基于使用机器学习的巨细胞两极分化相关基因开发骨髓瘤的预后模型:对个性化治疗的含义
Jin Zeng1, Dong Wang1, ZhaoChen Tong1
1Department of Spine Surgery, The Third Xiangya Hospital of Central South University, 138 Tongzipo Rd, Changsha, 410013, Hunan, China.
Clinical and experimental medicine
|May 9, 2025
概括
这项研究确定了调节骨髓瘤中巨细胞两极分化的关键基因,开发了一个指导免疫治疗的预后模型. 抑制BNIP3抑制瘤生长,促进M2巨细胞的两极分化,提供了一个潜在的治疗标.
科学领域:
- 在瘤学瘤学.
- 免疫学 免疫学 免疫学
- 遗传学 是一个遗传学.
背景情况:
- 骨髓瘤治疗,包括化疗和手术,对转移和复发病例的疗效有限.
- 免疫疗法是一个有希望的替代方案,但瘤相关巨细胞 (TAM) 现象型在骨髓瘤预后中的作用尚不清楚.
研究的目的:
- 为了确定调节巨细胞两极分化在骨髓瘤中的基因.
- 开发一个预后模型来预测长期生存.
- 探索骨髓瘤的潜在免疫治疗点.
主要方法:
- 使用Monocle包装进行差异表达基因分析.
- 权重基因共同表达网络分析 (WGCNA) 和最小绝对收缩和选择操作员 (LASSO) 回归.
- 机器学习算法,lentiviral传染,CCK-8,transwell迁移,西部斑块和流动细胞计.
主要成果:
- 确定了141个调节巨分极的基因;选择了8个用于预后模型构建.
- 高风险和低风险组在免疫细胞激活,信号通路和免疫功能方面表现出显著差异.
- 降低了骨髓瘤细胞的增殖/迁移,并促进了M2巨细胞的两极分化.
结论:
- 开发的预后模型为骨髓瘤提供了精确的免疫治疗策略.
- 确定BNIP3是骨髓瘤和其他癌症的潜在免疫治疗标.
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