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Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer
Published on: September 18, 2020
Multiple gene methylation of nonsmall cell lung cancers evaluated with 3-dimensional microarray
Yan Wang1, Dingdong Zhang, Wenli Zheng
1State Key Laboratory of Bioelectronics, College of Biological Science and Medical Engineering, Southeast University, Nanjing, China.
Cancer
|February 21, 2008
Summary
Aberrant DNA methylation in cancer-related genes is common. A novel 3-D DNA microarray effectively detected hypermethylation in nonsmall cell lung cancers, offering a high-throughput platform for analysis.
Area of Science:
- Oncology
- Molecular Biology
- Genetics
Background:
- Aberrant DNA methylation of CpG islands in cancer-related genes is an early and frequent event in cancer development.
- This alteration holds potential for cancer diagnosis and monitoring disease recurrence.
Purpose of the Study:
- To develop and validate a novel 3-dimensional (3-D) DNA microarray technique for detecting aberrant DNA hypermethylation.
- To assess the frequency of promoter methylation in 15 cancer-related genes in nonsmall cell lung cancer (NSCLC) tissues.
Main Methods:
- A 3-D polyacrylamide gel-based DNA microarray was developed, coupled with linker-polymerase chain reaction (PCR).
- The method was used to analyze promoter methylation of 15 genes in 28 primary NSCLC samples and 12 adjacent nonmalignant lung tissues.
Main Results:
- The 3-D microarray successfully detected aberrant promoter methylation in multiple genes within NSCLC samples.
- High frequencies of methylation were observed for genes such as adenomatous polyposis coli (APC) (68%) and calcitonin gene-related polypeptide alpha (CALCA) (64%).
- Methylation was infrequent in corresponding nonmalignant tissues, and methylation patterns correlated with clinicopathologic characteristics.
Conclusions:
- The developed 3-D microarray serves as an effective platform for detecting DNA hypermethylation.
- This high-throughput approach facilitates comprehensive DNA hypermethylation analysis for cancer research.