A gene expression signature‐based nomogram model in prediction of breast cancer bone metastases
A gene expression signature‐based nomogram model in prediction of breast cancer bone metastases
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DOI：
10.1002/cam4.1932
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发表时间：
2018-12
期刊：
影响因子：
4
通讯作者：
Chenglong Zhao;Yan Lou;Yao Wang;Dongsheng Wang;L. Tang;Xin Gao;Kun Zhang;Wei Xu;Tie-king Liu-Ti
中科院分区：
文献类型：
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作者：
Chenglong Zhao;Yan Lou;Yao Wang;Dongsheng Wang;L. Tang;Xin Gao;Kun Zhang;Wei Xu;Tie-king Liu-Ti
乳腺癌易于形成骨转移，随后的骨骼相关事件（SRE）显著降低患者的生活质量和生存率。骨病变的预测和早期管理是有价值的，然而，适当的预后模型是不够的。在目前的研究中，我们在三个微阵列数据集中共审查了572名乳腺癌患者，包括191名骨转移和381名无转移。基因集富集分析（GSEA）表明，与无转移的患者相比，骨转移患者的侵袭性和低级别特征较低，而管腔亚型更容易形成骨转移。确定了5个骨转移相关基因（KRT 23、REEP 1、SPIB、ALDH 3B2和GLDC），并构建了基于基因表达特征的列线图（GESBN）模型。该模型在评估乳腺癌骨转移（BCBM）的训练集和测试集中表现良好。在临床上，该模型可能有助于预测早期骨转移，预防和管理SRE，甚至有助于延长BCBM患者的生存期。五基因GESBN模型显示出作为分子诊断标记物和治疗靶点的一些意义。此外，我们的研究还提供了一种分析肿瘤器官特异性转移的方法。据我们所知，这是第一个发表的专注于肿瘤器官特异性转移的模型。
Breast cancer is prone to form bone metastases and subsequent skeletal‐related events (SREs) dramatically decrease patients’ quality of life and survival. Prediction and early management of bone lesions are valuable; however, proper prognostic models are inadequate. In the current study, we reviewed a total of 572 breast cancer patients in three microarray data sets including 191 bone metastases and 381 metastases‐free. Gene set enrichment analysis (GSEA) indicated less aggressive and low‐grade features of patients with bone metastases compared with metastases‐free ones, while luminal subtypes are more prone to form bone metastases. Five bone metastases‐related genes (KRT23, REEP1, SPIB, ALDH3B2, and GLDC) were identified and subjected to construct a gene expression signature‐based nomogram (GESBN) model. The model performed well in both training and testing sets for evaluation of breast cancer bone metastases (BCBM). Clinically, the model may help in prediction of early bone metastases, prevention and management of SREs, and even help to prolong survivals for patients with BCBM. The five‐gene GESBN model showed some implications as molecular diagnostic markers and therapeutic targets. Furthermore, our study also provided a way for analysis of tumor organ‐specific metastases. To the best of our knowledge, this is the first published model focused on tumor organ‐specific metastases.