基于XGBoost的尿路微生物特征使尿路癌的非侵入性诊断和预后成为可能
Hao Xie1,2, Changming Dong2, Yue Li2
1School of Clinical Medicine, Shandong Second Medical University, Weifang, Shandong, 261000, China.
BMC microbiology
|November 12, 2025
概括
尿路微生物群的特征显示出其作为尿路癌 (UC) 诊断和预后的非侵入性生物标志物的潜力. 这项研究分析了UC亚型,确定了关键的微生物差异,并开发了准确的诊断模型.
科学领域:
- 微生物组研究的研究.
- 在瘤学瘤学.
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
背景情况:
- 尿路癌 (UC) 是一种普遍存在的尿路系统恶性瘤,复发率很高.
- 以前的研究将尿路微生物群与膀癌联系起来,但UC亚型和临床影响仍未得到充分研究.
- 这项研究调查了UC亚型的微生物群,以确定诊断和预后生物标志物.
研究的目的:
- 分析各种尿路癌 (UC) 亚型的尿路微生物群概况.
- 为UC检测建立基于机器学习的诊断模型.
- 为了确定尿路微生物群中的潜在预后生物标志物.
主要方法:
- 来自112名受试者的尿样 (16S rDNA) 测序 (63名膀癌,29名上道尿路癌,9名癌,40名健康对照).
- 分析微生物多样性,社区网络和临床协会.
- 开发和验证基于XGBoost的诊断模型,使用SHAP进行解释性.
主要成果:
- 与健康对照组相比,尿癌 (UC) 组表现出较高的α多样性,具有显著的Streptococcus和Clostridium的丰富.
- 在瘤样本中观察到微生物网络结构的显著差异.
- 一个尿路微生物群模型在膀癌检测方面表现出高准确度 (AUC=0.927),而Lachnospiraceae则显示出预后潜力.
结论:
- 尿路微生物群签名为尿路癌 (UC) 诊断提供了有希望的非侵入性生物标志物.
- 在UC亚型中,微生物的形状不同,为疾病机制提供了洞察力.
- 这些发现支持使用尿道微生物群来诊断和预测UC的结果.
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