School of Integrated Chinese and Western Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, Nanjing, 210023, Jiangsu, China.
College of Health Economics Management, Nanjing University of Chinese Medicine, 138 Xianlin Road, Nanjing, 210023, Jiangsu, China.
BMC Cancer. 2021 May 31;21(1):644. doi: 10.1186/s12885-021-08318-1.
Triple-negative breast cancer (TNBC) is a highly heterogeneous subtype of breast cancer, showing aggressive clinical behaviors and poor outcomes. It urgently needs new therapeutic strategies to improve the prognosis of TNBC. Bioinformatics analyses have been widely used to identify potential biomarkers for facilitating TNBC diagnosis and management.
We identified potential biomarkers and analyzed their diagnostic and prognostic values using bioinformatics approaches. Including differential expression gene (DEG) analysis, Receiver Operating Characteristic (ROC) curve analysis, functional enrichment analysis, Protein-Protein Interaction (PPI) network construction, survival analysis, multivariate Cox regression analysis, and Non-negative Matrix Factorization (NMF).
A total of 105 DEGs were identified between TNBC and other breast cancer subtypes, which were regarded as heterogeneous-related genes. Subsequently, the KEGG enrichment analysis showed that these genes were significantly enriched in 'cell cycle' and 'oocyte meiosis' related pathways. Four (FAM83B, KITLG, CFD and RBM24) of 105 genes were identified as prognostic signatures in the disease-free interval (DFI) of TNBC patients, as for progression-free interval (PFI), five genes (FAM83B, EXO1, S100B, TYMS and CFD) were obtained. Time-dependent ROC analysis indicated that the multivariate Cox regression models, which were constructed based on these genes, had great predictive performances. Finally, the survival analysis of TNBC subtypes (mesenchymal stem-like [MSL] and mesenchymal [MES]) suggested that FAM83B significantly affected the prognosis of patients.
The multivariate Cox regression models constructed from four heterogeneous-related genes (FAM83B, KITLG, RBM24 and S100B) showed great prediction performance for TNBC patients' prognostic. Moreover, FAM83B was an important prognostic feature in several TNBC subtypes (MSL and MES). Our findings provided new biomarkers to facilitate the targeted therapies of TNBC and TNBC subtypes.
三阴性乳腺癌（TNBC）是一种高度异质性的乳腺癌亚型，表现出侵袭性的临床行为和不良预后。它迫切需要新的治疗策略来改善 TNBC 的预后。生物信息学分析已被广泛用于识别潜在的生物标志物，以促进 TNBC 的诊断和管理。
我们使用生物信息学方法鉴定了潜在的生物标志物，并分析了它们的诊断和预后价值。包括差异表达基因（DEG）分析、受试者工作特征（ROC）曲线分析、功能富集分析、蛋白质-蛋白质相互作用（PPI）网络构建、生存分析、多变量 Cox 回归分析和非负矩阵分解（NMF）。
共鉴定出 105 个 TNBC 与其他乳腺癌亚型之间的差异表达基因，这些基因被认为是异质性相关基因。随后，KEGG 富集分析表明，这些基因显著富集于“细胞周期”和“卵母细胞减数分裂”相关通路。在 TNBC 患者的无病间隔（DFI）中，从 105 个基因中鉴定出 4 个（FAM83B、KITLG、CFD 和 RBM24）为预后标志物，在无进展间隔（PFI）中，获得了 5 个基因（FAM83B、EXO1、S100B、TYMS 和 CFD）。时间依赖性 ROC 分析表明，基于这些基因构建的多变量 Cox 回归模型具有很好的预测性能。最后，对 TNBC 亚型（间质干细胞样[MSL]和间质[MES]）的生存分析表明，FAM83B 显著影响患者的预后。
从四个异质性相关基因（FAM83B、KITLG、RBM24 和 S100B）构建的多变量 Cox 回归模型对 TNBC 患者的预后具有很好的预测性能。此外，FAM83B 是几个 TNBC 亚型（MSL 和 MES）中的一个重要预后特征。我们的研究结果为促进 TNBC 及 TNBC 亚型的靶向治疗提供了新的生物标志物。
