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使用差异表达的自噬相关基因进行乳腺癌预后准确预测的亚型特异性风险模型。

Subtype-specific risk models for accurately predicting the prognosis of breast cancer using differentially expressed autophagy-related genes.

机构信息

Public Laboratory, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin 300060, China.

Key Laboratory of Cancer Prevention and Therapy, Tianjin 300060, China.

出版信息

Aging (Albany NY). 2020 Jul 10;12(13):13318-13337. doi: 10.18632/aging.103437.

DOI:10.18632/aging.103437
PMID:32649310
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7377895/
Abstract

Emerging evidence suggests that the dysregulation of autophagy-related genes (ARGs) is coupled with the carcinogenesis and progression of breast cancer (BRCA). We constructed three subtype-specific risk models using differentially expressed ARGs. In Luminal, Her-2, and Basal-like BRCA, four- (, , , and ), three- (, , and ), and five-gene (, , , , and ) risk models were identified, which all have a receiver operating characteristic > 0.65 in the training and testing dataset. Multivariable Cox analysis showed that those risk models can accurately and independently predict the overall survival of BRCA patients. Comprehensive analysis showed that the 12 identified ARGs were correlated with the overall survival of BRCA patients; six of the ARGs (, , , , , and ) were differentially expressed between BRCA and normal breast tissue at the protein level. In addition, the 12 identified ARGs were highly interconnected and displayed high frequency of copy number variation in BRCA samples. Gene set enrichment analysis suggested that the deactivation of the immune system was the important driving force for the progression of Basal-like BRCA. This study demonstrated that the 12 ARG signatures were potential multi-dimensional biomarkers for the diagnosis, prognosis, and treatment of BRCA.

摘要

新出现的证据表明，自噬相关基因 (ARGs) 的失调与乳腺癌 (BRCA) 的发生和发展有关。我们使用差异表达的 ARGs 构建了三个亚型特异性风险模型。在 Luminal、Her-2 和基底样 BRCA 中，确定了四个 (、、和)、三个 (、和) 和五个基因 (、、、、和) 的风险模型，它们在训练和测试数据集的接收者操作特征曲线均大于 0.65。多变量 Cox 分析表明，这些风险模型可以准确和独立地预测 BRCA 患者的总生存率。综合分析表明，这 12 个鉴定的 ARGs 与 BRCA 患者的总生存率相关；在蛋白质水平，ARGs 中的 6 个 (、、、、和) 在 BRCA 和正常乳腺组织之间存在差异表达。此外，这 12 个鉴定的 ARGs 之间高度相互关联，并在 BRCA 样本中显示出高频的拷贝数变异。基因集富集分析表明，免疫系统的失活是基底样 BRCA 进展的重要驱动力。这项研究表明，这 12 个 ARG 特征是 BRCA 诊断、预后和治疗的潜在多维生物标志物。

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