Single-cell Atlas reveals core function of CPVL/MSR1 expressing macrophages in the prognosis of triple-negative breast cancer

Abstract

Background: Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, with the worst prognosis among all subtypes. The impact of distinct cell subpopulations within the tumor microenvironment (TME) on TNBC patient prognosis has yet to be clarified.

Methods: Utilizing single-cell RNA sequencing (scRNA-seq) integrated with bulk RNA sequencing (bulk RNA-seq), we applied Cox regression models to compute hazard ratios, and cross-validated prognostic scoring using a GLMNET-based Cox model. Cell communication analysis was used to elucidate the potential mechanisms of CPVL and MSR1. Ultimately, RNA interference-mediated gene knockdown was utilized to validate the impact of specific genes on the polarization of tumor-associated macrophages (TAMs).

Results: Our findings revealed that the function of immune cells is more pivotal in prognosis, with TAMs showing the strongest correlation with TNBC patient outcomes, compared with other immune cells. Additionally, we identified CPVL and MSR1 as critical prognostic genes within TAMs, with CPVL expression positively correlated with favorable outcomes and MSR1 expression associated with poorer prognosis. Mechanistically, CPVL may contribute to favorable prognosis by inhibiting the SPP1-CD44 ligand-receptor and promoting CXCL9-CXCR3, C3-C3AR1 ligand-receptor, through which TAMs interact with other cells such as monocytes, neutrophils, and T cells. Moreover, cytokines including IL-18, IFNγR1, CCL20, and CCL2, along with complement-related gene like TREM2 and complement component CFD, may participate in the process of CPVL or MSR1 regulating macrophage polarization. Furthermore, RT-PCR experiments confirmed that CPVL is positively associated with M1-like TAM polarization, while MSR1 is linked to M2-like TAM polarization. Finally, the prognostic significance of these two genes is also validated in HER2-positive breast cancer subtypes.

Conclusions: CPVL and MSR1 are potential biomarkers for macrophage-mediated TNBC prognosis, suggesting the therapeutic potential of macrophage targeting in TNBC.

Keywords: MSR1; cPVL; macrophages; prognosis; single-cell sequence; triple-negative breast cancer.

Figure 1

Cell compartment of the tumor microenvironment in TNBC patients. Single-cell RNA sequencing (scRNA-seq) data from seven GEO datasets were integrated, covering 66 tissue samples (batch effects were removed using the “Seurat” package in R). (A) UMAP was used to separate the cell clusters. (B) Manual annotation was performed based on the expression characteristics of marker genes for the five cell cluster. (C) UMAP visualization of single-cell transcriptomes from 66 TNBC samples, showing the separation of major cell lineages. (D) A bubble plot displaying the expression levels of marker genes across different cell types. (E) Identification of dominant genes in each major cell type based on scRNA-seq data (fold-change > 3 compared to other cell types; adjusted P-value < 0.05) (left). These genes were mapped onto a bulk RNA-seq dataset from 230 TNBC patients (middle), showing their relative expression patterns, and pairwise correlations between the same genes (right). (F) Comparison of the prognostic impact of tumor-, stromal-, and immune-related genes. Hazard ratios for each feature were obtained using a multivariate Cox regression model (wald 95% confidence intervals and P-values are shown as horizontal bars), with cross-validated prognostic scores calculated using a GLMNET-based Cox model.

Figure 2

CPVL and MSR1 in macrophages are genes significantly associated with the prognosis of TNBC patients. (A) UMAP plot of immune cells, showing further subclustering of the cell populations. (B) Bubble plot illustrating the expression patterns of marker genes in the different immune cell subpopulations. (C) Repeated analysis from the image (A), highlighting the impact of different immune cell subpopulations on prognosis. (D) Cross-validated macrophage prognostic scores from the image (C) were used to generate Kaplan-Meier curves, showing genes with opposite effects on prognosis at the 50% cutoff. (E) Display of the primary expression situation of CPVL and MSR1 in macrophages. (F) The degree of macrophage infiltration in patients’ tumors was first calculated using “CIBERSORT”, followed by the application of a univariate Cox model to assess the impact of macrophage infiltration and the CPVL/MSR1 ratio (CM ratio) on patient prognosis. (G) A multivariate Cox model was employed to assess the impact of M2 macrophage infiltration and CM ratio on patient prognosis.

Figure 3

The coordinated relationship between CPVL/MSR1 and immune response in TNBC. (A) The scatter plot shows a lack of correlation between patient prognosis and common M1 and M2 markers, while there is a significant correlation with the individual expression of CPVL, MSR1, and the CM ratio. Spearman’s rank correlation was used, and a fitted blue line was shown when significant. (B) Cell counts of major cell types are displayed, and Spearman’s rank correlation analysis was used to assess correlations with CM ratio. (C) The correlation between the abundance of tumor cells, endothelial cells, mast cells, and B cells with common M1 and M2 markers, CPVL, MSR1, and CM ratio. Spearman’s rank correlation was used. *Indicates that the P value is less than 0.05. **Indicates that the P value is less than 0.01.

Figure 4

CPVL and MSR1 are involved in regulating signaling between macrophages and other cells. (A) A bubble plot displaying the expression of MSR1 and CPVL across the four macrophage subpopulations and other cell types. (B) CellChat analysis showing the cell-cell communication between the four macrophage subpopulations and other cells. (C, D) Dot plots illustrating ligand-receptor interactions between the four macrophage subpopulations and other cells.

Figure 5

Regulatory effects of CPVL and MSR1 on expression of cytokines, complement, and pathways. (A) The correlation between the expression of cytokines and MSR1 or CPVL in monocytes and macrophages. (B) The correlation between the expression of complement-related and MSR1 or CPVL in monocytes and macrophages. (C) The correlation between the HALLMARK pathway scores and MSR1 or CPVL in monocytes and macrophages.

Figure 6

Effect of CPVL and MRS1 on macrophage polarization. PCR was performed for the target genes and cell marker to observe mRNA levels in macrophages with different polarization states, and the values were subjected to T-tests for statistical significance. (All data were expressed as mean ± SEM; n = 3, and each set of experiments was repeated three times). (A, B) CPVL and MSR1 expression in different polarization states. (C-E) CPVL, CD86, CD163 expression in M0 macrophages, cells were treated with PMA then transfected with siRNAs targeting CPVL. (F, G) CD86, CD163 expression in M1-polarized macrophages. *Indicates that the P value is less than 0.05. **Indicates that the P value is less than 0.01. ***Indicates that the P value is less than 0.001.

Figure 7

The extent to which CM ratio affects prognosis in other intrinsic molecular subtypes of breast cancer (A-C) A univariate Cox model was used to evaluate the impact of CM ratio, TNM stage, and macrophage infiltration on patient prognosis across other intrinsic molecular subtypes of breast cancer.