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Identification of Cancer-Associated Fibroblast Subtype of Triple-Negative Breast Cancer.

Authors :
Wang, Maoli
Feng, Ruifa
Chen, Zihao
Shi, Wenjie
Li, Cheng
Liu, Huiquan
Wu, Kejin
Li, Dajin
Li, Xiqing
Source :
Journal of Oncology. 4/23/2022, p1-14. 14p.
Publication Year :
2022

Abstract

Background. There is limited knowledge about the role of cancer-associated fibroblasts (CAF) in the tumor microenvironment of triple-negative breast cancer (TNBC). Methods. Three hundred and thirty-five TNBC samples from four datasets were retrieved and analyzed. In order to determine the CAF subtype by combining gene expression profiles, an unsupervised clustering analysis was adopted. The prognosis, enriched pathways, immune cells, immune scores, and tumor purity were compared between CAF subtypes. The genes with the highest importance were selected by bioinformatics analysis. The machine learning model was built to predict the TNBC CAF subtype by these selected genes. Results. TNBC samples were classified into two CAF subtypes (CAF+ and CAF-). The CAF- subtype of TNBC was linked to the longer overall survival and more immune cells than the CAF+ subtype. CAF- and CAF+ were enriched in immune-related pathways and extracellular matrix pathways, respectively. Bioinformatics analysis identified 9 CAF subtype-related markers (ADAMTS12, AEBP1, COL10A1, COL11A1, CXCL11, CXCR6, EDNRA, EPPK1, and WNT7B). We constructed a robust random forest model using these 9 genes, and the area under the curve (AUC) value of the model was 0.921. Conclusion. The current study identified CAF subtypes based on gene expression profiles and found that CAF subtypes have significantly different overall survival, immune cells, and immunotherapy response rates. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
16878450
Database :
Academic Search Index
Journal :
Journal of Oncology
Publication Type :
Academic Journal
Accession number :
156465058
Full Text :
https://doi.org/10.1155/2022/6452636