A Risk Assessment Model Based on Circadian Rhythm-Related Genes for Predicting Breast Cancer Prognosis

在这项研究中，我们构建了一个基于昼夜节律相关基因(Circadian Genes, CRGs)的风险评估模型，用于预测乳腺癌(Breast Cancer, BRCA)患者的预后和免疫治疗效果。通过分析TCGA数据库中的基因表达数据和临床信息，我们识别出与预后相关的八个基因。通过多因素Cox回归分析，我们筛选出这些基因并构建了预后标志物。利用Kaplan-Meier分析和受试者工作特征(Receiver Operation Characteristic, ROC)曲线评估了该标志物的预后价值，并在GSE42568数据集中进行了验证。结果表明，高风险评分患者的预后较差，1年、3年和5年生存率的曲线下面积(Area under Curve, AUC)分别为0.667、0.703和0.713。通过基因集富集分析(Gene Set Enrichment Analysis, GSEA)和免疫景观分析，我们发现高风险组显著富集于细胞周期通路和半胱氨酸及甲硫氨酸代谢通路，而低风险组则富集于免疫相关通路。此外，高风险组中免疫检查点基因的表达较低，与较差的预后一致。我们的研究揭示了CRGs在乳腺癌预后和免疫治疗中的重要性，并为未来的个性化治疗策略提供了新的方向。In this study, we constructed a risk assessment model based on circadian rhythm-related genes (Circadian Genes, CRGs) to predict the prognosis and immunotherapy outcomes of breast cancer (Breast Cancer, BRCA) patients. By analyzing gene expression data and clinical information from the TCGA database, we identified eight genes associated with prognosis. Through multivariate Cox regression analysis, we screened these genes and constructed a prognostic biomarker. The prognostic value of this biomarker was evaluated using Kaplan-Meier analysis and Receiver Operating Characteristic (ROC) curves, and it was validated in the GSE42568 dataset. The results showed that patients with high-risk scores had poorer prognoses, with area under the curve (AUC) values for 1-year, 3-year, and 5-year survival rates being 0.667, 0.703, and 0.713, respectively. Through Gene Set Enrichment Analysis (GSEA) and immune landscape analysis, we found that the high-risk group was significantly enriched in cell cycle pathways and cysteine and methionine metabolism pathways, while the low-risk group was enriched in immune-related pathways. Additionally, the expression of immune checkpoint genes was lower in the high-risk group, consistent with poorer prognosis. Our study reveals the importance of CRGs in breast cancer prognosis and immunotherapy, providing new directions for future personalized treatment strategies.