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. 2023 Nov 25;23(1):294.
doi: 10.1186/s12935-023-03152-w.

Characterization of cancer-associated fibroblasts (CAFs) and development of a CAF-based risk model for triple-negative breast cancer

Ganggang Wang #  1 , Hao Zhang #  1 , Xiaowei Shen #  2 , Wenzhi Jin #  1 , Xiaoliang Wang  3 , Zhijie Zhou  4
Affiliations

Characterization of cancer-associated fibroblasts (CAFs) and development of a CAF-based risk model for triple-negative breast cancer

Ganggang Wang et al. Cancer Cell Int. .

Abstract

Triple-negative breast Cancer (TNBC) is a highly malignant cancer with unclear pathogenesis. Within the tumor microenvironment (TME), cancer-associated fibroblasts (CAFs) vitally influence tumor onset and progression. Thus, this research aimed to identify distinct subgroups of CAF using single-cell and TNBC-related information from the GEO and TCGA databases, respectively. The primary aim was to establish a novel predictive model based on the CAF features and their clinical relevance. Moreover, the CAFs were analyzed for their immune characteristics, response to immunotherapy, and sensitivity to different drugs. The developed predictive model demonstrated significant effectiveness in determining the prognosis of patients with TNBC, TME, and the immune landscape of the tumor. Of note, the expression of GPR34 was significantly higher in TNBC tissues compared to that in other breast cancer (non-TNBC) tissues, indicating that GPR34 plays a crucial role in the onset and progression of TNBC. In summary, this research has yielded a novel predictive model for TNBC that holds promise for the accurate prediction of prognosis and response to immunotherapy in patients with TNBC.

Keywords: Cancer-associated fibroblast; GPR34; Prognosis; Triple-negative breast cancer; Tumor microenvironment.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
The study flow chart
Fig. 2
Fig. 2
Detection of CAF cell clusters using the scRNA database of TNBC-affected individuals. A t-SNE plot showing the distribution of three samples; B distribution of seven CAF clusters; C expression profiles of the five leading marker genes in each of the seven clusters; D percentage and cell count of subgroups both in cancerous and nearby tissues; E KEGG enrichment analysis of seven fibroblast subgroups; F t-SNE plot displaying the malignant and non-malignant cell distribution in clustered cells
Fig. 3
Fig. 3
Attributes of cancer-associated pathways in clusters of CAF. A Heatmap displaying the scores of ten cancer-associated pathways in CAF cells; B comparative analysis between malignant cells and non-malignant cells in C CAF_0, D CAF_1, E CAF_2, F CAF_3, G CAF_4, H CAF_5, and I CAF_6 clusters based on the GSVA scores of each pathway. Wilcox test; *P < 0.05, **P < 0.01; ***P < 0.001; ****P < 0.0001; ns not significant
Fig. 4
Fig. 4
Links between the seven CAF clusters and the prognosis of patients with TNBC. A Comparative analysis of the seven CAF scores between cancerous and healthy tissues; B K–M curves analyzing the groups with high and low CAF scores in the seven clusters. ****P < 0.0001
Fig. 5
Fig. 5
Detection of hub predictive genes for developing the risk signature. A Volcano plot of DEGs between cancerous and healthy tissues in TCGA cohort; B volcano plot of prognosis-linked genes determined using the univariate Cox regression analysis; C trajectory of each independent variable with lambda, and plots of the coefficient distributions generated for the logarithmic (lambda) series used for parameter selection (lambda); D multivariate Cox coefficients for every risk signature gene. E, G K–M curves of the developed risk model based on the ten genes in the TCGA and GEO cohorts. F, H ROC curves of the developed risk model based on the ten genes in the TCGA and GEO cohorts
Fig. 6
Fig. 6
Responsiveness of risk score to PD-L1 blockade immunotherapy in the IMvigor210 cohort. A Prognostic differences between risk score groups in the IMvigor210 cohort. B Risk score differences in immunotherapy responses in the IMvigor210 cohort; C distribution of immunotherapy responses between risk score groups in the IMvigor210 cohort; D prognostic differences between risk score groups in early-stage patients in the IMvigor210 cohort; E prognostic differences between risk score groups in advanced-stage patients in the IMvigor210 cohort; F prognostic differences in risk score groups in the GSE78220 cohort; G risk score differences in immunotherapy responses in the GSE78220 cohort; H distribution of immunotherapy responses between risk score groups in the GSE78220 cohort. *P < 0.05; **P < 0.01
Fig. 7
Fig. 7
Immune landscape and molecular expression profiles of the two risk groups. A Differences in immune cell infiltration between the two risk groups; B stromal, immune, and ESTIMATE scores in the two risk groups; C expression of 47 ICMs in the two risk groups; D differences in metabolic and molecular subtypes in immunotherapy between the two risk groups
Fig. 8
Fig. 8
A Association of risk score with ESTIMATE, immune, and stromal scores and tumor purity. B Differences in ESTIMATE, immune, and stromal scores and tumor purity ratings between the two risk groups. C TIDE algorithm used to predict the probability of response to immune checkpoint inhibitor immunotherapy, categorizing patients into the two risk groups
Fig. 9
Fig. 9
A, B Association of ten identified hub genes with immune infiltration. C Correlation of the ten hub genes with infiltration of various immune cells. D Differences in immune infiltration between different gene expression groups
Fig. 10
Fig. 10
Efficacy of risk grouping signature in predicting drug sensitivity
Fig. 11
Fig. 11
A Immunohistochemical expression of GPR34 in TNBC tissue; B GPR34 expression level in TNBC tissue was substantially elevated in comparison to tissue of non-TNBC patients
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