Dataset Information

Comparison of Gene Expression in Uterine Smooth Muscle Tumors

ABSTRACT: Comparison of Gene Expression in Uterine Smooth Muscle Tumors (including normal myometrium, benign uterine leiomyoma, malignant uterine and extra-uterine leiomyosarcoma. Total RNA was isolated with Trizol and converted to labeled cRNA by standard Affymetrix protocols. Before analysis, Avg Diff values were normalized to a sum of 3 million for each probe set on the chip and values less than 20 were adjusted to 20 to allow log scaling in subsequent analysis. Keywords: other

ORGANISM(S): Homo sapiens

PROVIDER: GSE764 | GEO | 2004/01/01

SECONDARY ACCESSION(S): PRJNA87717

REPOSITORIES: GEO

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Project description:Objective: The objective of this study was to estimate the accuracy of transcriptome-based classifier in differential diagnosis of uterine leiomyoma and leiomyosarcoma. Methods: We manually selected 114 normal uterine tissue and 31 leiomyosarcoma samples from publicly available transcriptome data in UCSC Xena as training/validation sets. We developed pre-processing procedure and gene selection method to sensitively find genes of larger variance in leiomyosarcoma than normal uterine tissues. Through our method, twenty genes were selected to build transcriptome-based classifier. The prediction accuracies of deep feedforward neural network (DNN), support vector machine (SVM), Random Forest (RF), and Gradient Boosting (GB) models were examined. We interpret the biological functionality of selected genes via network-based analysis using Gene-Mania. To validate the performance of trained model, we additionally collected 35 clinical samples of leiomyosarcoma and leiomyoma as a test set (18 + 17 as 1st and 2nd test sets). Results: We discovered genes expressed in a highly variable way in leiomyosarcoma while these genes are expressed in a conserved way in normal uterine samples. These genes were mainly associated with DNA replication, cell cycle, and DNA damage checkpoint. Among evaluated machine learning classifiers, the DNN had the highest accuracy and average AUC value in training data set. As gene selection and model training were made in leiomyosarcoma and uterine normal tissue, proving discriminant of ability between leiomyosarcoma and leiomyoma is necessary. Thus, further validation of trained model was conducted in newly collected clinical samples of leiomyosarcoma and leiomyoma. The DNN classifier performed AUC of 0.917 and 0.914 supporting that the selected genes in conjunction with DNN classifier are well discriminating the difference between leiomyosarcoma and leiomyoma in clinical sample. Conclusion: The transcriptome-based classifier accurately distinguished uterine leiomyoma from leiomyosarcoma.

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)

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Comparison of Gene Expression in Uterine Smooth Muscle Tumors

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