由人工智能衍生的五基因特征预测了在基于博雷佐米布的治疗下多发性骨髓瘤的风险
Grazia Gargano1,2, Susanna Anita Pappagallo3, Angela Maria Quinto3
1Department of Mathematics, University of Bari Aldo Moro, Bari, Italy. grazia.gargano@uniba.it.
Scientific reports
|December 3, 2025
概括
一个新的预后模型,MM-5C,使用骨髓微环境中的五个基因来预测多发性骨髓瘤 (MM) 患者的结果. 这种方法增强了个性化治疗策略的风险分层.
科学领域:
- 血液学 血液学 血液学
- 在瘤学瘤学.
- 基因组学就是基因组学.
背景情况:
- 多发性骨髓瘤 (MM) 是一种血细胞癌,治疗方法有限.
- 预后往往忽略了骨髓瘤微环境 (TME).
- 长期肌痛严重影响了MM的进展和治疗耐药性.
研究的目的:
- 开发多发性骨髓瘤 (MM) 风险分层的预后模型.
- 评估骨髓瘤微环境 (TME) 在预测治疗结果中的作用.
- 在BM TME中识别关键基因,以预后能力.
主要方法:
- 对CD138阴性BM分量的转录组分析.
- 生物信息解卷和一个定制的免疫-风暴签名矩阵.
- 弹性网对特征选择和模型开发 (MM-5C) 的Cox回归进行了惩罚.
主要成果:
- 包含SOX11,METTL11B,C3,RBM10和HOMEZ的MM-5C模型准确地对MM患者进行了分层.
- 该模型表明预后独立于已建立的临床和细胞遗传标记.
- 鉴定的基因表达模式反映了MM TME在疾病进展中的作用.
结论:
- MM-5C模型为个性化的MM风险分层提供了一个可扩展和临床翻译的工具.
- 整合TME洞察力对于改善MM预后至关重要.
- 这种方法支持MM管理中的微环境知情治疗决策.
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