Huang Guanyou, Yu Yong, Su Heng, Gan Hongchuan, Chu Liangzhao
Department of Neurosurgery, The Affiliated Jinyang Hospital of Guizhou Medical University, Guiyang, Guizhou, PR China.
Department of Neurosurgery, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, PR China.
Ann Med. 2025 Dec;57(1):2447917. doi: 10.1080/07853890.2024.2447917. Epub 2025 Jan 23.
This study aims to explore the role of exosome-related genes in breast cancer (BRCA) metastasis by integrating RNA-seq and single-cell RNA-seq (scRNA-seq) data from BRCA samples and to develop a reliable prognostic model.
Initially, a comprehensive analysis was conducted on exosome-related genes from the BRCA cohort in The Cancer Genome Atlas (TCGA) database. Three prognostic genes (JUP, CAPZA1 and ARVCF) were identified through univariate Cox regression and Lasso-Cox regression analyses, and a metastasis-related risk score model was established based on these genes. Immune cell infiltration, immune escape and drug sensitivity disparities between high- and low-risk groups were assessed using CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) methods. High- and low-risk cell populations were discerned based on the expression of prognostic genes in BRCA scRNA-seq data.
M0 and M1 macrophages significantly promote the metastasis of breast cancer (BRCA). The developed prognostic model demonstrates good predictive performance for patient survival at 1, 3 and 5 years, with AUC values of 0.654, 0.602 and 0.635, respectively. Compared to the low-risk group, the high-risk group exhibits increased immune cell infiltration and higher levels of immune evasion. scRNA-seq data reveal that high-risk cells have significantly higher risk scores and exhibit notable differences in signalling pathways and intercellular communication patterns.
This study presents a novel risk score model based on exosome-related genes, validated by comprehensive analyses including differential expression, survival analysis and external dataset validation. The model's clinical significance is reinforced through its ability to stratify patients into high- and low-risk groups with distinct survival outcomes and immune landscape characteristics. The integration of RNA-seq and scRNA-seq data highlights the predictive accuracy of the model and underscores its potential for identifying novel therapeutic targets and improving patient prognosis.
本研究旨在通过整合来自乳腺癌（BRCA）样本的RNA测序和单细胞RNA测序（scRNA-seq）数据，探索外泌体相关基因在BRCA转移中的作用，并建立一个可靠的预后模型。
首先，对癌症基因组图谱（TCGA）数据库中BRCA队列的外泌体相关基因进行了全面分析。通过单变量Cox回归和Lasso-Cox回归分析确定了三个预后基因（JUP、CAPZA1和ARVCF），并基于这些基因建立了转移相关风险评分模型。使用CIBERSORT和单样本基因集富集分析（ssGSEA）方法评估高风险组和低风险组之间的免疫细胞浸润、免疫逃逸和药物敏感性差异。根据BRCA scRNA-seq数据中预后基因的表达识别高风险和低风险细胞群体。
M0和M1巨噬细胞显著促进乳腺癌（BRCA）转移。所建立的预后模型对患者1年、3年和5年生存率具有良好的预测性能，AUC值分别为0.654、0.602和0.635。与低风险组相比，高风险组表现出免疫细胞浸润增加和更高水平的免疫逃逸。scRNA-seq数据显示，高风险细胞具有显著更高的风险评分，并且在信号通路和细胞间通讯模式上表现出明显差异。
本研究提出了一种基于外泌体相关基因的新型风险评分模型，通过包括差异表达、生存分析和外部数据集验证在内的综合分析进行了验证。该模型的临床意义通过其将患者分为具有不同生存结果和免疫格局特征的高风险组和低风险组的能力得到加强。RNA-seq和scRNA-seq数据的整合突出了该模型的预测准确性，并强调了其识别新治疗靶点和改善患者预后的潜力。
