| Autor: |
Qinxing Cao, Zhenjia Dan, Nengyi Hou, Li Yan, Xingmei Yuan, Hejiang Lu, Song Yu, Jiangping Zhang, Huasheng Xiao, Qiang Liu, Xiaoyong Zhang, Min Zhang, Minghui Pang |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Clinical Epigenetics, Vol 16, Iss 1, Pp 1-14 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1868-7083 |
| DOI: |
10.1186/s13148-024-01735-6 |
| Popis: |
Abstract Background and purpose Early detection, diagnosis, and treatment of colorectal cancer and its precancerous lesions can significantly improve patients’ survival rates. The purpose of this research is to identify methylation markers specific to colorectal cancer tissues and validate their diagnostic capability in colorectal cancer and precancerous changes by measuring the level of DNA methylation in stool samples. Method We analyzed samples from six cancer tissues and adjacent normal tissues and fecal samples from 758 participants, including 62 patients with interfering diseases. Bioinformatics databases were used to screen for candidate biomarkers for CRC, and quantitative methylation-specific PCR methods were applied for identification. The methylation levels of the candidate biomarkers in fecal and tissue samples were measured. Logistic regression and random forest models were built and validated using fecal sample data from one of the centers, and the independent or combined diagnostic value of the candidate biomarkers in fecal samples for CRC and precancerous lesions was analyzed. Finally, the diagnostic capability and stability of the model were validated at another medical center. Results This study identified two colorectal cancer CpG sites with tissue specificity. These two biomarkers have certain diagnostic power when used individually, but their diagnostic value for colorectal cancer and colorectal adenoma is more significant when they are used in combination. Conclusion The results indicate that a DNA methylation biomarker combined diagnostic model based on two CpG sites, cg13096260 and cg12587766, has the potential for screening and diagnosing precancerous lesions and colorectal cancer. Additionally, compared to traditional diagnostic models, machine learning algorithms perform better but may yield more false-positive results, necessitating further investigation. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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