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使用可解释的机器学习模型识别与瘤免疫治疗反应相关的microRNAs

Dong-Yeon Nam¹, Je-Keun Rhee²

¹Department of Bioinformatics & Life Science, Soongsil University, Seoul, Republic of Korea.

Scientific reports

|March 15, 2024

概括

预测免疫治疗反应至关重要. 这项研究表明,microRNAs (miRNAs) 可以预测患者对免疫检查点阻塞疗法的反应,识别关键miRNA生物标志物,以便更好地选择治疗.

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:

预测患者对瘤免疫疗法的反应,特别是免疫检查点阻塞 (ICB),对于优化治疗疗效和最大限度地减少不良影响至关重要.
目前用于预选可能受益于ICB治疗的患者的方法仍然是临床瘤学的重大挑战.

研究的目的:

研究微RNAs (miRNAs) 作为预测生物标志物的潜力,用于患者对瘤免疫检查点阻塞 (ICB) 治疗的反应.
开发和验证使用miRNA表达特征的机器学习模型,用于预测各种癌症类型的ICB治疗结果.

主要方法:

基于19种癌症类型的miRNA表达数据,构建随机森林模型来预测ICB治疗反应.
应用夏普利添加式扩展 (SHAP) 来解释模型预测并确定单个miRNAs的贡献.
分析被高度重要的miRNA准的途径,以了解它们在免疫反应中的作用.

主要成果:

使用一组有限的重要miRNAs的机器学习模型实现了与使用整个miRNA表达特征的模型相比的预测性能.
在SHAP分析中,确定了特定的miRNAs,这些miRNAs对于预测ICB反应至关重要.
这些关键miRNA所准的基因与与瘤相关的和与免疫相关的生物学途径有显著的关联.

结论:

微RNA表达数据对预测患者对瘤免疫疗法的反应具有重大潜力.
选择的信息性miRNAs可以作为可靠的生物标志物来评估免疫疗法的有效性,从而促进对潜在治疗机制的理解.

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lncRNA - Long Non-coding RNAs

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