维维里尼阿匹索氏菌
生物
肝内胆管癌
激光捕获显微切割
奥皮斯托奇斯
阿片吸虫病
基因
肝吸虫
基因表达
病理
微阵列分析技术
微阵列
癌症研究
遗传学
医学
免疫学
蠕虫
作者
Natini Jinawath,Yaovalux Chamgramol,Yoichi Furukawa,Kazutaka Obama,Tatsuhiko Tsunoda,Banchob Sripa,Chawalit Pairojkul,Yusuke Nakamura
出处
期刊:Hepatology
[Lippincott Williams & Wilkins]
日期:2006-09-27
卷期号:44 (4): 1025-1038
被引量:126
摘要
Intrahepatic cholangiocarcinoma (ICC) is the second most common primary cancer in the liver, and its incidence is highest in the northeastern part of Thailand. ICCs in this region are known to be associated with infection with liver flukes, particularly Opisthorchis viverrini (OV), as well as nitrosamines from food. To clarify molecular mechanisms of ICC associated with or without liver flukes, we analyzed gene expression profiles of OV-associated ICCs from 20 Thai patients and compared their profiles with those of 20 Japanese ICCs that were not associated with OV, by means of laser microbeam microdissection and a cDNA microarray containing 27,648 genes. We identified 77 commonly upregulated genes and 325 commonly downregulated genes in the two ICC groups. Unsupervised hierarchical cluster analysis separated the 40 ICCs into two major branches almost completely according to the fluke status. The putative signature of OV-associated ICC exhibited elevated expression of genes involved in xenobiotic metabolism (UGT2B11, UGT1A10, CHST4, SULT1C1), whereas that of non–OV-associated ICC represented enhanced expression of genes related to growth factor signaling (TGFBI, PGF, IGFBP1, IGFBP3). Additional random permutation tests identified a total of 49 genes whose expression levels were significantly different between the two groups. We also identified genes associated with macroscopic type of ICCs. In conclusion, these data may not only contribute to clarification of common and OV-specific mechanisms underlying ICC, but also may serve as a starting point for the identification of novel diagnostic markers or therapeutic targets for the disease. (HEPATOLOGY 2006;44:1025–1038.)
科研通智能强力驱动
Strongly Powered by AbleSci AI