Zhu Mengdi, Lin Jinna, Liu Haohan, Wang Jingru, Liu Nianqiu, Li Yudong, Lai Hongna, Shi Qianfeng
Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
Cancer Cell Int. 2025 Jul 28;25(1):287. doi: 10.1186/s12935-025-03920-w.
Epigenetic acetylation plays an essential role in the development and drug resistance of luminal breast cancer. However, the acetylation regulatory network in luminal breast cancer remains underexplored.
We used the TCGA-BRCA database to explore the acetylation regulatory network in luminal breast cancer. Spearman correlation coefficients, Cox proportional hazards, and the STRING database were used to identify genes that were correlated with acetylation regulatory molecules in luminal breast cancer and could predict patient outcomes. An acetylation regulatory risk model was constructed via Consensus Cluster Plus and the LASSO risk model. GSEA, K‒M survival analysis, and receiver operating characteristic (ROC) curve analysis were used to analyze survival and possible regulatory pathways of the risk model. TIDE, Microenvironment Cell Populations-counter, and CIBERSORT algorithms were used to analyze the immune landscape of the risk model population. Patients' tumor specimens were used to detect the expression of KAT2B and TAF1L. The luminal breast cancer cell lines MCF-7 and T47D were used in cell viability, Transwell, western blotting, and RT‒qPCR experiments to confirm the risk model. Mouse model was constructed for in vivo validation of KAT2B and TAF1L function.
In our study, we utilized the TCGA-BRCA database to conduct a comprehensive analysis of the acetylation regulatory pattern in luminal breast cancer. Using Consensus Cluster Plus and the LASSO risk model, we screened 6 acetylation-related genes (KAT2B, TAF1L, CDC37, CCDC107, C17orf106, and ASPSCR1) and constructed a 6-gene risk model of luminal breast cancer. Based on this model, luminal breast cancer patients were classified into high- and low-risk subgroups. The high-risk subgroup had a poor prognosis. Further analysis revealed that the high-risk subgroup was associated with lower CD8 + T-cell infiltration and greater responsiveness to immune checkpoint inhibitor therapy. In vitro and in vivo experiments revealed that knockdown of KAT2B and TAF1L dramatically inhibited tumor cell proliferation. In vitro experiments also showed knockdown of KAT2B and TAF1L dramatically inhibited tumor cell migration, increased lymphocyte infiltration, and significantly upregulated the expression of CD8 + T-cell-associated chemokines in luminal breast cancer cells.
In this study, we successfully constructed a 6-gene acetylation-associated risk model for luminal breast cancer, providing a new direction and evidence for personalized treatment. Our results also suggested that KAT2B and TAF1L might serve as potential therapeutic targets in luminal breast cancer.
表观遗传乙酰化在腔面型乳腺癌的发展和耐药性中起着至关重要的作用。然而，腔面型乳腺癌中的乙酰化调控网络仍未得到充分探索。
我们使用TCGA-BRCA数据库来探索腔面型乳腺癌中的乙酰化调控网络。使用Spearman相关系数、Cox比例风险模型和STRING数据库来识别与腔面型乳腺癌中乙酰化调控分子相关且可预测患者预后的基因。通过Consensus Cluster Plus和LASSO风险模型构建乙酰化调控风险模型。使用GSEA、K-M生存分析和受试者工作特征（ROC）曲线分析来分析风险模型的生存情况和可能的调控途径。使用TIDE、微环境细胞群体计数器和CIBERSORT算法来分析风险模型人群的免疫格局。使用患者的肿瘤标本检测KAT2B和TAF1L的表达。使用腔面型乳腺癌细胞系MCF-7和T47D进行细胞活力、Transwell、蛋白质印迹和RT-qPCR实验以验证风险模型。构建小鼠模型以在体内验证KAT2B和TAF1L的功能。
在我们的研究中，我们利用TCGA-BRCA数据库对腔面型乳腺癌中的乙酰化调控模式进行了全面分析。使用Consensus Cluster Plus和LASSO风险模型，我们筛选出6个与乙酰化相关的基因（KAT2B、TAF1L、CDC37、CCDC107、C17orf106和ASPSCR1）并构建了腔面型乳腺癌的6基因风险模型。基于该模型，将腔面型乳腺癌患者分为高风险和低风险亚组。高风险亚组的预后较差。进一步分析表明，高风险亚组与较低的CD8 + T细胞浸润以及对免疫检查点抑制剂治疗的更高反应性相关。体外和体内实验表明，敲低KAT2B和TAF1L可显著抑制肿瘤细胞增殖。体外实验还表明，敲低KAT2B和TAF1L可显著抑制肿瘤细胞迁移，增加淋巴细胞浸润，并显著上调腔面型乳腺癌细胞中CD8 + T细胞相关趋化因子的表达。
在本研究中，我们成功构建了腔面型乳腺癌的6基因乙酰化相关风险模型，为个性化治疗提供了新的方向和证据。我们的结果还表明，KAT2B和TAF1L可能是腔面型乳腺癌的潜在治疗靶点。
