Tumor Nonimmune-Microenvironment-Related Gene Expression Signature Predicts Brain Metastasis in Lung Adenocarcinoma Patients after Surgery: A Machine Learning Approach Using Gene Expression Profiling.
Tumor Nonimmune-Microenvironment-Related Gene Expression Signature Predicts Brain Metastasis in Lung Adenocarcinoma Patients after Surgery: A Machine Learning Approach Using Gene Expression Profiling.
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DOI：
10.3390/cancers13174468
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发表时间：
2021-09-05
期刊：
影响因子：
5.2
通讯作者：
Koh YW
中科院分区：
文献类型：
--
作者：
Haam S;Han JH;Lee HW;Koh YW
能够预测肺腺癌患者的脑转移非常重要；然而，这方面的研究仍然缺乏。先前关于肺腺癌脑转移肿瘤微环境的大部分工作都涉及肿瘤免疫微环境。肿瘤非免疫微环境（细胞外基质（ECM）、上皮间质转化（EMT）特征和血管生成）在脑转移中的重要性被忽视。我们评估了肿瘤非免疫微环境相关的基因表达特征，这些特征可以使用机器学习方法预测肺腺癌手术切除后的脑转移。我们鉴定了肿瘤非免疫微环境相关的 17 个基因表达特征，该特征在四个机器学习分类器中显示出较高的脑转移预测能力。 17 个基因表达特征中前三个基因的免疫组织化学表达产生了与 NanoString 测试相似的结果。我们的肿瘤非免疫微环境相关基因表达特征是重要的生物标志物，可以预测脑转移并提供患者特异性的治疗选择。使用具有基因表达谱的机器学习方法，我们发现了肿瘤非免疫微环境相关的基因表达特征，包括细胞外基质 (ECM) 重塑、上皮-间质转化 (EMT) 和血管生成，可以预测 64 例肺腺癌 (LUAD) 手术切除后的脑转移 (BM)。基因表达谱鉴定了与 BM 显着相关的肿瘤非免疫微环境相关的 17 个基因表达特征。在这 17 个基因中，11 个是 ECM 重塑相关基因。 17 个基因表达特征在四个机器学习分类器中显示出较高的 BM 预测能力（接受者操作特征曲线下面积 = 朴素贝叶斯为 0.845，支持向量机为 0.849，随机森林为 0.858，神经网络为 0.839）。亚组分析显示，早期 LUAD 中 17 个基因特征的 BM 预测能力高于晚期 LUAD。通路富集分析表明，上调的差异表达基因主要富集在ECM-受体相互作用通路中。 17 个基因表达特征中前三个基因的免疫组织化学表达产生了与 NanoString 测试相似的结果。本研究中发现的肿瘤非免疫微环境相关基因表达特征是重要的生物标志物，可以预测 BM 并提供患者特异性的治疗选择。
It is important to be able to predict brain metastasis in lung adenocarcinoma patients; however, research in this area is still lacking. Much of the previous work on tumor microenvironments in lung adenocarcinoma with brain metastasis concerns the tumor immune microenvironment. The importance of the tumor nonimmune microenvironment (extracellular matrix (ECM), epithelial–mesenchymal transition (EMT) feature, and angiogenesis) has been overlooked with regard to brain metastasis. We evaluated tumor nonimmune-microenvironment-related gene expression signatures that could predict brain metastasis after the surgical resection of lung adenocarcinoma using a machine learning approach. We identified a tumor nonimmune-microenvironment-related 17-gene expression signature, and this signature showed high brain metastasis predictive power in four machine learning classifiers. The immunohistochemical expression of the top three genes of the 17-gene expression signature yielded similar results to NanoString tests. Our tumor nonimmune-microenvironment-related gene expression signatures are important biological markers that can predict brain metastasis and provide patient-specific treatment options. Using a machine learning approach with a gene expression profile, we discovered a tumor nonimmune-microenvironment-related gene expression signature, including extracellular matrix (ECM) remodeling, epithelial–mesenchymal transition (EMT), and angiogenesis, that could predict brain metastasis (BM) after the surgical resection of 64 lung adenocarcinomas (LUAD). Gene expression profiling identified a tumor nonimmune-microenvironment-related 17-gene expression signature that significantly correlated with BM. Of the 17 genes, 11 were ECM-remodeling-related genes. The 17-gene expression signature showed high BM predictive power in four machine learning classifiers (areas under the receiver operating characteristic curve = 0.845 for naïve Bayes, 0.849 for support vector machine, 0.858 for random forest, and 0.839 for neural network). Subgroup analysis revealed that the BM predictive power of the 17-gene signature was higher in the early-stage LUAD than in the late-stage LUAD. Pathway enrichment analysis showed that the upregulated differentially expressed genes were mainly enriched in the ECM–receptor interaction pathway. The immunohistochemical expression of the top three genes of the 17-gene expression signature yielded similar results to NanoString tests. The tumor nonimmune-microenvironment-related gene expression signatures found in this study are important biological markers that can predict BM and provide patient-specific treatment options.
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影响因子：
3.3
作者：
Anwer, Mehwish;Bolkvadze, Tamuna;Pitkanen, Asla
通讯作者：
Pitkanen, Asla
影响因子：
11.5
作者：
Besse, Benjamin;Le Moulec, Sylvestre;Soria, Jean-Charles
通讯作者：
Soria, Jean-Charles
影响因子：
4.1
作者：
de Oliveira-Barros, Eliane Gouvea;Branco, Luiza Castello;Nasciutti, Luiz Eurico
通讯作者：
Nasciutti, Luiz Eurico
DOI：
10.1093/bioinformatics/btp101
发表时间：
2009-04-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Bindea G;Mlecnik B;Hackl H;Charoentong P;Tosolini M;Kirilovsky A;Fridman WH;Pagès F;Trajanoski Z;Galon J
通讯作者：
Galon J
影响因子：
3
作者：
Dalman MR;Deeter A;Nimishakavi G;Duan ZH
通讯作者：
Duan ZH