Dataset Information

Integrated epigenome, exome and transcriptome analyses reveal molecular subtypes and homeotic transformation in uterine fibroids [DNA methylation]

ABSTRACT: Uterine fibroids are benign myometrial smooth muscle tumors of unknown etiology that when symptomatic are the most common indication for hysterectomy in the USA. We conducted an integrated analysis of fibroids and adjacent normal myometria by whole exome sequencing, DNA methylation (Human Methylation EPIC) array, and RNA-sequencing. Unsupervised clustering by DNA methylation segregated normal myometria and fibroids, and further separated the fibroids into subtypes marked by MED12 mutation, HMGA2 activation (HMGA2hi) and HMGA1 activation (HMGA1hi). Upregulation of HMGA2 expression in HMGA2hi fibroids did not always appear to be dependent on translocation, as has been historically described, and was associated with hypomethylation in the HMGA2 gene body. Furthermore, we found that expression of HOXA13 was highly upregulated in fibroids and that overexpression of HOXA13 in a myometrial cell line induced expression of genes classically associated with uterine fibroids. Transcriptome analyses of the most differentially expressed genes between cervix and myometrium also showed that uterine fibroids and normal cervix clustered together and apart from normal myometria. Together, our integrated analysis shows a role for epigenetic modification in fibroid biology and strongly suggests that homeotic transformation of myometrium cells to a more cervical phenotype is important for the etiology of the disease.

ORGANISM(S): Homo sapiens

PROVIDER: GSE120854 | GEO | 2018/10/05

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Uterine leiomyomata, or fibroids, are benign tumors of the uterine myometrium that significantly affect up to 30% of reproductive-age women. Despite being the primary cause of hysterectomy in the United States, accounting for up to 200,000 procedures annually, the etiology of leiomyoma remains largely unknown. Due to the lack of an effective medicinal therapy for these tumors, this disease continues to have a tremendous negative impact on women’s health. As a basis for understanding leiomyoma pathogenesis and identifying targets for pharmacotherapy, we conducted transcriptional profiling of leiomyoma and unaffected myometrium from humans and Eker rats, the best characterized preclinical model of leiomyoma. A global comparison of mRNA from leiomyoma versus myometrium in human and rat identified a highly significant overlap of dysregulated gene expression in leiomyoma. An unbiased pathway analysis using a method of gene set enrichment based on the Sigpathway algorithm detected the mammalian target of rapamycin (mTOR) pathway as one of the most highly upregulated pathways in both human and rat tumors. Activation of this pathway was confirmed in both human and rat leiomyomata at the protein level via Western. Inhibition of mTOR in female Eker rats with the rapamycin analog WAY-129327 for 2 weeks decreased mTOR signaling and cell proliferation in tumors, and treatment for 4 months significantly decreased tumor incidence, multiplicity and size. These results identify dysregulated mTOR signaling as a component of leiomyoma etiology across species and directly demonstrate the dependence of these tumors on mTOR signaling for growth in the Eker rat. Modulation of this pathway warrants additional investigation as a potential therapy for uterine leiomyoma. Experiment Overall Design: We analyzed 1-3 leiomyoma or normal myometrium biopsies from each 23 woman undergoing hysterectomy for the treatment of uterine fibroids. tment and compared it leiomyoma and normal myometrium from the Eker rat model of uterine fibroids (N=14-15)

Dataset Information

Integrated epigenome, exome and transcriptome analyses reveal molecular subtypes and homeotic transformation in uterine fibroids [DNA methylation]

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets