From the Dan L. Duncan Comprehensive Cancer Center Division of Biostatistics, Human Genome Sequencing Center, and Department of Medicine, Baylor College of Medicine; and Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX.
Cancer J. 2023;29(1):9-14. doi: 10.1097/PPO.0000000000000638.
The vast amount of gene expression profiling data of bulk tumors and cell lines available in the public domain represents a tremendous resource. For any major cancer type, expression data can identify molecular subtypes, predict patient outcome, identify markers of therapeutic response, determine the functional consequences of somatic mutation, and elucidate the biology of metastatic and advanced cancers. This review provides a broad overview of gene expression profiling in cancer (which may include transcriptome and proteome levels) and the types of findings made using these data. This review also provides specific examples of accessing public cancer gene expression data sets and generating unique views of the data and the resulting genes of interest. These examples involve pan-cancer molecular subtyping, metabolism-associated expression correlates of patient survival involving multiple cancer types, and gene expression correlates of chemotherapy response in breast tumors.
大量的肿瘤和细胞系的基因表达谱数据在公共领域中是一个巨大的资源。对于任何主要的癌症类型，表达数据可以识别分子亚型，预测患者的预后，识别治疗反应的标志物，确定体细胞突变的功能后果，并阐明转移性和晚期癌症的生物学。这篇综述广泛概述了癌症中的基因表达谱（可能包括转录组和蛋白质组水平），以及使用这些数据所得到的发现类型。这篇综述还提供了使用公共癌症基因表达数据集并生成数据和感兴趣基因的独特视图的具体示例。这些示例涉及泛癌分子分型、涉及多种癌症类型的与患者生存相关的代谢相关表达相关性，以及乳腺癌化疗反应的基因表达相关性。
