Identification of differentially expressed genes regulated by methylation in colon cancer based on bioinformatics analysis

Yu, Liang; Zhang, Cheng; Dai, Dong‐Qiu

doi:10.3748/wjg.v25.i26.3392

Cited by 43 publications

(41 citation statements)

References 55 publications

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“…Over-representation analysis revealed that our gene list was enriched in genes important for developmental and differentiation processes, cell communication, metabolic processes, and regulation of transcription. All of these biological processes are linked to cancer hallmarks, and similar methylation studies focusing on various types of cancer also present gene lists enriched in developmental processes [27], metabolic processes [28] and regulation of transcription [29]. Additionally, we showed that our gene lists contain many non-coding RNA genes, primarily consisting of lncRNAs, suggesting that methylation alterations at non-coding gene promoters are frequently present in cancer.…”

Section: Discussionsupporting

confidence: 69%

Machine Classification of Methylomes in Cancer

Newsham

Sendera

Jammula

et al. 2020

Preprint

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Cancer remains a leading cause of morbidity and mortality worldwide. Its evolutionary nature and resultant complex interactions with the tumour micro-environment and the host immune system engender heterogeneity, make developing interventions difficult. Usually detected at the advanced stages of disease, metastatic cancer accounts for 90% of cancer-associated deaths. Therefore early detection of cancer, combined with current therapies, would have a significant impact on survival and treatment of this insidious disease. Epigenetic changes such as DNA methylation are some of the early events in carcinogenesis. Here, we report on a machine learning model that can classify 13 types of cancer as well as non-cancer tissue samples using only DNA methylome data, with an accuracy of 98.2%. We utilise the features identified by this model to develop a robust deep neural network that can generalise to independent data sets. We also demonstrate that the methylation associated genomic loci detected by the classifier are associated with genes involved in cancer, providing insights into the epigenomic regulation of carcinogenesis.

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Section: Discussionsupporting

confidence: 69%

Machine Classification of Methylomes in Cancer

Newsham

Sendera

Jammula

et al. 2020

Preprint

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“…Microarray technology reveals numerous genes that are activated in different tissues as well as their physiological and pathological statuses and has been regarded as a novel approach for clarifying the mechanisms underlying different diseases[ 10 ]. In recent years with the optimization of gene sequencing platforms, differentially expressed genes (DEGs) have been identified using bioinformatics analyses[ 11 ]. To date, several studies have reported the bioinformatics analysis of IBD using arrays or chips[ 12 , 13 ], but the analysis of UC is still lacking.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we applied two databases from Gene Expression Omnibus (GEO), GEO2R and online tools for constructing Venn diagrams to identify the DEGs, including upregulated and downregulated genes. Then, the Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to analyze the DEGs based on the molecular function (MF), cellular component (CC), and biological process (BP) and different Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways[ 11 ]. Third, we constructed a protein-protein interaction (PPI) network using Cytoscape and the Molecular Complex Detection (MCODE) app for further analysis.…”

Section: Introductionmentioning

confidence: 99%

Identification of differentially expressed genes in ulcerative colitis and verification in a colitis mouse model by bioinformatics analyses

Shi

Han

et al. 2020

WJG

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BACKGROUND Ulcerative colitis (UC) is an inflammatory bowel disease that is difficult to diagnose and treat. To date, the degree of inflammation in patients with UC has mainly been determined by measuring the levels of nonspecific indicators, such as C-reactive protein and the erythrocyte sedimentation rate, but these indicators have an unsatisfactory specificity. In this study, we performed bioinformatics analysis using data from the National Center for Biotechnology Information-Gene Expression Omnibus (NCBI-GEO) databases and verified the selected core genes in a mouse model of dextran sulfate sodium (DSS)-induced colitis. AIM To identify UC-related differentially expressed genes (DEGs) using a bioinformatics analysis and verify them in vivo and to identify novel biomarkers and the underlying mechanisms of UC. METHODS Two microarray datasets from the NCBI-GEO database were used, and DEGs between patients with UC and healthy controls were analyzed using GEO2R and Venn diagrams. We annotated these genes based on their functions and signaling pathways, and then protein-protein interactions (PPIs) were identified using the Search Tool for the Retrieval of Interacting Genes. The data were further analyzed with Cytoscape software and the Molecular Complex Detection (MCODE) app. The core genes were selected and a Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was performed. Finally, colitis model mice were established by administering DSS, and the top three core genes were verified in colitis mice using real-time polymerase chain reaction (PCR). RESULTS One hundred and seventy-seven DEGs, 118 upregulated and 59 downregulated, were initially identified from the GEO2R analysis and predominantly participated in inflammation-related pathways. Seven clusters with close interactions in UC formed: Seventeen core genes were upregulated [ C-X-C motif chemokine ligand 13 ( CXCL13 ), C-X-C motif chemokine receptor 2 ( CXCR2 ), CXCL9 , CXCL5 , C-C motif chemokine ligand 18 , interleukin 1 beta , matrix metallopeptidase 9 , CXCL3 , formyl peptide receptor 1 , complement component 3 , CXCL8 , CXCL1 , CXCL10 , CXCL2 , CXCL6 , CXCL11 and hydroxycarboxylic acid receptor 3 ] and one was downregulated [ neuropeptide Y receptor Y1 ( NYP1R )] in the top cluster according to the PPI and MCODE analyses. These genes were substantially enriched in the cytokine-cy...

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“…HCC carcinogenesis is a complex pathological process which associated with speci c tumor genes, multiple signaling cascades as well as epigenetic modi cations [22,23]. In recent years, bioinformatics analyses have been widely used to identify novel diagnostic markers and therapeutic targets for many cancers [24,25], which provide helpful tools for tumor researches. By bioinformatics analyzing, we explored DEGs between HCC tissues and adjacent tumor tissues, based on 3 independent GEO expression datasets in this study.…”

Section: Discussionmentioning

confidence: 99%

Identification of Key Genes and Evaluation of Their Prognosis Potential in Hepatocellular Carcinoma by Integrated Bioinformatics Analysis

Liu

et al. 2020

Preprint

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Abstract Background Hepatocellular carcinoma (HCC) is a malignancy causing highly death rate in the world. Despite the development of treatment strategies for HCC, prognosis of this malignancy remains unsatisfactory. In this study, we aimed to identify the target genes associated with the prognosis of HCC patients. Methods Three expression profiles of HCC tissues were extracted from the Gene Expression Omnibus database to explore the differentially expressed genes (DEGs) using GEO2R method. Functional enrichment analysis was performed to reveal the biological characteristics of DEGs. Protein-protein interaction (PPI) network was constructed using Cytoscape software. The survival curve of identified DEGs were tested by Kaplan-Meier analysis. Results We identified 15 DEGs (CYP39A1, CYR61, FOS, FOXO1, GADD45B, ID1, IL1RAP, MT1M, PHLDA1, RND3, SDS, SOCS2, TAT, S100P, and SPINK1) in HCC tissues. Prognosis analysis showed that 4 DEGs (FOXO1,SPINK1༌SOCS2, and TAT) correlated with overall survival time of HCC patients, which might serve as therapeutic targets for HCC patients. Conclusions By integrated bioinformatics analysis, we proposed a novel way to reveal key genes that closely relate to HCC development.

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Identification of differentially expressed genes regulated by methylation in colon cancer based on bioinformatics analysis

Cited by 43 publications

References 55 publications

Machine Classification of Methylomes in Cancer

Machine Classification of Methylomes in Cancer

Identification of differentially expressed genes in ulcerative colitis and verification in a colitis mouse model by bioinformatics analyses

Identification of Key Genes and Evaluation of Their Prognosis Potential in Hepatocellular Carcinoma by Integrated Bioinformatics Analysis

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