Involvement of immune system and Epithelial–Mesenchymal-Transition in increased invasiveness of clustered circulatory tumor cells in breast cancer

Abstract

Background: 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. Methods: 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. Results: 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. Conclusions: 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.