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应用机器学习算法来开发基于与脂肪酸代谢相关的基因的肺腺癌生存预测模型

Dan Cong¹, Yanan Zhao¹, Wenlong Zhang¹

¹Department of Oncology and Hematology, China-Japan Union Hospital of Jilin University, Changchun, China.

Frontiers in pharmacology

|November 3, 2023

概括

这项研究表明,异常的脂肪酸代谢 (FAM) 与肺腺癌 (LUAD) 的进展有关. 使用FAM相关基因的新型风险评分模型可以预测LUAD的预后,并指导向药物开发.

科学领域:

在瘤学瘤学.
分子生物学分子生物学
代谢过程中的代谢.

背景情况:

肺腺癌 (LUAD) 的进展可能涉及异常的脂肪酸代谢 (FAM).
了解FAM相关基因在LUAD中的预后作用对于开发向疗法至关重要.

研究的目的:

研究FAM相关基因与LUAD患者预后之间的关系.
开发基于FAM相关基因的LUAD预后预测模型.

主要方法:

使用了癌症基因组图谱 (TCGA) 的LUAD数据集.
采用基因组丰富分析,共识聚类和机器学习算法 (GBM,LASSO,XGBoost,SVM,随机森林,决策树) 来识别关键基因并开发风险评分模型.
评估免疫细胞透和预测药物敏感性.

主要成果:

确定了三种LUAD分子亚型 (C1,C2,C3) 具有不同的预后;C1显示最好,C3显示最差.
开发了一种12基因风险评分模型,其中高风险评分与较差的生存率和较低的免疫透率相关.
风险评分模型预测了特定抗癌药物对高风险评分患者的潜在益处.

结论:

一个基于FAM相关基因的新型风险评分模型有效预测LUAD预后.
该模型可以帮助识别可能受益于向治疗的患者,并为新抗癌药物的开发提供信息.

关键词:

风险评分风险评分脂肪酸代谢脂肪酸代谢肺部腺癌的癌症机器学习是机器学习.预后预后预后幸存的可能性生存的可能性.

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