Abstract:

Breast cancer （BRCA） is one of the most common cancers in women worldwide， and its development is closely related to the interaction of stromal cells in the tumor microenvironment （TME）. As a key component of the TME， cancer associated fibroblasts （CAF） play an important role in tumor growth， metastasis and immunity. In order to investigate the value of CAF functional subpopulations in the treatment and prognosis of breast cancer patients， single-cell RNA sequencing （scRNA-seq） data from the Gene Expression Omnibus （GEO） and The Cancer Genome Atlas （TCGA） databases were integrated with the clinical information of breast cancer， and differentially expressed genes （DEGs） were analyzed in the TCGA database to identify the DEGs in the normal and tumor samples. CAF-related prognostic genes were selected by Pearson correlation coefficients and a one-way Cox regression analysis. Based on this foundation， a CAF-related risk profile was constructed by the least absolute shrinkage and selection operator （Lasso） regression model， and a column chart was created by combining the clinicopathological variables to establish a prognostic model based on CAF subgroup profiles， which provides a new theoretical support for the accurate assessment of clinical prognosis， optimization of immunotherapy strategies， and guidance of individualized drug use.

Key words: cancer-associated fibroblasts, breast cancer, tumor microenvironment, prognosis, immunotherapy

摘要：

乳腺癌（breast cancer，BRCA）是全球范围内女性最为常见的癌症之一，其发生发展和肿瘤微环境（tumor microenvironment，TME）基质细胞的相互作用密切相关。肿瘤相关成纤维细胞（cancer associated fibroblasts，CAF）作为TME的关键组成部分，在肿瘤的生长、转移以及免疫过程中发挥着重要作用。为深入研究CAF亚群在乳腺癌患者治疗、预后中的功能，将基因表达总库（Gene Expression Omnibus，GEO）和癌症基因组图谱（The Cancer Genome Atlas，TCGA）数据库中的单细胞RNA测序（single cell RNA sequencing，scRNA-seq）数据与乳腺癌临床信息进行整合分析，筛选出正常样本和肿瘤样本两者间的差异表达基因（differentially expressed genes，DEGs），利用Pearson相关系数和单因素考克斯回归分析筛选出CAF相关的预后基因。基于此，依靠最小绝对值收敛和选择算子（least absolute shrinkage and selection operator，Lasso）回归模型构建CAF相关风险特征，并结合临床病理变量制作列线图，顺利建立了基于CAF亚组特征的乳腺癌预后预测模型，该体系为精准评估患者的临床预后效果、优化免疫治疗策略以及指导临床个体化用药等提供了新的理论支撑。

关键词: 肿瘤相关成纤维细胞, 乳腺癌, 肿瘤微环境, 预后, 免疫治疗