Ren Xiaocang, Ma Yanyan, Li Jing, Liu Yuee, Liao Xuankai, Lin Rubing, Qiu Zhihong
Huabei Petroleum Administration Bureau General Hospital, Cangzhou, China.
Department of Pathology, The Seventh Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
Discov Oncol. 2025 May 30;16(1):957. doi: 10.1007/s12672-025-02702-0.
Breast cancer (BC) is a heterogeneous disease with diverse subtypes that influence prognosis and treatment outcomes. While advances have been made in molecular subtyping, the role of exosome-related genes in BC remains underexplored. This study aimed to identify exosome-related gene expression profiles in BC and develop a novel immune score to predict clinical outcomes. We first intersected exosome-related gene sets with differentially expressed genes (DEGs) in BC, identifying 19 overlapping genes, which were then used to stratify patients into two distinct molecular subtypes with significant differences in immune infiltration and prognosis. A machine learning model based on exosome-related genes was constructed to calculate an immune score, which was validated through multiple datasets and demonstrated strong predictive power with areas under the curve (AUC) of 0.777 and 0.763 in training and validation cohorts, respectively. Furthermore, single-cell RNA sequencing data revealed distinct immune landscapes between high and low immune score groups. We found significant differences in immune cell infiltration, with the high immune score group exhibiting enhanced infiltration of CD8 + T cells and NK cells, while the low immune score group was characterized by a more immunosuppressive environment. The immune score was also predictive of response to both chemotherapy and immunotherapy, with high immune score patients showing significantly better responses. We further verified the expression upregulation of the 3 genes responsible for immune score with qPCR and immunoblot. These findings highlight the potential of exosome-related gene expression profiles as a prognostic and predictive biomarker in breast cancer, offering a new avenue for personalized therapeutic strategies.
乳腺癌（BC）是一种异质性疾病，具有多种影响预后和治疗结果的亚型。虽然在分子亚型分类方面已取得进展，但外泌体相关基因在乳腺癌中的作用仍未得到充分探索。本研究旨在确定乳腺癌中外泌体相关基因表达谱，并开发一种新的免疫评分来预测临床结果。我们首先将外泌体相关基因集与乳腺癌中的差异表达基因（DEG）进行交叉分析，确定了19个重叠基因，然后用这些基因将患者分为两种不同的分子亚型，这两种亚型在免疫浸润和预后方面存在显著差异。构建了基于外泌体相关基因的机器学习模型来计算免疫评分，该评分在多个数据集中得到验证，在训练队列和验证队列中的曲线下面积（AUC）分别为0.777和0.763，显示出强大的预测能力。此外，单细胞RNA测序数据揭示了高免疫评分组和低免疫评分组之间不同的免疫格局。我们发现免疫细胞浸润存在显著差异，高免疫评分组CD8 + T细胞和NK细胞浸润增强，而低免疫评分组的特征是免疫抑制环境更强。免疫评分还可预测对化疗和免疫治疗的反应，高免疫评分患者的反应明显更好。我们通过qPCR和免疫印迹进一步验证了负责免疫评分的3个基因的表达上调。这些发现突出了外泌体相关基因表达谱作为乳腺癌预后和预测生物标志物的潜力，为个性化治疗策略提供了新途径。
