基于细胞焦亡基因构建子宫内膜癌预后模型及敏感药物筛选
目的本研究利用机器学习构建子宫内膜癌（UCEC）细胞焦亡相关的预后模型及筛选敏感药物。方法通过TCGA数据库获取511例UCEC临床样本的基因表达谱，随机将UCEC样本分为训练集和验证集。使用ssGSEA算法计算UCEC患者的细胞焦亡评分，基于该评分将患者分为High-Pyroptosis组和Low-Pyroptosis组。应用limma包识别High-Pyroptosis组和Low-Pyroptosis组中的差异表达基因。应用单因素Cox回归和机器学习-迭代LASSO回归构建UCEC最优细胞焦亡评分相关Signature。最后，使用GDSC数据库基于该Signature筛选了UCEC患者敏感...
I tiakina i:
| Ngā kaituhi matua: | , , |
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| Hōputu: | Artigo |
| Reo: | Chinês |
| I whakaputaina: |
Editorial Office of Chinese Journal of Laboratory Diagnosis
2025-07-01
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| Rangatū: | Zhongguo shiyan zhenduanxue |
| Ngā marau: | |
| Urunga tuihono: | http://ZSZD.publish.founderss.cn/thesisDetails#10.3969/j.issn.1007-4287.2025.07.016 |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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