Montero-Conde C, Martín-Campos J M, Lerma E, Gimenez G, Martínez-Guitarte J L, Combalía N, Montaner D, Matías-Guiu X, Dopazo J, de Leiva A, Robledo M, Mauricio D
Hereditary Endocrine Cancer Research Group, Spanish National Cancer Research Centre (CNIO), Madrid, Spain.
Oncogene. 2008 Mar 6;27(11):1554-61. doi: 10.1038/sj.onc.1210792. Epub 2007 Sep 17.
Undifferentiated and poorly differentiated thyroid tumors are responsible for more than half of thyroid cancer patient deaths in spite of their low incidence. Conventional treatments do not obtain substantial benefits, and the lack of alternative approaches limits patient survival. Additionally, the absence of prognostic markers for well-differentiated tumors complicates patient-specific treatments and favors the progression of recurrent forms. In order to recognize the molecular basis involved in tumor dedifferentiation and identify potential markers for thyroid cancer prognosis prediction, we analysed the expression profile of 44 thyroid primary tumors with different degrees of dedifferentiation and aggressiveness using cDNA microarrays. Transcriptome comparison of dedifferentiated and well-differentiated thyroid tumors identified 1031 genes with >2-fold difference in absolute values and false discovery rate of <0.15. According to known molecular interaction and reaction networks, the products of these genes were mainly clustered in the MAPkinase signaling pathway, the TGF-beta signaling pathway, focal adhesion and cell motility, activation of actin polymerization and cell cycle. An exhaustive search in several databases allowed us to identify various members of the matrix metalloproteinase, melanoma antigen A and collagen gene families within the upregulated gene set. We also identified a prognosis classifier comprising just 30 transcripts with an overall accuracy of 95%. These findings may clarify the molecular mechanisms involved in thyroid tumor dedifferentiation and provide a potential prognosis predictor as well as targets for new therapies.
未分化和低分化甲状腺肿瘤尽管发病率较低，但却导致了一半以上的甲状腺癌患者死亡。传统治疗方法并未取得显著疗效，且缺乏替代方法限制了患者的生存期。此外，缺乏针对高分化肿瘤的预后标志物使个体化治疗变得复杂，并促使复发形式的进展。为了认识肿瘤去分化所涉及的分子基础并确定甲状腺癌预后预测的潜在标志物，我们使用cDNA微阵列分析了44例具有不同去分化程度和侵袭性的甲状腺原发性肿瘤的表达谱。去分化和高分化甲状腺肿瘤的转录组比较确定了1031个基因，其绝对值差异>2倍且错误发现率<0.15。根据已知的分子相互作用和反应网络，这些基因的产物主要聚集在丝裂原活化蛋白激酶信号通路、转化生长因子-β信号通路、粘着斑和细胞运动、肌动蛋白聚合激活和细胞周期中。在多个数据库中进行详尽搜索使我们能够在上调基因集中识别基质金属蛋白酶、黑色素瘤抗原A和胶原蛋白基因家族的各种成员。我们还确定了一个仅包含30个转录本的预后分类器，总体准确率为95%。这些发现可能阐明甲状腺肿瘤去分化所涉及的分子机制，并提供潜在的预后预测指标以及新疗法的靶点。
