Wang Yufei, Wang Yanmei, Zhou Jia, Ying Pingting, Wang Zhuo, Wu Yan, Hao Minyan, Qiu Shuying, Jin Hongchuan, Wang Xian
Department of Medical Oncology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
Laboratory of Cancer Biology, Key Lab of Biotherapy in Zhejiang Province, Cancer Center of Zhejiang University, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
J Cancer Res Clin Oncol. 2023 Nov;149(15):14205-14225. doi: 10.1007/s00432-023-05222-y. Epub 2023 Aug 9.
Breast cancer (BRCA) is a prevalent tumor worldwide. The association between the coiled-coil domain-containing (CCDC) protein family and different tumors has been established. However, the prognostic significance of this protein family in breast cancer remains uncertain.
Gene expression and clinical data were obtained from the TCGA, METABRIC, and GEO databases. Prognosis genes were identified using univariate Cox and LASSO Cox regression, leading to the establishment of a prognostic signature. Subsequently, the risk model was conducted based on survival and clinical feature analyses, and a nomogram for prognosis prediction was developed. Furthermore, analyses of biological function, immune characteristics, and drug sensitivity were performed. Finally, single-cell sequencing data were utilized to uncover the expression patterns of genes in the risk model.
Five genes were identified and utilized for risk modeling. The model demonstrated excellent prognostic value as indicated by ROC and Kaplan-Meier analysis. The high-risk group exhibited shorter survival time and higher likelihood of recurrence. Functional annotation indicated a correlation between the risk score and immune pathways. Conversely, the low-risk group displayed a greater enrichment in immune pathways and exhibited more active immune microenvironment characteristics. Additionally, drug sensitivity analysis using both public and our sequencing data revealed that the risk model possessed a broad range of predictive values.
We have developed a gene signature and have verified that patients with low-risk are more likely to have better prognosis and respond positively to therapy. This finding offers a valuable point of reference for BRCA individualized treatment.
乳腺癌（BRCA）是全球范围内一种常见的肿瘤。含卷曲螺旋结构域（CCDC）蛋白家族与不同肿瘤之间的关联已被确立。然而，该蛋白家族在乳腺癌中的预后意义仍不明确。
从TCGA、METABRIC和GEO数据库获取基因表达和临床数据。使用单变量Cox和LASSO Cox回归识别预后基因，从而建立一个预后特征。随后，基于生存和临床特征分析构建风险模型，并开发用于预后预测的列线图。此外，还进行了生物学功能、免疫特征和药物敏感性分析。最后，利用单细胞测序数据揭示风险模型中基因的表达模式。
鉴定出五个基因并用于风险建模。ROC和Kaplan-Meier分析表明该模型具有优异的预后价值。高风险组的生存时间较短且复发可能性更高。功能注释表明风险评分与免疫途径之间存在相关性。相反，低风险组在免疫途径中表现出更大的富集，并表现出更活跃的免疫微环境特征。此外，使用公共数据和我们的测序数据进行的药物敏感性分析表明，风险模型具有广泛的预测价值。
我们开发了一种基因特征，并验证了低风险患者更有可能具有更好的预后并对治疗产生积极反应。这一发现为BRCA个体化治疗提供了有价值的参考点。
