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      Epigenetic alterations of CYLD promoter modulate its expression in gastric adenocarcinoma: A footprint of infections : GHADAMI et al .

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          Abstract

          <p class="first" id="d12811989e245">Gastric cancer (GC) is one of the most common causes of cancer-related death in the world, with multiple genetic and epigenetic alterations involved in disease development. CYLD tumor suppressor gene encodes a multifunctional deubiquitinase which negatively regulates various signaling pathways. Deregulation of this gene has been found in different types of cancer. This study aimed to evaluate for the first time the CpG island methylation pattern of CYLD gene promoter, and its expression level in gastric adenocarcinoma. CYLD messenger RNA expression and promoter methylation in 53 tumoral and their non-neoplastic counterpart tissues were assessed using quantitative polymerase chain reaction and bisulfite sequencing. Also, we investigated the impacts of the infectious agents including Helicobacter pylori (H. pylori), EBV, and CMV on CYLD expression and promoter methylation in GC. Results showed that the expression level of CYLD was downregulated in GC, and was significantly associated with gender (female), patient's age (&lt;60), high grade, and no lymph-node metastasis (p = 0.001, 0.002, 0.03, and 0.003, respectively). Among the 31 analyzed CpG sites located in about 600 bp region within the promoter, two CpG sites were hypermethylated in GC tissues. We also found a significant inverse association between DNA promoter methylation and CYLD expression (p = 0.02). Furthermore, a direct association between H. pylori, EBV, and CMV infections with hypermethylation and reduced CYLD expression was observed (p = 0.04, 0.03, and 0.03, respectively). Our findings indicate that CYLD is downregulated in GC. Infectious agents may influence CYLD expression. </p>

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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            CpG island hypermethylation and tumor suppressor genes: a booming present, a brighter future.

            We have come a long way since the first reports of the existence of aberrant DNA methylation in human cancer. Hypermethylation of CpG islands located in the promoter regions of tumor suppressor genes is now firmly established as an important mechanism for gene inactivation. CpG island hypermethylation has been described in almost every tumor type. Many cellular pathways are inactivated by this type of epigenetic lesion: DNA repair (hMLH1, MGMT), cell cycle (p16(INK4a), p15(INK4b), p14(ARF)), apoptosis (DAPK), cell adherence (CDH1, CDH13), detoxification (GSTP1), etc em leader However, we still know little of the mechanisms of aberrant methylation and why certain genes are selected over others. Hypermethylation is not an isolated layer of epigenetic control, but is linked to the other pieces of the puzzle such as methyl-binding proteins, DNA methyltransferases and histone deacetylase, but our understanding of the degree of specificity of these epigenetic layers in the silencing of specific tumor suppressor genes remains incomplete. The explosion of user-friendly technologies has given rise to a rapidly increasing list of hypermethylated genes. Careful functional and genetic studies are necessary to determine which hypermethylation events are truly relevant for human tumorigenesis. The development of CpG island hypermethylation profiles for every form of human tumors has yielded valuable pilot clinical data in monitoring and treating cancer patients based in our knowledge of DNA methylation. Basic and translational will both be needed in the near future to fully understand the mechanisms, roles and uses of CpG island hypermethylation in human cancer. The expectations are high.
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              DNA methylation markers for diagnosis and prognosis of common cancers.

              The ability to identify a specific cancer using minimally invasive biopsy holds great promise for improving the diagnosis, treatment selection, and prediction of prognosis in cancer. Using whole-genome methylation data from The Cancer Genome Atlas (TCGA) and machine learning methods, we evaluated the utility of DNA methylation for differentiating tumor tissue and normal tissue for four common cancers (breast, colon, liver, and lung). We identified cancer markers in a training cohort of 1,619 tumor samples and 173 matched adjacent normal tissue samples. We replicated our findings in a separate TCGA cohort of 791 tumor samples and 93 matched adjacent normal tissue samples, as well as an independent Chinese cohort of 394 tumor samples and 324 matched adjacent normal tissue samples. The DNA methylation analysis could predict cancer versus normal tissue with more than 95% accuracy in these three cohorts, demonstrating accuracy comparable to typical diagnostic methods. This analysis also correctly identified 29 of 30 colorectal cancer metastases to the liver and 32 of 34 colorectal cancer metastases to the lung. We also found that methylation patterns can predict prognosis and survival. We correlated differential methylation of CpG sites predictive of cancer with expression of associated genes known to be important in cancer biology, showing decreased expression with increased methylation, as expected. We verified gene expression profiles in a mouse model of hepatocellular carcinoma. Taken together, these findings demonstrate the utility of methylation biomarkers for the molecular characterization of cancer, with implications for diagnosis and prognosis.
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                Author and article information

                Contributors
                (View ORCID Profile)
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                Journal
                Journal of Cellular Physiology
                J Cell Physiol
                Wiley
                00219541
                April 2019
                April 2019
                August 21 2018
                : 234
                : 4
                : 4115-4124
                Affiliations
                [1 ]Department of Genetics, Faculty of Medicine; Babol University of Medical Sciences; Babol Iran
                [2 ]Department of Genetics; Cellular and Molecular Biology Research Center, Health Research Institute, Babol University of Medical Sciences; Babol Iran
                [3 ]Department of Surgery; Rouhani Hospital, Babol University of Medical Sciences; Babol Iran
                [4 ]Department of Molecular Biology; North Research Center of Pasteur Institute; Amol Iran
                [5 ]Department of Pathology; Rouhani Hospital, Babol University of Medical Sciences; Babol Iran
                [6 ]Department of Internal Medicine; Rouhani Hospital, Babol University of Medical Sciences; Babol Iran
                [7 ]Department of Thoracic Surgery; Imam Khomeini Hospital, Mazandaran University of Medical Sciences; Sari Iran
                [8 ]Department of Pathology; Imam Khomeini Hospital, Mazandaran University of Medical Sciences; Sari Iran
                [9 ]Department of Genetics; Gastrointestinal and Liver Diseases Research Center (GLDRC), Guilan University of Medical Sciences; Rasht Iran
                [10 ]Faculty of Paramedicine; Babol University of Medical Sciences; Babol Iran
                Article
                10.1002/jcp.27220
                30132887
                84fae797-e5be-429a-9e5d-a1c70a039b28
                © 2018

                http://doi.wiley.com/10.1002/tdm_license_1.1

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