Wu Tong, Wang Yunfeng, Jiang Ronghui, Lu Xinliang, Tian Jiawei
Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Heilongjiang Province, China.
College of Bioinformatics Science and Technology, Harbin Medical University, Heilongjiang Province, China.
Oncotarget. 2017 Jun 17;8(35):58809-58822. doi: 10.18632/oncotarget.18544. eCollection 2017 Aug 29.
Breast cancer is highly heterogeneous and is classified into four subtypes characterized by specific biological traits, treatment responses, and clinical prognoses. We performed a systemic analysis of 698 breast cancer patient samples from The Cancer Genome Atlas project database. We identified 136 breast cancer genes differentially expressed among the four subtypes. Based on unsupervised clustering analysis, these 136 core genes efficiently categorized breast cancer patients into the appropriate subtypes. Functional enrichment based on Kyoto Encyclopedia of Genes and Genomes analysis identified six functional pathways regulated by these genes: JAK-STAT signaling, basal cell carcinoma, inflammatory mediator regulation of TRP channels, non-small cell lung cancer, glutamatergic synapse, and amyotrophic lateral sclerosis. Three support vector machine (SVM) classification models based on the identified pathways effectively classified different breast cancer subtypes, suggesting that breast cancer subtype-specific risk assessment based on disease pathways could be a potentially valuable approach. Our analysis not only provides insight into breast cancer subtype-specific mechanisms, but also may improve the accuracy of SVM classification models.
乳腺癌具有高度异质性，可分为四种亚型，其特征在于特定的生物学特性、治疗反应和临床预后。我们对来自癌症基因组图谱（The Cancer Genome Atlas）项目数据库的698例乳腺癌患者样本进行了系统分析。我们鉴定出136个在四种亚型之间差异表达的乳腺癌基因。基于无监督聚类分析，这136个核心基因能够有效地将乳腺癌患者分类到相应的亚型中。基于京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes）分析的功能富集鉴定出由这些基因调控的六个功能通路：JAK-STAT信号通路、基底细胞癌、TRP通道的炎症介质调节、非小细胞肺癌、谷氨酸能突触和肌萎缩侧索硬化症。基于所鉴定通路构建的三个支持向量机（SVM）分类模型有效地对不同的乳腺癌亚型进行了分类，这表明基于疾病通路的乳腺癌亚型特异性风险评估可能是一种潜在的有价值的方法。我们的分析不仅深入了解了乳腺癌亚型特异性机制，还可能提高SVM分类模型的准确性。
