Differentiation and adaptation epigenetic networks: Translational research in gastric carcinogenesis

Deng, Dajun; Lu, Zheming

doi:10.1007/s11434-012-5578-0

Cited by 17 publications

(4 citation statements)

References 37 publications

(39 reference statements)

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“…Most importantly, alterations of DNA methylation, even if they occur only in a very limited number of cells in specimens, can be sensitively detected using methylation or unmethylation specific assays. These DNA methylation changes can be used as one kind of optimal biomarker for predicting disease development and progression [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Significant impact of amount of PCR input templates on various PCR-based DNA methylation analysis and countermeasure

Liu

Zhou

et al. 2016

Oncotarget

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Methylation changes of CpG islands can be determined using PCR-based assays. However, the exact impact of the amount of input templates (TAIT) on DNA methylation analysis has not been previously recognized. Using COL2A1 gene as an input reference, TAIT difference between human tissues with methylation-positive and −negative detection was calculated for two representative genes GFRA1 and P16. Results revealed that TAIT in GFRA1 methylation-positive frozen samples (n = 332) was significantly higher than the methylation-negative ones (n = 44) (P < 0.001). Similar difference was found in P16 methylation analysis. The TAIT-related effect was also observed in methylation-specific PCR (MSP) and denatured high performance liquid chromatography (DHPLC) analysis. Further study showed that the minimum TAIT for a successful MethyLight PCR reaction should be ≥ 9.4 ng (CtCOL2A1 ≤ 29.3), when the cutoff value of the methylated-GFRA1 proportion for methylation-positive detection was set at 1.6%. After TAIT of the methylation non-informative frozen samples (n = 94; CtCOL2A1 > 29.3) was increased above the minimum TAIT, the methylation-positive rate increased from 72.3% to 95.7% for GFRA1 and 26.6% to 54.3% for P16, respectively (Ps < 0.001). Similar results were observed in the FFPE samples. In conclusion, TAIT critically affects results of various PCR-based DNA methylation analyses. Characterization of the minimum TAIT for target CpG islands is essential to avoid false-negative results.

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Section: Introductionmentioning

confidence: 99%

Significant impact of amount of PCR input templates on various PCR-based DNA methylation analysis and countermeasure

Liu

Zhou

et al. 2016

Oncotarget

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“…The methylation states of cell differentiation related genes have been shown to be highly dynamic during germ cell and preimplantation development, but become relatively static during the development of somatic tissues [1] – [3] . In contrast, the methylation states of host adaptation related genes remain dynamic in somatic tissues in order to allow for the proper response to environmental factor exposure and subsequent pathogenesis [4] – [6] . Hydroxymethylation of DNA is intimately involved in altering gene methylation status and has been found to not only play a key role in DNA demethylation, but also serve many of its own functions [7] , [8] .…”

Section: Introductionmentioning

confidence: 99%

Homeostatic Maintenance of Allele-Specific p16 Methylation in Cancer Cells Accompanied by Dynamic Focal Methylation and Hydroxymethylation

Qin

Zhou

et al. 2014

PLoS ONE

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Aim p16 Methylation frequently occurs in carcinogenesis. While it has been hypothesized that the p16 methylation states are dynamically maintained in cancer cells, direct evidence supporting this hypothesis has not been available until now.MethodsA fusion cell model was established which reprogrammed the native DNA methylation pattern of the cells. The methylation status of the p16 alleles was then repeatedly quantitatively analyzed in the fusion monoclonal, parental cancer cell lines (p16-completely methylated-AGS and unmethylated-MGC803), and HCT116 non-fusion cell using DHPLC and bisulfite sequencing. Histone methylation was analyzed using chromatin immuno-precipitation (ChIP)-PCR. P16 expression status was determined using immuno-staining and RT-PCR.ResultsThe methylation status for the majority of the p16 alleles was stably maintained in the fusion monoclonal cells after up to 60 passages. Most importantly, focal de novo methylation, demethylation, and hydroxymethylation were consistently observed within about 27% of the p16 alleles in the fusion monoclones, but not the homozygously methylated or unmethylated parental cells. Furthermore, subclones of the monoclones consistently maintained the same p16 methylation pattern. A similar phenomenon was also observed using the p16 hemi-methylated HCT116 non-fusion cancer cell line. Interestingly, transcription was not observed in p16 alleles that were hydroxymethylated with an antisense-strand-specific pattern. Also, the levels of H3K9 and H3K4 trimethylation in the fusion cells were found to be slightly lower than the parental AGS and MGC803 cells, respectively.ConclusionThe present study provides the first direct evidence confirming that the methylation states of p16 CpG islands is not only homeostatically maintained, but also accompanied by a dynamic process of transient focal methylation, demethylation, and hydroxymethylation in cancer cells.

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“…Numerous studies have demonstrated great potentials of using gene-specific DNA methylation changes as biomarkers for early detection of cancer, diagnosis and prognosis of cancer, and predicting chemotherapy sensitivity and drug resistance. DNA methylation markers may be better suited in clinical oncology practice due to its high sensitivity of detection, high stability in the genome, and relatively low requirements for sample storage [ 30 , 31 ]. For example, Sept9 methylation is used as a biomarker for colorectal cancer screening, MGMT methylation is used for predicting sensitivity of gliomas to alkylating agents, and DNA methylation panel is used to characterize tissue origin of un-identified cancers [ 32 , 33 ].…”

Section: Discussionmentioning

confidence: 99%

A similar effect of P16 hydroxymethylation and true-methylation on the prediction of malignant transformation of oral epithelial dysplasia: observation from a prospective study

Liu

et al. 2018

BMC Cancer

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BackgroundTotal P16 methylation (P16M), including P16 hydroxymethylation (P16H) and true-P16M, correlates with malignant transformation of oral epithelial dysplasia (OED). Both true-P16M and P16H are early events in carcinogenesis. The aim of this study is to prospectively determine if discrimination of true-P16M from P16H is necessary for prediction of cancer development from OEDs.MethodsPatients (n = 265) with mild or moderate OED were recruited into the double blind two-center cohort. Total-P16M and P16H were analyzed using the 115-bp MethyLight, TET-assisted bisulfite (TAB) methylation-specific PCR (MSP), and TAB-sequencing. Total-P16M-positive and P16H-negative samples were defined as true-P16M-positive. Progression of OEDs was monitored for a minimum 24 months follow-up period.ResultsP16H was detected in 23 of 73 (31.5%) total-P16M-positive OEDs. Follow-up information was obtained from 247 patients with an ultimate compliance rate of 93.2%. OED-derived squamous cell carcinomas were observed in 13.0% (32/247) patients during follow-up (median, 41.0 months). The cancer progression rate for total-P16M-positive patients was significantly increased when compared to total-P16M-negative patients [23.3% vs 8.6%; adjusted odds ratio = 2.67 (95% CI: 1.19–5.99)]. However, the cancer progression rates were similar between P16H- and true-P16M-positive OEDs [26.1% (6/23) vs 22.0% (11/50); odds ratio = 0.80 (95% CI: 0.22–2.92)]. The cancer-free survival was also similar for these patients.ConclusionP16H and true-P16M are similar biomarkers for determining malignant potential of OEDs. Discrimination of P16H from true-P16M, at least in OED, may be not necessary in clinical applications.Trial registrationThis study is registered prospectively in the U.S. National Institutes of Health Clinical Trials Protocol Registration System (trial number NCT02967120, available at https://ClinicalTrials.gov/ct2/show/NCT02967120).Electronic supplementary materialThe online version of this article (10.1186/s12885-018-4787-6) contains supplementary material, which is available to authorized users.

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Differentiation and adaptation epigenetic networks: Translational research in gastric carcinogenesis

Cited by 17 publications

References 37 publications

Significant impact of amount of PCR input templates on various PCR-based DNA methylation analysis and countermeasure

Significant impact of amount of PCR input templates on various PCR-based DNA methylation analysis and countermeasure

Homeostatic Maintenance of Allele-Specific p16 Methylation in Cancer Cells Accompanied by Dynamic Focal Methylation and Hydroxymethylation

A similar effect of P16 hydroxymethylation and true-methylation on the prediction of malignant transformation of oral epithelial dysplasia: observation from a prospective study

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