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.
研究问题：ER阴性乳腺癌的新型药物靶点研究方法：对705,704例来自SEER和1034例来自TCGA的乳腺癌患者RNAseq数据集进行了分析，根据表达量构建了激酶-底物边强度，通过LASSO回归筛选出了ER阳性和阴性亚型特异的激酶-底物特征，利用随机森林的方法阐明了激酶-底物点和边强度特征对乳腺癌两种亚型预后预测的影响。主要成果1：在SEER和TCGA两个数据集中，ER阴性乳腺癌都表现出了相比于ER阳性更差的生存。主要成果2：根据每一对激酶和底物之间的相关性，激酶和底物表达的“点”特征被转换为激酶-底物的边强度。LASSO回归筛选出了ER阳性和阴性亚型特异的激酶-底物特征。主要成果3：乳腺癌中激酶-底物相关特征具有非常好的预后判别能力，并且显著好于已知的乳腺癌预后标记物。主要成果4：2条ER阴性乳腺癌亚型特异的激酶-底物边CSNK1A1-NFATC3和SRC-OCLN，为ER阴性乳腺癌患者的治疗提供了新的思路。





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