综合性转录基因和机器学习框架揭示了候选基因和乳腺癌中阿弗拉托克素B1暴露的潜在机制
Wenjing Wang1, Mengting Liu1, Xiang Li2
1Department of Breast Disease Center, General Surgery, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China.
Scientific reports
|February 13, 2026
概括
非洲毒素B1 (AFB1) 暴露与乳腺癌有关. 这项研究使用多omics和机器学习来识别七个关键生物标志物 (EGFR,MIF,MET,PPARG,MME,NQO2,NR3C2) 来预测乳腺癌风险和理解AFB1.
科学领域:
- 环境致癌的产生
- 基因组学和计算生物学
- 癌症生物学 癌症生物学
背景情况:
- 甲毒素B1 (AFB1) 是一种与乳腺癌相关的环境真菌毒素.
- 在乳腺癌中AFB1的致癌潜力背后的精确生物机制尚未完全理解.
研究的目的:
- 综合调查AFB1通过哪些分子途径影响乳腺瘤生物学.
- 通过使用多omics和机器学习来确定AFB1相关乳腺癌的预测生物标志物.
主要方法:
- 综合的多omics分析包括转录组学,WGCNA,免疫分析和空间/单细胞转录组学.
- 机器学习管道用于生物标志物发现和分类器开发.
- 对生物标志物的解释性进行SHAP分析.
主要成果:
- 确定了一个由七个关键生物标志物 (EGFR,MIF,MET,PPARG,MME,NQO2,NR3C2) 组成的小组,具有高分辨精度 (AUC=0.996).
- 发现生物标志物的不同作用,其中PPARG具有潜在的保护性和MIF风险促进性.
- 揭示了细胞群体和空间区域的表达异质性.
结论:
- 综合分析框架为AFB1在乳腺癌发展中的作用提供了新的见解.
- 鉴定出的基因组可以作为AFB1诱导的乳腺癌的机械媒介和诊断标记物.
- 突出了多组学和机器学习在环境致癌研究中的实用性.
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