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基于肠道微生物组的机器学习模型用于早期结直肠癌和腺瘤查

Yi-Jian Tsai^1,2, Wei-Ni Lyu³, Nai-Shun Liao¹

¹Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.

|October 9, 2025

概括

一项新的研究显示,肠道微生物体的特征可以非侵入性地检测结直肠癌 (CRC) 和腺瘤. 机器学习模型对早期检测有希望,改善患者的治疗结果.

科学领域:

微生物组研究的研究.
在瘤学瘤学.
生物信息学是一种生物信息学.

背景情况:

大肠直肠癌 (CRC) 是癌症死亡的主要原因.
早期检测,特别是在腺瘤阶段,显著提高了患者的生存率.
目前的查方法,如结肠镜检查和便免疫化学测试 (FIT),对早期病变的侵袭性或敏感性有局限性.

研究的目的:

开发一种针对结直肠癌和腺瘤的非侵入性查策略.
为了识别肠道微生物群的特征,表明结直肠癌和腺瘤.
评估机器学习模型在基于微生物组数据的疾病状态分类方面的有效性.

主要方法:

分析了来自北美和东亚人口的五个公开的16S rRNA测序数据集.
利用微生物组组合分析与偏差校正 (ANCOM-BC) 和千平方测试来识别歧视性微生物种群.
训练随机森林 (RF) 分类模型以区分健康对照,腺瘤和CRC,并开发了微生物风险评分 (MRS).

主要成果:

确定了109种歧视性的微生物种群.
射频模型在内部 (AUC=0.90) 和外部 (AUC=0.82) 验证中表现出强的表现,显示出跨种群通用性.
跨队列的CRC患者的微生物风险评分 (MRS) 显著升高,已知CRC相关病原体的丰富.

结论:

肠道微生物组签名对于早期,非侵入性检测结直肠癌和腺瘤具有重大潜力.
机器学习方法与微生物组分析相结合,为改善CRC查提供了一个可扩展的策略.
这些发现支持特定肠道细菌在结直肠癌的发展和检测中的生物相关性.

关键词:

腺瘤是一种腺瘤.结肠直肠癌是一种癌症.我们的肠道微生物组.机器学习是机器学习.微生物风险评分的微生物风险评分.非侵入性的查.随机的森林随机的森林

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