Clinical significance of CDH13 promoter methylation as a biomarker for bladder cancer: a meta-analysis

Chen, Feng; Huang, Tao; Ren, Yu; Wei, Junjun; Lou, Zhongguan; Wang, Xue; Fan, Xiaohui; Chen, Yirun; Weng, Guobin; Yao, Xuping

doi:10.1186/s12894-016-0171-5

Cited by 13 publications

(9 citation statements)

References 37 publications

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“…In addition, CDH13 can induce the reversion of morphology from an invasive to a normal cell-like type. In a previous study, downregulated CDH13 protein expression was found in bladder TCC ( 26 ). However, the effects and mechanisms of downregulated CDH13 expression on bladder TCC invasion need to be further elucidated.…”

Section: Discussionmentioning

confidence: 79%

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CDH13 promoter methylation regulates cisplatin resistance of non-small cell lung cancer cells

et al. 2018

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Reversing cisplatin resistance of lung cancer cell line A549/DDP through recovering cadherin 13 (CDH13) expression by demethylation was investigated in the current study. RT-PCR was used to measure CDH13 expression in lung cancer A549 and A549/DDP cells with or without 5-Aza-CdR intervention. Methylation-specific PCR was used to detect CDH13 methylation. MTT assay and flow cytometry were used to measure the effects of cisplatin on inhibiting cell proliferation, apoptosis, and the reversal of cisplatin resistance. The IC50 value of cisplatin for A549 and A549/DDP cells was 3.278±0.532 and 28.341±1.435 µmol/l, respectively (P<0.05). The cisplatin-resistance index of A549/DDP cells was up to 8.65. After 2.5, 10, or 40 µmol/l 5-Aza-CdR treatment, the apoptotic rates of A549/DDP cells were 9.4±0.86, 18.1±1.42 and 42±2.01%, respectively, which were significantly different to those of the control group (P<0.05). Methylation-specific PCR detected both methylation (M) and unmethylation (U) bands at CDH13 promoter region before 5-Aza-CdR intervention while it only detected an unmethylation band after the treatment with a higher concentration of 5-Aza-CdR, which indicates the transformation to unmethylation state. When 10 µmol/l 5-Aza-CdR was added, the IC50 of cisplatin to A549/DDP cells was 8.472±0.415 µmol/l, and cisplatin resistance was reversed by 3.35-fold. CDH13 methylation is related to the cisplatin resistance of A549/DDP cells. 5-Aza-CdR can inhibit CDH13 methylation and recover CDH13 expression. With the increase in 5-Aza-CdR concentration, the unmethylation state of CDH13 is enhanced, which can strengthen the function of cisplatin inhibiting proliferation and apoptosis in A549/DDP cells.

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Section: Discussionmentioning

confidence: 79%

“…Moreover, an association between the abnormal expression of CDH13 and its promoter methylation in lung cancer has been demonstrated ( 22 – 24 ). Recent studies have reported that CDH13 functioned as an anti-oncogene in lung ( 1 ), breast ( 25 ), ovarian ( 3 ), bladder ( 26 ), esophageal ( 27 ) and gastric cancer ( 28 ).…”

Section: Introductionmentioning

confidence: 99%

CDH13 promoter methylation regulates cisplatin resistance of non-small cell lung cancer cells

et al. 2018

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“…The hypermethylation of the ADHFE1 promoter in colorectal cancer has recently been demonstrated [ 64 , 65 ]. CDH13 promoter methylation has been identified as a biomarker for bladder cancer [ 66 ]. Recently, ENG promoter hypermethylation was reported in several human cancers [ 67 – 69 ].…”

Section: Resultsmentioning

confidence: 99%

LogLoss-BERAF: An ensemble-based machine learning model for constructing highly accurate diagnostic sets of methylation sites accounting for heterogeneity in prostate cancer

et al. 2018

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Although modern methods of whole genome DNA methylation analysis have a wide range of applications, they are not suitable for clinical diagnostics due to their high cost and complexity and due to the large amount of sample DNA required for the analysis. Therefore, it is crucial to be able to identify a relatively small number of methylation sites that provide high precision and sensitivity for the diagnosis of pathological states. We propose an algorithm for constructing limited subsamples from high-dimensional data to form diagnostic panels. We have developed a tool that utilizes different methods of selection to find an optimal, minimum necessary combination of factors using cross-entropy loss metrics (LogLoss) to identify a subset of methylation sites. We show that the algorithm can work effectively with different genome methylation patterns using ensemble-based machine learning methods. Algorithm efficiency, precision and robustness were evaluated using five genome-wide DNA methylation datasets (totaling 626 samples), and each dataset was classified into tumor and non-tumor samples. The algorithm produced an AUC of 0.97 (95% CI: 0.94–0.99, 9 sites) for prostate adenocarcinoma and an AUC of 1.0 (from 2 to 6 sites) for urothelial bladder carcinoma, two types of kidney carcinoma and colorectal carcinoma. For prostate adenocarcinoma we showed that identified differential variability methylation patterns distinguish cluster of samples with higher recurrence rate (hazard ratio for recurrence = 0.48, 95% CI: 0.05–0.92; log-rank test, p-value < 0.03). We also identified several clusters of correlated interchangeable methylation sites that can be used for the elaboration of biological interpretation of the resulting models and for further selection of the sites most suitable for designing diagnostic panels. LogLoss-BERAF is implemented as a standalone python code and open-source code is freely available from https://github.com/bioinformatics-IBCH/logloss-beraf along with the models described in this article.

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“…In ALL, TP53 mutations are rather infrequent. In childhood B-ALL and pediatric T-ALL the reported prevalence is 2–5% of cases [ 24 , 25 ], whilst in adult patients it is 6.4–11.1% of cases [ 26 ]. On the other hand, TP53 mutations occur in 15.7% of ALL cases, which increases with age.…”

Section: Discussionmentioning

confidence: 99%

MGMT promoter methylation as a potential prognostic marker for acute leukemia

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Czech

Gabło

et al. 2017

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IntroductionIt has been proved that genetic and epigenetic changes play a significant role in the development and progression of acute leukemia. The aim of our study was to evaluate the frequency and prognostic implications of genetic and epigenetic alterations in p15, MGMT, DNMT3A and TP53 genes in acute leukemias.Material and methodsWe included in the study 59 patients with acute leukemia. Evaluation of TP53 and DNMT3A mutations was performed using sequencing analysis and PCR-RFLP, respectively. Methylation status of MGMT and p15 genes was evaluated using MSP and COBRA, respectively. For assessment of global DNA methylation ELISA-based kit was used.ResultsWe found that overall survival was higher for ALL patients. MGMT promoter methylation was significantly associated with patients age at the time of diagnosis (p = 0.03). TP53 and DNMT3A mutations were observed only in AML patients (16.67% and 8.8%, respectively). Patients with acute leukemia and p15 promoter methylation had significantly more frequently mutated TP53 gene (p = 0.04) and AML patients with p15 promoter methylation had significantly more frequently detected global hypomethylation of DNA (p = 0.009). In the group of ALL patients we noted an opposite trend: only patients negative for p15 promoter methylation were characterized by global DNA hypomethylation.ConclusionsOur findings demonstrate that MGMT promoter methylation can have a considerable impact on the development of acute leukemia in older patients. DNMT3A and TP53 mutations may play a significant role in AML development. However, further studies conducted in a larger cohort of patients are needed to determine its clinical utility.

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Clinical significance of CDH13 promoter methylation as a biomarker for bladder cancer: a meta-analysis

Cited by 13 publications

References 37 publications

CDH13 promoter methylation regulates cisplatin resistance of non-small cell lung cancer cells

CDH13 promoter methylation regulates cisplatin resistance of non-small cell lung cancer cells

LogLoss-BERAF: An ensemble-based machine learning model for constructing highly accurate diagnostic sets of methylation sites accounting for heterogeneity in prostate cancer

MGMT promoter methylation as a potential prognostic marker for acute leukemia

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