Laboratory of Systems Biology and Bioinformatics (LBB), Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran.
Chemical Injuries Research Center, Systems Biology and Poisonings Institute, Tehran, Iran.
BMC Med Genomics. 2021 Nov 20;14(1):273. doi: 10.1186/s12920-021-01112-9.
Circulating tumor cells (CTCs) are the critical initiators of distant metastasis formation. In which, the reciprocal interplay among different metastatic pathways and their metastasis driver genes which promote survival of CTCs is not well introduced using network approaches.
Here, to investigate the unknown pathways of single/cluster CTCs, the co-expression network was reconstructed, using WGCNA (Weighted Correlation Network Analysis) method. Having used the hierarchical clustering, we detected the Immune-response and EMT subnetworks. The metastatic potential of genes was assessed and validated through the support vector machine (SVM), neural network, and decision tree methods on two external datasets. To identify the active signaling pathways in CTCs, we reconstructed a casual network. The Log-Rank test and Kaplan-Meier curve were applied to detect prognostic gene signatures for distant metastasis-free survival (DMFS). Finally, a predictive model was developed for metastasis risk of patients using VIF-stepwise feature selection.
Our results showed the crosstalk among EMT, the immune system, menstrual cycles, and the stemness pathway in CTCs. In which, fluctuation of menstrual cycles is a new detected pathway in breast cancer CTCs. The reciprocal association between immune responses and EMT was identified in CTCs. The SVM model indicated a high metastatic potential of EMT subnetwork (accuracy, sensitivity, and specificity scores were 87%). The DMFS model was identified to predict patients' metastasis risks. (c-index = 0.7). Finally, novel metastatic biomarkers of KRT18 and KRT19 were detected in breast cancer CTCs.
In conclusion, the reciprocal interplay among critical unknown pathways in CTCs manifests both their survival in blood and metastatic potentials. Such findings may help to develop more precise predictive metastatic-risk models or detect pivotal metastatic biomarkers.
循环肿瘤细胞（CTC）是远处转移形成的关键启动子。在网络方法中，不同转移途径及其促进 CTC 存活的转移驱动基因之间的相互作用尚未得到很好的介绍。
为了研究单个/簇 CTC 的未知途径，使用 WGCNA（加权相关网络分析）方法重建了共表达网络。通过层次聚类，我们检测到了免疫反应和 EMT 子网络。通过支持向量机（SVM）、神经网络和决策树方法，在两个外部数据集上评估和验证了基因的转移潜力。为了确定 CTC 中活跃的信号通路，我们重建了一个因果网络。对数秩检验和 Kaplan-Meier 曲线用于检测无远处转移生存（DMFS）的预后基因特征。最后，使用 VIF-逐步特征选择为患者开发了转移风险预测模型。
我们的结果显示了 EMT、免疫系统、月经周期和干细胞途径在 CTC 中的相互作用。其中，月经周期的波动是乳腺癌 CTC 中一个新检测到的途径。在 CTC 中检测到免疫反应和 EMT 之间的相互关联。SVM 模型表明 EMT 子网络具有较高的转移潜力（准确性、敏感性和特异性评分为 87%）。DMFS 模型被确定为预测患者转移风险的模型。（c 指数=0.7）。最后，在乳腺癌 CTC 中检测到了 KRT18 和 KRT19 的新型转移生物标志物。
总之，CTC 中关键未知途径的相互作用既表现了它们在血液中的存活能力，也表现了它们的转移潜力。这些发现可能有助于开发更精确的预测转移风险模型或检测关键的转移生物标志物。
