基于DNA甲基化的机器学习模型用于口腔癌和潜在恶性病变的分类:一项概念验证研究
Gowri Sivaramakrishnan1, Kannan Sridharan2, Mohammed Abdulla AlMuharraqi3
1Bahrain Defence Force Royal Medical Services, Bahrain.
Journal of stomatology, oral and maxillofacial surgery
|October 6, 2025
概括
机器学习模型显示,使用DNA甲基化对口腔组织进行分类是有前途的. 虽然对瘤和正常组织有效,但检测口腔潜在恶性病变仍然是一个挑战.
科学领域:
- 基因组学和生物信息学
- 癌症研究 癌症研究
- 计算生物学 计算生物学
背景情况:
- 口腔状细胞癌 (OSCC) 和口腔潜在恶性病变 (OPLs) 的准确分类受到组织病理学变异性的阻碍.
- 有限的预测生物标志物需要新的口腔病变诊断方法.
- 基因甲基化为组织分类提供了潜在的分子特征,但其应用尚未得到充分探索.
研究的目的:
- 评估DNA甲基化模式对分类口腔组织 (包括OSCC,OPL和正常组织) 的有用性.
- 为了比较各种监督机器学习模型在基于DNA甲基化的口腔组织分类中的性能.
- 确定关键的DNA甲基化标记物,并评估它们在口腔癌诊断中临床应用的潜力.
主要方法:
- 统一的公开可用的DNA甲基化数据集 (GSE97784, GSE204943; n=142).
- 选择了前100个最可变的CpG站点进行分析.
- 训练了八个监督机器学习模型 (RF,GBM,XGBoost等) 使用十倍交叉验证.
主要成果:
- 高变异性CpG位点集中在染色体1,2和6上的基因体中.
- 主要成分分析表明,复杂的甲基化模式需要55个成分以达到90%的差异.
- 随机森林 (RF) 实现了最高准确度 (78%) 和AUC-ROC (0.84),优于其他模型,尽管OPL检测灵敏度低 (<50%).
结论:
- 整体机器学习模型,特别是射频和梯度增强机 (GBM),显示了基于DNA甲基化的口腔组织分类的概念验证.
- 实现了瘤和正常组织的稳健分类,但OPL检测需要进一步改进.
- 较大,平衡的数据集和互补的生物标志物对于提高DNA甲基化在口腔瘤学中的早期检测和临床实用性至关重要.
相关概念视频
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Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
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02:43Mouse Models of Cancer Study
Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
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