Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Transcription profiling of human Gene Expression in Uterine Smooth Muscle Tumors (including normal myometrium, benign uterine leiomyoma, malignant uterine and extra-uterine leiomyosarcoma)

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.

ORGANISM(S): Homo sapiens

SUBMITTER: Bradley Quade

PROVIDER: E-GEOD-764 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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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.

Dataset Information

Transcription profiling of human Gene Expression in Uterine Smooth Muscle Tumors (including normal myometrium, benign uterine leiomyoma, malignant uterine and extra-uterine leiomyosarcoma)

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