Dataset Information

A systematic analysis on DNA methylation and the expression of both mRNA and microRNA in bladder cancer.

ABSTRACT:

Background

DNA methylation aberration and microRNA (miRNA) deregulation have been observed in many types of cancers. A systematic study of methylome and transcriptome in bladder urothelial carcinoma has never been reported.

Methodology/principal findings

The DNA methylation was profiled by modified methylation-specific digital karyotyping (MMSDK) and the expression of mRNAs and miRNAs was analyzed by digital gene expression (DGE) sequencing in tumors and matched normal adjacent tissues obtained from 9 bladder urothelial carcinoma patients. We found that a set of significantly enriched pathways disrupted in bladder urothelial carcinoma primarily related to "neurogenesis" and "cell differentiation" by integrated analysis of -omics data. Furthermore, we identified an intriguing collection of cancer-related genes that were deregulated at the levels of DNA methylation and mRNA expression, and we validated several of these genes (HIC1, SLIT2, RASAL1, and KRT17) by Bisulfite Sequencing PCR and Reverse Transcription qPCR in a panel of 33 bladder cancer samples.

Conclusions/significance

We characterized the profiles between methylome and transcriptome in bladder urothelial carcinoma, identified a set of significantly enriched key pathways, and screened four aberrantly methylated and expressed genes. Conclusively, our findings shed light on a new avenue for basic bladder cancer research.

SUBMITTER: Zhu J

PROVIDER: S-EPMC3227661 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Publications

A systematic analysis on DNA methylation and the expression of both mRNA and microRNA in bladder cancer.

Zhu Jialou J Jiang Zhimao Z Gao Fei F Hu Xueda X Zhou Liang L Chen Jiahao J Luo Huijuan H Sun Jihua J Wu Song S Han Yonghua Y Yin Guangliang G Chen Maoshan M Han Zujing Z Li Xianxin X Huang Yi Y Zhang Weixing W Zhou Fangjian F Chen Tong T Fa Pingping P Wang Yong Y Sun Liang L Leng Huimin H Sun Fenghao F Liu Yuchen Y Ye Mingzhi M Yang Huanming H Cai Zhiming Z Gui Yaoting Y Zhang Xiuqing X

PloS one 20111130 11

<h4>Background</h4>DNA methylation aberration and microRNA (miRNA) deregulation have been observed in many types of cancers. A systematic study of methylome and transcriptome in bladder urothelial carcinoma has never been reported.<h4>Methodology/principal findings</h4>The DNA methylation was profiled by modified methylation-specific digital karyotyping (MMSDK) and the expression of mRNAs and miRNAs was analyzed by digital gene expression (DGE) sequencing in tumors and matched normal adjacent ti ...[more]

PMID: 22140553

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Project description:Introduction: Despite the significant progress in understanding cancer biology, the deduction of metastasis is still a challenge in the clinic. Transcriptional regulation is one of the critical mechanisms underlying cancer development. Even though mRNA, microRNA, and DNA methylation mechanisms have a crucial impact on the metastatic outcome, there are no comprehensive data mining models that combine all transcriptional regulation aspects for metastasis prediction. This study focused on identifying the regulatory impact of genetic biomarkers for monitoring metastatic molecular signatures of melanoma by investigating the consolidated effect of miRNA, mRNA, and DNA methylation. Method: We developed multiple machine learning models to distinguish the metastasis by integrating miRNA, mRNA, and DNA methylation markers. We used the TCGA melanoma dataset to differentiate between metastatic melanoma samples by assessing a set of predictive models. For this purpose, machine learning models using a support vector machine with different kernels, artificial neural networks, random forests, AdaBoost, and Naïve Bayes are compared. An iterative combination of differentially expressed miRNA, mRNA, and methylation signatures is used as a candidate marker to reveal each new biomarker category's impact. In each iteration, the performances of the combined models are calculated. During all comparisons, the choice of the feature selection method and under and oversampling approaches are analyzed. Selected biomarkers of the highest performing models are further analyzed for the biological interpretation of functional enrichment. Results: In the initial model, miRNA biomarkers can identify metastatic melanoma with an 81% F-score. The addition of mRNA markers upon miRNA increased the F-score to 92%. In the final integrated model, the addition of the methylation data resulted in a similar F-score of 92% but produced a stable model with low variance across multiple trials. Conclusion: Our results support the role of miRNA regulation in metastatic melanoma as miRNA markers model metastasis outcomes with high accuracy. Moreover, the integrated evaluation of miRNA with mRNA and methylation biomarkers increases the model's power. It populates selected biomarkers on the metastasis-associated pathways of melanoma, such as the "osteoclast", "Rap1 signaling", and "chemokine signaling" pathways. Source Code: https://github.com/aysegul-kt/MelonomaMetastasisPrediction/.

Dataset Information

A systematic analysis on DNA methylation and the expression of both mRNA and microRNA in bladder cancer.

Background

Methodology/principal findings

Conclusions/significance

Publications

A systematic analysis on DNA methylation and the expression of both mRNA and microRNA in bladder cancer.

Similar Datasets

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