Liu Dingyun, Bai Jun, Chen Qian, Tan Renbo, An Zheng, Xiao Jun, Qu Yingwei, Xu Ying
Center for Cancer Systems Biology, China-Japan Union Hospital of Jilin University, Changchun, China.
College of Computer Science and Technology, Jilin University, Changchun, China.
Front Oncol. 2022 Sep 28;12:1003715. doi: 10.3389/fonc.2022.1003715. eCollection 2022.
Brain metastasis of a cancer is a malignant disease with high mortality, but the cause and the molecular mechanism remain largely unknown. Using the samples of primary tumors of 22 cancer types in the TCGA database, we have performed a computational study of their transcriptomic data to investigate the drivers of brain metastases at the basic physics and chemistry level. Our main discoveries are: (i) the physical characteristics, namely electric charge, molecular weight, and the hydrophobicity of the extracellular structures of the expressed transmembrane proteins largely affect a primary cancer cell's ability to cross the blood-brain barrier; and (ii) brain metastasis may require specific functions provided by the activated enzymes in the metastasizing primary cancer cells for survival in the brain micro-environment. Both predictions are supported by published experimental studies. Based on these findings, we have built a classifier to predict if a given primary cancer may have brain metastasis, achieving the accuracy level at = 0.92 on large test sets.
癌症脑转移是一种死亡率很高的恶性疾病,但其病因和分子机制在很大程度上仍不清楚。利用TCGA数据库中22种癌症类型的原发性肿瘤样本,我们对其转录组数据进行了计算研究,以在基础物理和化学层面探究脑转移的驱动因素。我们的主要发现是:(i)表达的跨膜蛋白细胞外结构的物理特性,即电荷、分子量和疏水性,在很大程度上影响原发性癌细胞穿越血脑屏障的能力;(ii)脑转移可能需要转移的原发性癌细胞中激活的酶提供特定功能,以便在脑微环境中存活。这两个预测都得到了已发表的实验研究的支持。基于这些发现,我们构建了一个分类器来预测给定的原发性癌症是否可能发生脑转移,在大型测试集上达到了准确率=0.92的水平。