Wang Yunxiang, Xu Sheng, Liu Junfeng, Qi Pan
Head and Neck Breast Department, Xinxiang Central Hospital, The Fourth Clinical College of Xinxiang Medical University, Xinxiang, Henan, 453000, People's Republic of China.
Breast Cancer (Dove Med Press). 2024 Dec 9;16:887-911. doi: 10.2147/BCTT.S490154. eCollection 2024.
Peroxisomes are increasingly linked to cancer development, yet the prognostic role of peroxisome-related genes (PRGs) in breast cancer remains unclear.
This study aimed to construct a prognostic model based on PRG expression in breast cancer to clarify their prognostic value and clinical implications.
Transcriptomic data from TCGA and GEO were used for training and validation cohorts. TME characteristics were analyzed with ESTIMATE, MCP-counter, and CIBERSORT algorithms. qPCR validated mRNA expression levels of risk genes, and data analysis was conducted in R.
Univariate and multivariate Cox regression identified a 7-gene PRG risk signature (ACBD5, ACSL5, DAO, NOS2, PEX3, PEX10, and SLC27A2) predicting breast cancer prognosis in training (n=1069), internal validation (n=327), and external validation (merged from four GEO datasets, n=640) datasets. While basal and Her2 subtypes had higher risk scores than luminal subtypes, a significant prognostic impact of the PRG risk signature was seen only in luminal subtypes. The high-risk subgroup exhibited a higher frequency of focal synonymous copy number alterations (SCNAs), arm-level amplifications and deletions, and single nucleotide variations. These increased genomic aberrations were associated with greater immune suppression and reduced CD8+ T cell infiltration. Bulk RNA sequencing and single-cell analyses revealed distinct expression patterns of peroxisome-related genes (PRGs) in the breast cancer TME: PEX3 was primarily expressed in malignant and stromal cells, while ACSL5 showed high expression in T cells. Additionally, the PRG risk signature demonstrated efficacy comparable to that of well-known biomarkers for predicting immunotherapy responses. Drug sensitivity analysis revealed that the PRG high-risk subgroup was sensitive to inhibitors of BCL-2 family proteins (BCL-2, BCL-XL, and MCL1) and other kinases (PLK1, PLK1, BTK, CHDK1, and EGFR).
The PRG risk signature serves as a promising biomarker for evaluating peroxisomal activity, prognosis, and responsiveness to immunotherapy in breast cancer.
过氧化物酶体与癌症发展的关联日益密切，但过氧化物酶体相关基因（PRGs）在乳腺癌中的预后作用仍不清楚。
本研究旨在构建基于PRG表达的乳腺癌预后模型，以阐明其预后价值和临床意义。
来自TCGA和GEO的转录组数据用于训练和验证队列。使用ESTIMATE、MCP-counter和CIBERSORT算法分析肿瘤微环境（TME）特征。qPCR验证风险基因的mRNA表达水平，并在R中进行数据分析。
单变量和多变量Cox回归确定了一个7基因PRG风险特征（ACBD5、ACSL5、DAO、NOS2、PEX3、PEX10和SLC27A2），可在训练数据集（n = 1069）、内部验证数据集（n = 327）和外部验证数据集（合并自四个GEO数据集，n = 640）中预测乳腺癌预后。虽然基底样和Her2亚型的风险评分高于管腔亚型，但PRG风险特征仅在管腔亚型中显示出显著的预后影响。高风险亚组表现出更高频率的局灶性同义拷贝数改变（SCNAs）、臂水平扩增和缺失以及单核苷酸变异。这些增加的基因组畸变与更大的免疫抑制和CD8 + T细胞浸润减少有关。批量RNA测序和单细胞分析揭示了乳腺癌TME中过氧化物酶体相关基因（PRGs）的不同表达模式：PEX3主要在恶性和基质细胞中表达，而ACSL5在T细胞中高表达。此外，PRG风险特征显示出与预测免疫治疗反应的知名生物标志物相当的疗效。药物敏感性分析表明，PRG高风险亚组对BCL-2家族蛋白（BCL-2、BCL-XL和MCL1）和其他激酶（PLK1、PLK1、BTK、CHDK1和EGFR）的抑制剂敏感。
PRG风险特征是评估乳腺癌中过氧化物酶体活性、预后和免疫治疗反应性的有前景的生物标志物。
