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. 2022 Oct 31;14(21):5364.
doi: 10.3390/cancers14215364.

Transcriptomic Profiling of Breast Cancer Cells Induced by Tumor-Associated Macrophages Generates a Robust Prognostic Gene Signature

Meijun Long  1 , Jiajie Wang  2   3 , Mei Yang  2   3
Affiliations

Transcriptomic Profiling of Breast Cancer Cells Induced by Tumor-Associated Macrophages Generates a Robust Prognostic Gene Signature

Meijun Long et al. Cancers (Basel). .

Abstract

Breast cancer, one of the most prevalent neoplasms in the world, continues attracting worldwide attention. Macrophage, as the most abundant non-malignant cell in tumor, plays critical roles in both immune surveillance and tumorigenesis and has become a cell target of immunotherapy. Among all macrophages, tumor-associated macrophage (TAM) is regarded as the main force to promote tumorigenesis. To get an overall view of its impact on breast cancer, we employed a simplified and indirect coculturing cell model followed by RNA-sequencing to detect cancer cell's transcriptomic response induced by TAM and a prognostic gene signature was constructed based on it. Evidence from both cell models and clinical samples strengthened TAM's full-dimensional impact on breast cancer, involved in almost all known signal pathways dysregulated during tumorigenesis from transcription, translation and molecule transport to immune-related pathways. Consequently, the gene signature developed from these genes was tested to be powerful in prognostic prediction and associated with various clinical and biological features of breast cancer. Our study presented a more complete view of TAM's impact on breast cancer, which strengthened its role as an important therapy target. A 45-gene signature from the TAM-regulated genes was developed and shown potential in clinical application.

Keywords: TAM; breast cancer; gene signature; macrophage.

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Conflict of interest statement

The authors declare no conflict of interest. Additionally, the funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
Comparison of the transcriptomes induced by medium from MDMs or TAMs. (A) The PCA figure of the MCF7 transcriptomes. (B) Heatmap of DEGs in MCF7 transcriptomes induced by either MDMs or TAMs. (C,D) GO enrichment analysis of the upregulated and downregulated DEGs separately. The pathways shown here were most significantly enriched (p < 0.05, q < 0.01).
Figure 2
Figure 2
Development of the gene signature based on data in the TCGA-BRCA training cohort. (A) LASSO coefficient profiles of the DEGs. (B) Ten-time cross-validation for tuning parameter screening in the Lasso–Cox penalized regression model. (C) Univariate Cox regression analysis of the relation between the expression of the 45 signature genes and the overall survival in the TCGA-BRCA cohorts. HR, hazard ratio; CI, confidence interval.
Figure 3
Figure 3
Prognostic performance of the 45-gene signature. (A,C) Time-dependent ROC curves with AUCs at 1-, 3-, 5-, 7- and 10-year OS based on the gene signature in the TCGA-BRCA training (A) and validation (C) cohorts separately. (B,D) Kaplan–Meier plots of OS in subgroups with different risk scores in the training (B) and validation (D) cohorts separately. (E) Time-dependent ROC curves with AUCs at 1-, 3-, 5-, 7- and 10-year distant metastasis-free survival in MBC cohort. (F) Kaplan–Meier plots of distant metastasis-free survival in MBC cohort. (G) Time-dependent ROC curves with AUCs at 1-, 3-, 5-, 7- and 10-year disease-free survival in whole TCGA-BRCA cohort. (H) Kaplan–Meier plots of disease-free survival in whole TCGA-BRCA cohort.
Figure 4
Figure 4
Comparison of the transcriptomes with different risk scores in TCGA-BRCA cohorts. (A) Heatmap of top 200 DE transcripts between samples with 50 highest/lowest risk scores. (B–D) GSEA plots of commonly enriched GO pathways in the TCGA-BRCA cohort. The pathways shown here were top 10 most significantly enriched (p < 0.05).
Figure 5
Figure 5
Association between the risk score and clinical features. The difference in the AJCC pathologic T (A), N (B), M (C), stage (D) status, ER status by IHC (E), PR status by IHC (F) and HER2 status by IHC (G) between high- and low-risk score groups in the TCGA-BRCA cohort were compared by chi-square test.
Figure 6
Figure 6
Association between the risk score and biological features. The fraction genome altered (A), mutation count (B), TMB (C) and the estimated immune infiltrations (D) were compared between high- and low-risk score groups by Wilcoxon test. Only cell types differentially distributed between groups were shown in (D).
Figure 7
Figure 7
Association of the risk score with the sensitivity of CDK4/6 inhibitor. Linear association between risk score and IC50 of first line CDK4/6 inhibitors was demonstrated in dot plot, including Palbociclib (A,B) and Ribociclib (C).
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