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Research Article
Anhao Wu
The Third Affiliated Hospital of Kunming Medical University: Yunnan Cancer Hospital
Corresponding Author
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Background: The effect of breast cancer heterogeneity on prognosis of patients is still unclear, especially the role of immune cells in prognosis of breast cancer. Therefore, the discovery of new markers to assess the effect of breast cancer heterogeneity on patient prognosis is crucial to improve prognosis and survival of patients.
Methods: Single cell transcriptome sequencing data of breast cancer were downloaded from GEO database. PCA and UMAP were used for dimensionality reduction analysis and cell clustering. Find All Markers function was used to calculate differential genes in each cluster, and Do Heatmap function was used to plot the distribution of differential genes in each clusters. CellPhoneDB was used to analyze ligand-receptor interactions. TRRUST database combined with Cytoscape were used to construct a receptor-ligand-transcription factor interaction network. WGCNA is used to analyze pivotal modules associated with breast cancer prognosis. Univariate regression analysis and KM survival analysis were used to identify prognostic genes in prognostic modules. Multivariate regression analysis combined with risk scoring were used to construct a breast prognosis model, which was verified by TCGA and ICGC sample data.
Result: In this study, 14 cell clusters were identified in two single-cell datasets (GSE75688 and G118389). The results of ligand receptor interaction network revealed that macrophages and DC cells were the most frequently interacting cells with other cells in breast cancer. The results of WGCNA analysis suggested that the MEblue module is most relevant to the overall survival time of triple-negative breast cancer. Twenty-four prognostic genes in the blue module were identified by univariate Cox regression analysis and KM survival analysis. Multivariate regression analysis combined with risk analysis was used to analyze 24 prognostic genes to construct a prognostic model. The verification result of our prognostic model showed that there were significant differences in the expression of PCDH12, SLIT3, ACVRL1, and DLL4 genes between the high-risk group and the low-risk group.
Conclusion: PCDH12, SLIT3, ACVRL1 and DLL4 are prognostic biomarkers and relate to the type and proportion of immune cells in breast cancer.
Keywords
breast cancer, tumor heterogeneity, single cell sequencing, disease-related modules, prognostic markers
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This preprint is under consideration at Journal of Translational Medicine. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Anhao Wu
The Third Affiliated Hospital of Kunming Medical University: Yunnan Cancer Hospital
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 20 Apr, 2021
No community comments so far
Reviews received
Received 20 Apr, 2021
Reviewers invited
Invitations sent on 17 Apr, 2021
First submitted
On 13 Apr, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Translational Medicine
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: The effect of breast cancer heterogeneity on prognosis of patients is still unclear, especially the role of immune cells in prognosis of breast cancer. Therefore, the discovery of new markers to assess the effect of breast cancer heterogeneity on patient prognosis is crucial to improve prognosis and survival of patients.
Methods: Single cell transcriptome sequencing data of breast cancer were downloaded from GEO database. PCA and UMAP were used for dimensionality reduction analysis and cell clustering. Find All Markers function was used to calculate differential genes in each cluster, and Do Heatmap function was used to plot the distribution of differential genes in each clusters. CellPhoneDB was used to analyze ligand-receptor interactions. TRRUST database combined with Cytoscape were used to construct a receptor-ligand-transcription factor interaction network. WGCNA is used to analyze pivotal modules associated with breast cancer prognosis. Univariate regression analysis and KM survival analysis were used to identify prognostic genes in prognostic modules. Multivariate regression analysis combined with risk scoring were used to construct a breast prognosis model, which was verified by TCGA and ICGC sample data.
Result: In this study, 14 cell clusters were identified in two single-cell datasets (GSE75688 and G118389). The results of ligand receptor interaction network revealed that macrophages and DC cells were the most frequently interacting cells with other cells in breast cancer. The results of WGCNA analysis suggested that the MEblue module is most relevant to the overall survival time of triple-negative breast cancer. Twenty-four prognostic genes in the blue module were identified by univariate Cox regression analysis and KM survival analysis. Multivariate regression analysis combined with risk analysis was used to analyze 24 prognostic genes to construct a prognostic model. The verification result of our prognostic model showed that there were significant differences in the expression of PCDH12, SLIT3, ACVRL1, and DLL4 genes between the high-risk group and the low-risk group.
Conclusion: PCDH12, SLIT3, ACVRL1 and DLL4 are prognostic biomarkers and relate to the type and proportion of immune cells in breast cancer.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
This is a list of supplementary files associated with this preprint. Click to download.
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