Fibroid Growth Study

在纤维瘤生长研究(FGS)中，我们收集了各种数据，例如随着时间的推移(最多4个时间点)纤维瘤体积的MRI测量，从选择手术的女性获得的肿瘤基因表达微阵列数据等。 这项研究是由我们最近关于绝经前妇女子宫肌瘤生长的出版物(Peddada等人，PNAS，2008和Baird等人，生育力和不孕症，2010)推动的。子宫肌瘤(肌瘤)的研究为研究激素依赖性肿瘤生长和自发性肿瘤消退的生理和分子决定因素提供了独特的机会。我们对患有子宫肌瘤的绝经前妇女进行了一项纵向临床研究，结果显示白人和黑人妇女的生长速度随年龄的不同而显著不同。我们现在报告在这项研究中收集的肿瘤的基因表达数据。用Affymetrix基因芯片表达阵列分析了52例子宫肌瘤和8例子宫肌瘤组织的总RNA。首先比较所有肌瘤和正常子宫肌层之间的基因表达数据，然后根据收集这些肿瘤的女性的年龄和种族，比较定义为快速生长的肌瘤和定义为不生长的肌瘤之间的基因表达数据。在配对比较中发现有意义的基因被进一步分析，以寻找典型的途径、网络和生物功能，使用Invenity Path Analysis(IPA)软件。虽然我们对肌瘤和子宫肌层的比较产生了非常大的基因清单，与之前的许多研究高度相似，但在生长和不生长的肌瘤的比较中发现了不同的基因集和信号通路。其中关键的是与细胞凋亡调控相关的基因。据我们所知，这是第一项从临床研究中比较生长和非生长肿瘤的研究，目的是区分肿瘤生长所特有的分子信号和引起多形性肿瘤表型的分子信号的复杂性。 我们目前正在研究根据肌瘤的大小可能与肌瘤唯一相关的基因和途径。

In the Fibroid Growth Study(FGS) we collected a variety of data, such as MRI measurements of fibroid volumes over time (up to 4 time points), gene expression microarray data obtained on tumors from women who opted for surgery, etc. We are presently analyzing data obtained from that study. This research was motivated by our recent publications (Peddada et al., PNAS, 2008 and Baird et al.,Fertility and Sterility, 2010) regarding growth of fibroids in pre-menopausal women. The study of uterine leiomyomata (fibroids) provides a unique opportunity to investigate the physiological and molecular determinants of hormone dependent tumor growth and spontaneous tumor regression. We conducted a longitudinal clinical study of premenopausal women with fibroids that showed significantly different growth rates between white and black women depending on their age. We now report the gene expression data from tumors collected in this study. Total RNA from 52 fibroid and 8 myometrial samples were analyzed using Affymetrix Gene Chip expression arrays. Gene expression data was first compared between all fibroids and normal myometrium and then between fibroids defined as rapidly growing and fibroids defined as non-growing based on age and race of the women from which these tumors were collected. Genes that were found significant in pairwise comparisons were further analyzed for canonical pathways, networks and biological functions using the Ingenuity Pathway Analysis (IPA) software. Whereas our comparison of leiomyoma to myometrium produced a very large list of genes highly similar to numerous previous studies, distinct sets of genes and signaling pathways were identified in comparisons of growing and non-growing fibroids. Key among these were genes associated with regulation of apoptosis. To our knowledge, this is the first study to compare growing and non-growing tumors from a clinical study in order to differentiate the molecular signals specific for tumor growth from the complexity of molecular signals that give rise to pleomorphic tumor phenotypes. We are currently investigating genes and pathways that may be uniquely associated with fibroids according to their size.