Department of Geriatric General Surgery, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Chongqing Bohao Diagnostic Technology Co., Ltd, Chongqing, 410010, China.
Clin Epigenetics. 2024 Sep 7;16(1):122. doi: 10.1186/s13148-024-01735-6.
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.
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.
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.
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.
早期发现、诊断和治疗结直肠癌及其癌前病变可以显著提高患者的生存率。本研究旨在通过测量粪便样本中的 DNA 甲基化水平，鉴定出特定于结直肠组织的甲基化标记物，并验证其在结直肠癌和癌前病变中的诊断能力。
我们分析了来自 6 个癌症组织和相邻正常组织的样本以及来自 758 名参与者的粪便样本，其中包括 62 名患有干扰性疾病的患者。我们使用生物信息学数据库筛选结直肠癌的候选生物标志物，并应用定量甲基化特异性 PCR 方法进行鉴定。测量了粪便和组织样本中候选生物标志物的甲基化水平。使用其中一个中心的粪便样本数据构建并验证了逻辑回归和随机森林模型，并分析了候选生物标志物在粪便样本中对结直肠癌和癌前病变的独立或联合诊断价值。最后，在另一家医疗机构验证了模型的诊断能力和稳定性。
本研究确定了两个具有组织特异性的结直肠癌 CpG 位点。这两个生物标志物单独使用时具有一定的诊断能力，但联合使用时对结直肠癌和结直肠腺瘤的诊断价值更为显著。
结果表明，基于两个 CpG 位点（cg13096260 和 cg12587766）的 DNA 甲基化生物标志物联合诊断模型具有筛查和诊断癌前病变和结直肠癌的潜力。此外，与传统诊断模型相比，机器学习算法的性能更好，但可能会产生更多的假阳性结果，需要进一步研究。
