPCR verification. Results After transfection of AGS cells, the cell morphology changes in a hummingbird shape and causes the level of CagA phosphorylation to increase. Transcriptomics identified 6882 DEG, of which 4052 were upregulated and 2830 were downregulated, among which q-value < 0.05, FC > 2, and FC under the condition of ≤2. Accordingly, 1062 DEG were screened, of which 594 were upregulated and 468 were downregulated. The DEG participated in a total of 151 biological processes, 56 cell components, and 40 molecular functions. The KEGG pathway analysis revealed that the DEG were involved in 21 pathways. The PPI network analysis revealed three highly interconnected clusters. In addition, 30 DEG with the highest degree were analyzed in the TCGA database. As a result, 12 DEG were found to be highly expressed in gastric cancer, while seven DEG were related to the poor prognosis of gastric cancer. RT-qPCR verification results showed that Helicobacter pylori CagA caused up-regulation of BPTF, caspase3, CDH1, CTNNB1, and POLR2A expression. Conclusion The current comprehensive analysis provides new insights for exploring the effect of CagA in human gastric cancer, which could help us understand the molecular mechanism underlying the occurrence and development of gastric cancer caused by Helicobacter pylori.

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