Research Paper Volume 15, Issue 9 pp 3480—3497

A novel signature based on cancer-associated fibroblast genes to predict prognosis, immune feature, and therapeutic response in breast cancer

Yichen Wang1, , Wenchang Lv1, , Yi Yi1, , Qi Zhang1, , Jun Zhang2, &, , Yiping Wu1, ,

  • 1 Department of Plastic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China
  • 2 Department of Thyroid and Breast Surgery, Shenzhen Qianhai Shekou Free Trade Zone Hospital, Shenzhen 518067, Guangdong, China

Received: January 17, 2023       Accepted: April 17, 2023       Published: May 4, 2023
How to Cite

Copyright: © 2023 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Breast cancer (BC) ranks first in the incidence of tumors in women and remains the most prevalent malignancy in women worldwide. Cancer-associated fibroblasts (CAFs) in the tumor microenvironment (TME) profoundly influence the progression, recurrence, and therapeutic resistance in BC. Here, we intended to establish a risk signature based on screened CAF-associated genes in BC (BCCGs) for patient stratification. Initially, BCCGs were screened by a combination of several CAF gene sets. The identified BCGGs were found to differ significantly in the overall survival (OS) of BC patients. Accordingly, we constructed a prognostic prediction signature of 5 BCCGs, which were independent prognostic factors associated with BC based on univariate and multivariate Cox regression. The risk model divided patients into low- and high-risk groups, accompanied by different OS, clinical features, and immune infiltration characteristics. Receiver operating characteristic (ROC) curves and a nomogram further validated the predictive performance of the prognostic model. Notably, 21 anticancer agents targeting these BCCGs possessed better sensitivity in BC patients. Meanwhile, the elevated expression of the majority of immune checkpoint genes suggested that the high-risk group may benefit more from immune checkpoint inhibitors (ICIs) therapy. Taken together, our well-established model is a robust instrument to precisely and comprehensively predict the prognosis, immune features, and drug sensitivity in BC patients, for combating BC.


BC: Breast cancer; CAFs: Cancer-associated fibroblasts; TME: Tumor microenvironment; BCCGs: CAF-associated genes in BC; OS: Overall survival; ICI: Immune checkpoint inhibitor; EMT: Epithelial-mesenchymal transition; α-SMA: Alpha-smooth muscle actin; PDGFRβ: Platelet-derived growth factor beta; FAP: Fibroblast activating protein; Cav1: Caveolin-1; K-M: Kaplan-Meier; GEO: Gene Expression Omnibus; ROC: Receiver operating characteristic curve; DCA: Decision curve analysis; IC50: Inhibitory concentration; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; HR: Hazard ratios; CI: Confidence intervals; AUC: Area under the ROC curve; DEGs: Differentially expressed genes; DCA: Decision curve analysis; Tregs: T cells regulatory; ICIs: Immune checkpoint inhibitors; GO: The Gene Ontology; BP: Biological Processes; CC: Cellular Component; MF: Molecular Function; KEGG: The Kyoto Encyclopedia of Genes and Genomes; HPA: Human Protein Atlas; IHC: Immunohistochemistry; ECM: Extracellular matrix; TIME: Tumor Immune Microenvironment.