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Updated: Jul 15, 2026

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer
Published on: September 18, 2020
[Gene expression profiling using oligonucleotide microarray in atrophic gastritis and intestinal metaplasia].
Kyong Rae Kim1, Soo Youn Oh, Ung Chae Park
1Department of General Surgery, Konkuk University College of Medicine, Chungju, Korea. kkongr@kku.ac.kr
This study analyzed gene expression in atrophic gastritis with intestinal metaplasia, identifying key molecular patterns. These findings offer a basis for future gastric cancer diagnosis and treatment strategies.
Area of Science:
- Gastroenterology
- Molecular Biology
- Oncology
Background:
- Atrophic gastritis with intestinal metaplasia is a major risk factor for gastric cancer.
- The molecular mechanisms underlying this process are not fully understood.
- Identifying key molecules is crucial for understanding gastric carcinogenesis.
Purpose of the Study:
- To analyze the genome-wide gene expression patterns in patients with atrophic gastritis and intestinal metaplasia.
- To identify differentially expressed genes associated with gastric precancerous lesions.
- To establish a molecular genetic basis for gastric cancer development.
Main Methods:
- Oligonucleotide microarray technique was employed to compare gene expression profiles.
- Significance Analysis of Microarrays (SAM) was used to identify differentially expressed genes.
- Global, intensity-dependent, and box plot normalization methods were applied for data analysis.
Main Results:
- Eight genes (FABP, REG, OR6C1, MEP1, SLC6A1, SI, Mucin 1, RAB23) were significantly upregulated (>10-fold) in atrophic gastritis with intestinal metaplasia.
- One gene (LOC44119) was significantly downregulated (>10-fold).
- Expression patterns of known gastric carcinogenesis genes (e.g., FN1, TP53, TGFB1) showed significant alterations (>2-fold).
Conclusions:
- Oligonucleotide microarray successfully identified genome-wide patterns in atrophic gastritis with intestinal metaplasia.
- These findings provide a foundational bioinformatic resource for clinical applications.
- The results are expected to aid in the diagnosis and treatment of gastric cancer and its precancerous lesions.
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