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在甲状腺癌中预测TNFRSF9表达和分子病理特征,使用机器学习来构建Pathomics模型

Ying Liu^1,2,3, Junping Zhang¹, Shanshan Li¹

¹Department of Endocrine and Metabolism, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.

|May 16, 2024

概括

这项研究开发了一种渐变增强机 (GBM) 病理学模型,用于预测甲状腺癌 (THCA) 中的TNFRSF9表达. 该模型准确预测TNFRSF9水平,并确定预后因素,有助于THCA理解.

科学领域:

在瘤学瘤学.
基因组学就是基因组学.
计算病理学计算病理学

背景情况:

瘤坏死因子受体超级家族9号成员 (TNFRSF9) 在甲状腺癌 (THCA) 发病过程中发挥着关键作用.
预测TNFRSF9的表达和理解其分子机制对于THCA治疗策略至关重要.

研究的目的:

开发和验证一种Pathomics模型,用于预测THCA中的TNFRSF9表达.
探索THCA中TNFRSF9表达的分子机制和预后影响.

主要方法:

利用了癌症基因组图谱 (TCGA) 数据,包括转录组,病理图像和临床信息.
采用图像细分 (OTSU的算法) 和特征提取 (pyradiomics) 来进行Pathomics分析.
开发了一种带有特征选择 (mRMR_RFE) 的梯度增强机 (GBM) 模型,以预测TNFRSF9表达和评估预后值.

主要成果:

高TNFRSF9表达与较差的无进展间隔 (PFI) 相关,并作为一个独立的风险因素.
GBM Pathomics 模型实现了良好的预测效率 (AUC 0.819,0.769) 并确定了九个关键的病理学特征.
较高的概率病理学分数 (PS) 与风险增加,丰富的途径,更高的TIGIT表达,Tregs透和更多的基因突变相关.

结论:

一个GBM Pathomics模型有效地预测THCA中的TNFRSF9表达水平,使用H&E染色的基因病理特征.
该模型为TNFRSF9在甲状腺癌中的分子机制和预后意义提供了宝贵的见解.

关键词:

在TNFRSF9中.机器学习机器学习病理学是一种病理学.甲状腺癌是一种甲状腺癌.

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