Gene expression profiling of human sarcomas: Insights into sarcoma biology
Gene expression profiling of human sarcomas: Insights into sarcoma biology
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DOI：
10.1158/0008-5472.can-05-1699
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发表时间：
2005-10-15
期刊：
影响因子：
11.2
通讯作者：
Meltzer, PS
中科院分区：
文献类型：
--
作者：
Baird, K;Davis, S;Meltzer, PS
肉瘤是一组生物学上复杂的间叶起源的肿瘤。通过使用基因表达微阵列分析，我们的目的是找到线索，在这些肿瘤中的细胞分化和致癌途径，以及潜在的生物标志物和治疗靶点。我们在12，601个特征的cDNA微阵列上检测了181个肿瘤，代表了16类人类骨和软组织肉瘤。值得注意的是，在该样品组中差异表达的2，766个探针清楚地描绘了各种肿瘤类别。几个潜在的生物学和治疗的兴趣与每种肉瘤类型，包括特定的酪氨酸激酶，转录因子，和同源异型盒基因。我们还确定了脂肪肉瘤、平滑肌瘤和恶性纤维组织细胞瘤中的肿瘤亚组。我们发现了每个肿瘤组的显著基因本体相关性，并通过基因集富集分析鉴定了与正常组织的相似性。对275例肿瘤样本进行的突变分析显示，某些肿瘤中表皮生长因子受体（EGFR）的高表达与基因突变无关。最后，为了进一步研究人类肉瘤生物学，我们创建了一个在线的、公开可用的、可搜索的数据库，该数据库包含来自这些肿瘤的基因表达谱的数据（http：//watson.nhgri.nih.gov/sarcoma），允许用户交互式地深入探索该数据集。
Sarcomas are a biologically complex group of tumors of mesenchymal origin. By using gene expression microarray analysis, we aimed to find clues into the cellular differentiation and oncogenic pathways active in these tumors as well as potential biomarkers and therapeutic targets. We examined 181 tumors representing 16 classes of human bone and soft tissue sarcomas on a 12,601-feature cDNA microarray. Remarkably, 2,766 probes differentially expressed across this sample set clearly delineated the various tumor classes. Several genes of potential biological and therapeutic interest were associated with each sarcoma type, including specific tyrosine kinases, transcription factors, and homeobox genes. We also identified subgroups of tumors within the liposarcomas, leiomyosarcomas, and malignant fibrous histiocytomas. We found significant gene ontology correlates for each tumor group and identified similarity to normal tissues by Gene Set Enrichment Analysis. Mutation analysis done on 275 tumor samples revealed that the high expression of epidermal growth factor receptor (EGFR) in certain tumors was not associated with gene mutations. Finally, to further the investigation of human sarcoma biology, we have created an online, publicly available, searchable database housing the data from the gene expression profiles of these tumors (http://watson.nhgri.nih.gov/sarcoma), allowing the user to interactively explore this data set in depth.