Metastasis Progression Through the Interplay Between the Immune System and Epithelial-mesenchymal-transition in Circulating Breast Tumor Cells
Background
Circulating 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.
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 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.
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Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.
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Table S1. midnightblue (immune) subnetwork. The list of genes, module membership, the logarithm of fold change, differential analysis adj-p.value, and gene-significance statistics.
Table S2. turuise (EMT) subnetwork. The list of genes, module membership, the logarithm of fold change, differential analysis adj-p.value, and gene-significance statistics.
Table S3. The preservation statistics. This table includes Z_(summary ) and 〖Median〗_rank statistics.
Table S4. Biological pathways in the directed network. We categorized genes of the detected directed network, using ClueGO plugin in Cytoscape. The Biological pathways and p-values were reported in Table S4.
Table S5. the EMT subnetwork cox-PH results. The cox-PH analysis was implemented for the EMT subnetwork genes. The selected genes, coefficients, and p-values were reported in Table S5.
Table S6. the Immune subnetwork cox-PH results The cox-PH analysis was implemented for the immune subnetwork genes. The selected genes, coefficients, and p-values were reported in Table S6.
Table S7. EMT VIF values. To investigate multicollinearity in the cox-PH model, we calculated the Variance Inflation Factor (VIF). The VIF < 10 indicates no multicollinearity. The VIF of immune genes was reported in Table S7.
Table S8. immune VIF values. To investigate multicollinearity in the cox-PH model, we calculated the Variance Inflation Factor (VIF). The VIF < 10 indicates no multicollinearity. The VIF of immune genes was reported in Table S8.
Figure s1. subnetwork-trait correlation p-values. The metastasis-related subnetwork was selected, using subnetwork/trait correlation. (|correlation |> 0.5)
Figure S2. The Schoenfeld residuals for EMT genes. The proportional hazard ratio investigated, using the Schoenfeld residuals. The residuals (red dots) must be between the curves.
Figure S3. The Schoenfeld residuals for immune genes. The proportional hazard ratio investigated, using the Schoenfeld residuals. The residuals (red dots) must be between the curves.
Posted 21 Sep, 2020
On 23 Nov, 2020
Received 07 Nov, 2020
Received 07 Nov, 2020
On 29 Oct, 2020
On 28 Oct, 2020
Received 28 Oct, 2020
On 07 Oct, 2020
Invitations sent on 03 Oct, 2020
On 24 Sep, 2020
On 18 Sep, 2020
On 18 Sep, 2020
On 10 Sep, 2020
Metastasis Progression Through the Interplay Between the Immune System and Epithelial-mesenchymal-transition in Circulating Breast Tumor Cells
Posted 21 Sep, 2020
On 23 Nov, 2020
Received 07 Nov, 2020
Received 07 Nov, 2020
On 29 Oct, 2020
On 28 Oct, 2020
Received 28 Oct, 2020
On 07 Oct, 2020
Invitations sent on 03 Oct, 2020
On 24 Sep, 2020
On 18 Sep, 2020
On 18 Sep, 2020
On 10 Sep, 2020
Background
Circulating 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.
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 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.
Figure 1
Figure 2
Figure 3
Figure 4
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.