ArticleScientific reports2022
Pivotal models and biomarkers related to the prognosis of breast cancer based on the immune cell interaction network.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 3 citations in OpenAlex.
- Discovery of Breast Cancer Diagnostic Markers Using Lipase-Based Plasma Proteomics of Lipid-Bound Proteins.Journal of mass spectrometry : JMS · 2026Article
- Unraveling the breast cancer tumor microenvironment: crucial factors influencing natural killer cell function and therapeutic strategies.International journal of biological sciences · 2025Review
- Interferon-Gamma Secretion Is Significantly Decreased in Stage III Breast Cancer Patients.International journal of molecular sciences · 2024Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The effect of breast cancer heterogeneity on prognosis of patients is still unclear, especially the role of immune cells in prognosis of breast cancer. In this study, single cell transcriptome sequencing data of breast cancer were used to analyze the relationship between breast cancer heterogeneity and prognosis. In this study, 14 cell clusters were identified in two single-cell datasets (GSE75688 and G118389). Proportion analysis of immune cells showed that NK cells were significantly aggregated in triple negative breast cancer, and the proportion of macrophages was significantly increased in primary breast cancer, while B cells, T cells, and neutrophils may be involved in the metastasis of breast cancer. 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, which can be used as prognostic biomarkers.
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