While several multigene signatures are available for predicting breast cancer prognosis, particularly in early stage disease, effective molecular indicators are needed, especially for triple-negative carcinomas, to improve treatments and predict diagnostic outcomes. The objective of this study was to identify transcriptional regulatory networks to better understand mechanisms giving rise to breast cancer development and to incorporate this information into a model for predicting clinical outcomes. Gene expression profiles from 1097 breast cancer patients were retrieved from The Cancer Genome Atlas (TCGA). Breast cancer-specific transcription regulatory information was identified by considering the binding site information from ENCODE and the top co-expressed targets in TCGA using a nonlinear approach. We then used this information to predict breast cancer patient survival outcome. We built a multiple regulator-based prediction model for breast cancer. This model was validated in more than 5000 breast cancer patients from the Gene Expression Omnibus (GEO) databases. We demonstrated our regulator model was significantly associated with clinical stage and that cell cycle and DNA replication related pathways were significantly enriched in high regulator risk patients. Our findings demonstrate that transcriptional regulator activities can predict patient survival. This finding provides additional biological insights into the mechanisms of breast cancer progression.

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转录调节器活性的高度可靠模型预测乳腺癌的总体存活率

尽管有几种多基因标记可用于预测乳腺癌的预后，尤其是在早期疾病中，但仍需要有效的分子指标，尤其是对于三阴性癌症，才能改善治疗并预测诊断结果。这项研究的目的是确定转录调控网络，以更好地理解引起乳腺癌发展的机制，并将该信息纳入预测临床结果的模型中。从癌症基因组图谱（TCGA）中检索了1097名乳腺癌患者的基因表达谱。通过使用非线性方法考虑来自ENCODE的结合位点信息和TCGA中最常见的共表达靶标，确定了乳腺癌特异性转录调控信息。然后，我们使用此信息来预测乳腺癌患者的生存结果。我们建立了基于多重调节剂的乳腺癌预测模型。该模型已通过Gene Expression Omnibus（GEO）数据库在5000多名乳腺癌患者中得到验证。我们证明了我们的调节剂模型与临床阶段显着相关，并且在高度调节剂风险较高的患者中细胞周期和DNA复制相关的途径显着丰富。我们的发现表明，转录调节活性可以预测患者的存活率。这一发现为乳腺癌进展的机制提供了更多的生物学见解。该模型已通过Gene Expression Omnibus（GEO）数据库在5000多名乳腺癌患者中得到验证。我们证明了我们的调节剂模型与临床阶段显着相关，并且在高度调节剂风险较高的患者中细胞周期和DNA复制相关的途径显着丰富。我们的发现表明，转录调节活性可以预测患者的存活率。这一发现为乳腺癌进展的机制提供了更多的生物学见解。该模型已通过Gene Expression Omnibus（GEO）数据库在5000多名乳腺癌患者中得到验证。我们证明了我们的调节剂模型与临床阶段显着相关，并且在高度调节剂风险较高的患者中细胞周期和DNA复制相关的途径显着丰富。我们的发现表明，转录调节活性可以预测患者的存活率。这一发现为乳腺癌进展的机制提供了更多的生物学见解。