Wu Huacong, Chen Yutao, Li Mengyi, Chen Zijun, Liu Jie, Lai Guie
Department of Thyroid and Breast Surgery, The First Affiliated Hospital of Dali University, Dali, China.
The Second Clinical School of Clinical Medicine, Southern Medical University, Guangzhou, China.
J Cancer Res Clin Oncol. 2023 Nov;149(15):13889-13904. doi: 10.1007/s00432-023-05198-9. Epub 2023 Aug 4.
It is unknown how the cell cycle plays a role in breast cancer (BC). This study aimed to establish a clinically applicable predictive model to predict the therapeutic responses and overall survival in BC patients.
Cell cycle-related genes (CCGs) were identified within the Cancer Genome Atlas cohort (n is equal to 1001) and the Gene Expression Omnibus cohort (n is equal to 3265). An analysis of univariate and multivariate Cox was then conducted to develop a nomogram based on CCGs. After validating the nomogram, risk cohort stratification was established and the predictive value was examined. Finally, immune cell infiltration and therapeutic responses were analysed.
Based on 15 CCGs, 4 prognostic predictors were identified and entered into the nomogram. According to the curves of calibration, the estimated and observed value of the nomogram is in optimal agreement. Subsequently, stratification into two risk cohorts showed that the predictive value, immune cell infiltration and overall survival were better among patients with low risk. Immune checkpoint expression in patients with BC at higher risk was downregulated. Furthermore, the results of the study revealed that doxorubicin, paclitaxel, docetaxel, cisplatin and vinorelbine all had higher IC50 values in patients with high-risk BC.
The nomogram based on CCG could assess tumour immune micro-environment regulation and therapeutic responses of immunotherapy in BC. Moreover, it may provide novel information on the control of immune micro-environment infiltration in BC and aid in the development of targeted immunotherapy.
细胞周期在乳腺癌（BC）中如何发挥作用尚不清楚。本研究旨在建立一种临床适用的预测模型，以预测BC患者的治疗反应和总生存期。
在癌症基因组图谱队列（n = 1001）和基因表达综合数据库队列（n = 3265）中鉴定细胞周期相关基因（CCGs）。然后进行单变量和多变量Cox分析，以开发基于CCGs的列线图。在验证列线图后，建立风险队列分层并检查预测价值。最后，分析免疫细胞浸润和治疗反应。
基于15个CCGs，鉴定出4个预后预测因子并纳入列线图。根据校准曲线，列线图的估计值和观察值具有最佳一致性。随后，分为两个风险队列显示，低风险患者的预测价值、免疫细胞浸润和总生存期更好。高风险BC患者的免疫检查点表达下调。此外，研究结果显示，阿霉素、紫杉醇、多西他赛、顺铂和长春瑞滨在高风险BC患者中的IC50值均较高。
基于CCGs的列线图可以评估BC中肿瘤免疫微环境调节和免疫治疗的治疗反应。此外，它可能为控制BC中的免疫微环境浸润提供新信息，并有助于开发靶向免疫治疗。
