Fibroid Growth Study

在纤维瘤生长研究（FGS）中，我们收集了各种数据，如随时间推移（最多4个时间点）纤维瘤体积的MRI测量，选择手术的女性肿瘤的基因表达微阵列数据等。 这项研究的动机是我们最近的出版物（Peddada等人，PNAS，2008年和Baird等人，Fertility and Sterility，2010）关于绝经前妇女纤维瘤生长的研究。子宫平滑肌瘤（肌瘤）的研究为研究激素依赖性肿瘤生长和自发肿瘤消退的生理和分子决定因素提供了独特的机会。我们进行了一项纵向临床研究绝经前妇女肌瘤显示显着不同的生长速度之间的白色和黑人妇女取决于他们的年龄。 我们现在报告本研究中收集的肿瘤基因表达数据。 使用Affyssin基因芯片表达阵列分析来自52个子宫肌瘤和8个子宫肌层样品的总RNA。 基因表达数据首先在所有肌瘤和正常子宫肌层之间进行比较，然后根据收集这些肿瘤的女性的年龄和种族在定义为快速生长的肌瘤和定义为非生长的肌瘤之间进行比较。在成对比较中发现显著的基因使用Inconsistency Pathway Analysis（IPA）软件进一步分析经典途径、网络和生物学功能。虽然我们比较子宫肌瘤和子宫肌层产生了一个非常大的基因列表，与许多以前的研究高度相似，但在生长和非生长肌瘤的比较中确定了不同的基因和信号通路。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，以区分肿瘤生长特异性的分子信号与引起多形性肿瘤表型的分子信号的复杂性。 我们目前正在研究基因和途径，可能是唯一相关的肌瘤根据其大小。

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.