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通过使用机器学习方法,识别组织和血液中白细胞的转录特征,用于多种癌症的诊断

QingLan Ma¹, JingXin Ren¹, Lei Chen²

¹School of Life Sciences, Shanghai University, Shanghai 200444, China.

Cancer genetics

|January 10, 2026

概括

机器学习在四种癌症类型的免疫细胞 (T细胞和白细胞) 中确定了关键基因. 这些转录签名为瘤微环境和潜在的免疫治疗点提供了洞察力.

科学领域:

癌症免疫学癌症免疫学
计算生物学计算生物学
文字转录学 (Transcriptomics) 是一个学科.

背景情况:

了解免疫细胞在瘤微环境中的作用对于癌症免疫疗法至关重要.
免疫细胞的转录特征可以揭示它们在各种癌症类型中的功能和影响.

研究的目的:

通过机器学习在不同类型的癌症中识别与免疫细胞功能相关的关键基因.
根据免疫细胞的转录形状,开发免疫细胞的分类器和分类规则.

主要方法:

分析来自T细胞和CD45+CD3-白细胞的RNA-seq数据,通过瘤,相邻的正常组织和外周血液样本.
应用Boruta方法用于数据集缩小和最大相关性/最小冗余性用于基因排名.
使用增量特征选择 (IFS) 与决策树或随机森林用于基因识别和分类.

主要成果:

确定了不同的基因表达模式,包括瘤透T细胞中的CST7下调和CD2差异表达.
在T细胞中发现LCP1,CD27和MAL作为关键基因,在CD45+CD3-白细胞中发现IFI30,CXCR4和FOSB.
通过现有文献验证已识别的基因,支持它们在抗瘤过程中的作用.

结论:

这项研究揭示了各种癌症类型中免疫细胞的关键转录特征.
这些发现为开发癌症免疫学的新诊断,预后和治疗策略提供了基础.

关键词:

癌症癌症癌症癌症白血球是白血球的细胞.机器学习机器学习转录的签名转录的签名

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