Chang Yiming, Huang Zhiyuan, Quan Hong, Li Hui, Yang Shuo, Song Yifei, Wang Jian, Yuan Jian, Wu Chenming
Jinzhou Medical University, Shanghai East Hospital, Shanghai, China.
Research Center for Translational Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.
Front Oncol. 2023 Jan 11;12:1085632. doi: 10.3389/fonc.2022.1085632. eCollection 2022.
DNA damage repair (DDR) genes are involved in developing breast cancer. Recently, a targeted therapeutic strategy through DNA repair machinery, including PARPi, has initially shown broad development and application prospects in breast cancer therapy. However, few studies that focused on the correlation between the expression level of DNA repair genes, prognosis, and immune response in breast cancer patients have been recently conducted. Herein, we focused on identifying differentially expressed DNA repair genes (DEGs) in breast cancer specimens and normal samples using the Wilcoxon rank-sum test. Biofunction enrichment analysis was performed with DEGs using the R software "cluster Profiler" package. DNA repair genes were involved in multivariate and univariate Cox regression analyses. After the optimization by AIC value, 11 DNA repair genes were sorted as prognostic DNA repair genes for breast cancer patients to calculate risk scores. Simultaneously, a nomogram was used to represent the prognostic model, which was validated using a calibration curve and C-index. Single-sample gene set enrichment analysis (ssGSEA), CIBERSORT algorithms, and ESTIMATE scores were applied to evaluate the immune filtration of tumor samples. Subsequently, anticarcinogen sensitivity analysis was performed using the R software "pRRophetic" package. Unsupervised clustering was used to excavate the correlation between the expression level of prognostic-significant DNA repair genes and clinical features. In summary, 56 DEGs were sorted, and their potential enriched biofunction pathways were revealed. In total, 11 DNA repair genes (, , , , , , , , , , and ) were preserved as prognostic genes to estimate risk score, which was applied to establish the prognostic model and stratified breast cancer patients into two groups with high or low risk. The calibration curve and C-index indicated that they reliably predicted the survival of breast cancer patients. Immune filtration analysis, anticarcinogen sensitivity analysis, and unsupervised clustering were applied to reveal the character of DNA repair genes between low- and high-risk groups. We identified 11 prognosis-significant DNA repair genes to establish prediction models and immune responses in breast cancer patients.
DNA损伤修复（DDR）基因与乳腺癌的发生发展有关。最近，一种通过DNA修复机制的靶向治疗策略，包括PARPi，最初已在乳腺癌治疗中显示出广阔的发展和应用前景。然而，最近很少有研究关注乳腺癌患者DNA修复基因表达水平、预后和免疫反应之间的相关性。在此，我们使用Wilcoxon秩和检验，重点鉴定乳腺癌标本和正常样本中差异表达的DNA修复基因（DEGs）。使用R软件“cluster Profiler”包对DEGs进行生物功能富集分析。DNA修复基因参与多变量和单变量Cox回归分析。通过AIC值进行优化后，筛选出11个DNA修复基因作为乳腺癌患者的预后DNA修复基因以计算风险评分。同时，使用列线图来表示预后模型，并通过校准曲线和C指数进行验证。采用单样本基因集富集分析（ssGSEA）、CIBERSORT算法和ESTIMATE评分来评估肿瘤样本的免疫浸润。随后，使用R软件“pRRophetic”包进行抗癌药物敏感性分析。采用无监督聚类来挖掘预后显著的DNA修复基因表达水平与临床特征之间的相关性。总之，筛选出56个DEGs，并揭示了它们潜在的富集生物功能途径。总共保留了11个DNA修复基因（ 、 、 、 、 、 、 、 、 、 、 ）作为预后基因来估计风险评分，该评分用于建立预后模型并将乳腺癌患者分为高风险或低风险两组。校准曲线和C指数表明它们能够可靠地预测乳腺癌患者的生存情况。应用免疫浸润分析、抗癌药物敏感性分析和无监督聚类来揭示低风险和高风险组之间DNA修复基因的特征。我们鉴定出11个预后显著的DNA修复基因，以建立乳腺癌患者的预测模型和免疫反应。
