乳腺癌是一种由基因组或转录组中的各种改变引起的异质性疾病。乳腺癌的分子亚型已有报告，但有用的生物标志物仍需确定以揭示潜在的生物学机制并指导临床决策。为了寻找生物标志物，一些研究重点关注提供差异的基因组改变，而很少有关注介导肿瘤进展的转录组表征。我们提出一种名为GLassonet的特征选择方法，以确定具有鉴别性的生物标志物从转录组广泛表达谱中，通过将高维表达关系图嵌入Lassonet模型。GLassonet包括用于识别癌症亚型的非线性神经网络、用于取消隐藏层从输入特征到输出类别的连接的跳过全连接层，以及用于将鉴别图保留到所选子空间中的图增强。首先，一种迭代优化算法在TCGA乳腺癌数据集上学习模型参数以研究分类性能。然后，我们探究GLassonet选择的基因集在癌症亚型中的分布模式，并将它们与最新技术输出的基因集进行比较。更深入地，我们对三个GLassonet选择的新的标记基因，即SOX10、TPX2和TUBA1C，进行整体生存分析，以研究它们的表达变化和评估它们的预后影响。最后，我们进行富集分析，以发现GLassonet选择的基因与GO术语和KEGG通路的功能关联。实验结果表明，GLassonet具有强大的选择鉴别基因的能力，可提高癌症亚型分类性能并为癌症个性化治疗提供潜在的生物标志物。

Breast cancer is a heterogeneous disease caused by various alterations in the genome or transcriptome. Molecular subtypes of breast cancer have been reported, but useful biomarkers remain to be identified to uncover underlying biological mechanisms and guide clinical decisions. Towards biomarker discovery, several studies focus on genomic alterations that provide differences, while few works concern transcriptomic characterizations that mediate tumor progression. Rather than using differential expression (DE) or weighted network analysis, we propose a feature selection method, dubbed GLassonet, to identify discriminative biomarkers from transcriptome-wide expression profiles by embedding the relationship graph of high-dimensional expressions into the Lassonet model. GLassonet comprises a nonlinear neural network for identifying cancer subtypes, a skipping fully connected layer for canceling the connections of hidden layers from input features to output categories, and a graph enhancement for preserving the discriminative graph into the selected subspace. First, an iterative optimization algorithm learns model parameters on the TCGA breast cancer dataset to investigate the classification performance. Then, we probe the distribution patterns of GLassonet-selected gene sets across the cancer subtypes and compare them to gene sets outputted from the state-of-the-art. More profoundly, we conduct the overall survival analysis on three GLassonet-selected new marker genes, i.e., SOX10, TPX2, and TUBA1C, to investigate their expression changes and assess their prognostic impacts. Finally, we perform the enrichment analysis to discover the functional associations of the GLassonet-selected genes with GO terms and KEGG pathways. Experimental results show that GLassonet has a powerful ability to select the discriminative genes, which improve cancer subtype classification performance and provide potential biomarkers for cancer personalized therapy.