Wang Ling, Gao Xiang, Zuo Ximeng, Wang Tangshun, Shi Xiaoguang
Department of Breast Surgery, Dongzhimen Hospital, Beijing University of Chinese Medicine, Haiyuncang 5th, Dongcheng District, Beijing, 100700, China.
World J Surg Oncol. 2025 May 14;23(1):186. doi: 10.1186/s12957-025-03829-8.
Breast cancer (BC) remains the most prevalent malignancy among women. Clinical evidence indicates that genetic variations related to circadian rhythms, as well as the timing of therapeutic interventions, influence the response to radiation therapy and the toxicity of pharmacological treatments in women with BC. This study aimed to identify key circadian rhythm-related genes (CRGs) using bioinformatics and machine learning, and construct a prognostic model to predict clinical outcomes.
Transcriptome data for BC were retrieved from The Cancer Genome Atlas database. Univariate Cox regression and least absolute shrinkage and selection operator regression analyses were used to develop a prognostic model based on CRGs. The predictive performance of the risk score model was evaluated. Univariate and multivariate Cox regression analyses were applied to construct the prognostic model and stratify patients into high-risk and low-risk groups. Additionally, differences in immune microenvironment, immunotherapy efficacy, and tumor mutation burden were assessed between risk groups.
A prognostic risk score model comprising 17 CRGs was developed. The areas under the receiver operating characteristic curve for overall survival at 1, 3, 5, and 7 years exceeded 0.6, indicating acceptable predictive performance. Calibration plots and decision curve analyses demonstrated the use of the model in prognostic prediction. Significant differences in immune microenvironment, immunotherapy efficacy, and tumor mutation burden were identified between the low-risk and high-risk groups.
The circadian rhythm-based gene model, effectively predicted the prognosis of individuals with BC, highlighting its potential to inform personalized therapeutic strategies and improve patient outcomes.
乳腺癌（BC）仍是女性中最常见的恶性肿瘤。临床证据表明，与昼夜节律相关的基因变异以及治疗干预的时机，会影响BC女性对放射治疗的反应和药物治疗的毒性。本研究旨在利用生物信息学和机器学习确定关键的昼夜节律相关基因（CRGs），并构建一个预测临床结局的预后模型。
从癌症基因组图谱数据库中检索BC的转录组数据。使用单变量Cox回归和最小绝对收缩和选择算子回归分析，基于CRGs开发一个预后模型。评估风险评分模型的预测性能。应用单变量和多变量Cox回归分析构建预后模型，并将患者分为高风险和低风险组。此外，评估风险组之间免疫微环境、免疫治疗疗效和肿瘤突变负担的差异。
开发了一个包含17个CRGs的预后风险评分模型。1、3、5和7年总生存的受试者工作特征曲线下面积超过0.6，表明预测性能可接受。校准图和决策曲线分析证明了该模型在预后预测中的应用。低风险和高风险组之间在免疫微环境、免疫治疗疗效和肿瘤突变负担方面存在显著差异。
基于昼夜节律的基因模型有效预测了BC个体的预后，突出了其为个性化治疗策略提供信息并改善患者结局的潜力。
