综合的多omics和机器学习框架确定PRDX4作为氧化还原-EMT调节器和骨转移性乳腺癌的预测标志物
Xiao Zhou1, Longgui Xie1, Jianhui Liu1
1Guangxi Medical University Cancer Hospital, Department of Breast Surgery, Nanning, Guangxi, China.
NPJ precision oncology
|January 22, 2026
概括
机器学习确定过氧化素4 (PRDX4) 是骨转移性乳腺癌的关键生物标志物. 这一发现指导了包括Docetaxel在内的向疗法,以改善患者的治疗结果.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 骨转移显著影响乳腺癌患者的发病率和死亡率.
- 目前的预后模型和针对骨转移性乳腺癌的向治疗方法不足.
研究的目的:
- 开发一种机器学习 (ML) 驱动的多主题框架,整合表皮细胞-介质细胞转换 (EMT) 和核酸代谢 (NM) 签名.
- 确定预后生物标志物,并指导骨转移性乳腺癌的合理药物发现.
主要方法:
- 利用了基因表达综合 (GEO) 和癌症基因组图谱 (TCGA) 骨转移数据集.
- 应用最小绝对收缩和选择操作员 (LASSO) ML来识别与NM相关的枢纽基因.
- 进行多层次分析,共识聚类,药物重定位和分子对接.
主要成果:
- 确定过氧化素4 (PRDX4) 是一个关键的风险相关基因,与低生存率,免疫透,MSI,TMB和EMT相关.
- 将患者分层为不同的EMT-NM分子子组,具有不同的临床结果和免疫特征.
- 鉴定出多塞塔塞尔是一种高亲和度的PRDX4向化合物.
结论:
- 基于ML的多omics分析有效地将生物标志物发现和骨转移性乳腺癌的药物设计联系起来.
- PRDX4作为一个关键的预后生物标志物.
- 研究结果支持多目标和多药物策略,以改善患者的治疗结果.
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