The estrogen receptor (ER)-negative breast cancer subtype is aggressive with few treatment options available. To identify specific prognostic factors for ER-negative breast cancer, this study included 705,729 and 1034 breast invasive cancer patients from the Surveillance, Epidemiology, and End Results (SEER) and The Cancer Genome Atlas (TCGA) databases, respectively. To identify key differential kinase–substrate node and edge biomarkers between ER-negative and ER-positive breast cancer patients, we adopted a network-based method using correlation coefficients between molecular pairs in the kinase regulatory network. Integrated analysis of the clinical and molecular data revealed the significant prognostic power of kinase–substrate node and edge features for both subtypes of breast cancer. Two promising kinase–substrate edge features, CSNK1A1–NFATC3 and SRC–OCLN, were identified for more accurate prognostic prediction in ER-negative breast cancer patients.

雌激素受体 (ER) 阴性乳腺癌亚型具有侵袭性，可用的治疗方案很少。为了确定ER 阴性乳腺癌的具体预后因素，本研究分别纳入了监测、流行病学和最终结果 (SEER) 和癌症基因组图谱 (TCGA) 数据库中的 705,729 名和 1034 名乳腺癌浸润性癌症患者。为了识别ER 阴性和 ER 阳性乳腺癌患者之间的关键差异激酶-底物节点和边缘生物标志物，我们采用了基于网络的方法，利用激酶调节网络中分子对之间的相关系数。临床和分子数据的综合分析揭示了激酶底物节点和边缘特征对两种乳腺癌亚型的显着预后能力。两个有前途的激酶底物边缘特征CSNK1A1 – NFATC3和SRC – OCLN被确定用于ER 阴性乳腺癌患者更准确的预后预测。