Metastasis Progression Through the Interplay Between the Immune System and Epithelial-mesenchymal-transition in Circulating Breast Tumor Cells

Abstract BackgroundCirculating tumor cells (CTCs) are the critical initiators of distant metastasis formation. In which, the reciprocal interplay among different metastatic pathways which promote survival of CTCs, is not well introduced, using network approaches. CTC cells include single and cluster cells, in which cluster cells revealed 23-50 fold more metastatic potentials.Here, to investigate the unknown pathways of single/cluster CTCs, the co-expression network 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 metastasis-free survival. Finally, a predictive model was developed for metastasis risk of patients, using VIF-stepwise feature selection. ResultsOur 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 single/cluster CTCs. The SVM model indicated a high metastatic potential of EMT subnetwork (accuracy, sensitivity, and specificity scores were 87%). The distant-metastasis-free-survival model was identified to predict patients’ metastasis risks. (c-index=0.8). Finally, novel metastatic biomarkers including PTCRA, F13A1, ICAM2, and SNRPC were detected in breast cancer.Conclusions In conclusion, the reciprocal interplay among critical pathways in CTCs enhances their survival and metastatic potentials. Such findings may help to develop more precise predictive metastatic-risk models or detect novel biomarkers.