Cancer-associated fibroblast-derived gene signature discriminates distinct prognoses by integrated single-cell and bulk RNA-seq analyses in breast cancer

Cancer-associated fibroblasts (CAFs) are one of the most predominant cellular subpopulations in the tumor stroma and play an integral role in cancer occurrence and progression. However, the prognostic role of CAFs in breast cancer remains poorly understood. We identified a number of CAF-related biom...

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Published in:Aging (Albany, NY.) NY.), 2024-05, Vol.16 (9), p.8279-8305
Main Authors: Fang, Zhou, Han, Yi-Ling, Gao, Zhi-Jie, Yao, Feng
Format: Article
Language:English
Subjects:
Biomarkers, Tumor - genetics
Biomarkers, Tumor - metabolism
Breast Neoplasms - genetics
Breast Neoplasms - pathology
Cancer-Associated Fibroblasts - metabolism
Female
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
Humans
Prognosis
Research Paper
RNA-Seq
Single-Cell Analysis
Transcriptome
Tumor Microenvironment - genetics
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Summary:Cancer-associated fibroblasts (CAFs) are one of the most predominant cellular subpopulations in the tumor stroma and play an integral role in cancer occurrence and progression. However, the prognostic role of CAFs in breast cancer remains poorly understood. We identified a number of CAF-related biomarkers in breast cancer by combining single-cell and bulk RNA-seq analyses. Based on univariate Cox regression as well as Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, a novel CAF-associated prognostic model was developed. Breast cancer patients were grouped according to the median risk score and further analyzed for outcome, clinical characteristic, pathway activity, genomic feature, immune landscape, and drug sensitivity. A total of 341 CAF-related biomarkers were identified from single-cell and bulk RNA-seq analyses. We eventually screened eight candidate prognostic genes, including , , , , , , , and , and constructed the novel CAF-related prognostic model. Grouped by the median risk score, high-risk patients showed a significantly worse prognosis and exhibited distinct pathway activities such as uncontrolled cell cycle progression, angiogenesis, and activation of glycolysis. In addition, the combined risk score and tumor mutation burden significantly improved the ability to predict patient prognosis. Importantly, patients in the high-risk group had a higher infiltration of M2 macrophages and a lower infiltration of CD8 T cells and activated NK cells. Finally, we calculated the IC50 for a range of anticancer drugs and personalized the treatment regimen for each patient. Integrating single-cell and bulk RNA-seq analyses, we identified a list of compositive CAF-associated biomarkers and developed a novel CAF-related prognostic model for breast cancer. This robust CAF-derived gene signature acts as an excellent predictor of patient outcomes and treatment responses in breast cancer.
ISSN:1945-4589
1945-4589
DOI:10.18632/aging.205817