Genomic and Histologic Analysis of Uterine Leiomyosarcoma from Leiomyoma with Bizarre Nuclei
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1128661 | ENA |
REPOSITORIES: ENA
Similar Datasets
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.
2023-12-13 | GSE222045 | GEO
Project description:Leiomyoma with bizarre nuclei (LM-BN) is a rare variant of leiomyoma with a benign clinical course. In contrast, leiomyosarcoma (LMS) is a high-grade, malignant neoplasm characterized by high recurrence rates and poor survival. While LM-BN and LMS show distinct morphologies, they share similar immunoprofile and molecular alterations, with both considered “DNA unstable”. Rare cases of LM-BN associated with LMS have been reported, however the histogenesis and molecular relationship between these two tumors remains unclear. In this study, we assessed 11 cases of LMS arising in conjunction with LM-BN and further analyzed the clinical, histologic, and molecular characteristics of these lesions. Tumor slides were examined by histology and immunohistochemistry to confirm the presence of both LMS and LM-BN components. LM-BN and LMS had similar p16 and p53 IHC patterns, but LMS had a higher Ki-67 index and lower ER/PR expression. Digital image analysis based on nuclear and cytologic features revealed spatial relationships between LMS and LM-BN. Genomic copy number alteration (CNA) demonstrated the same clonal origin of LMS from existing LM-BN through conserved copy number alterations. LMS harbored higher CNAs and frequent loss of the TP53, Rb, PTEN, and/or CDKN2A genomic region than LM-BN, indicative of tumor progression. Spatial transcriptome analysis defined uniquely expressed gene signatures in a geographical distribution, demonstrating molecular evidence of regional cell specific differences of LM-BN and LMS. Mutation analysis of large oncogenic panel revealed many shared functional gene alterations in both LM-BN and LMS, but more highly enriched in LMS. Our findings for the first time suggest that a subset of LMS arise from an existing LM-BN.
2024-11-05 | GSE270865 | GEO
Project description: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.
2007-09-25 | E-GEOD-764 | biostudies-arrayexpress
Project description:Uterine leiomyosarcoma is a most aggressive gynecological malignancy, and uterine leiomyoma is a benign tumor. Here, we performed bulk RNA-seq using archival fresh-frozen tumor samples stored at the National Cancer Center Biobank.
2022-06-30 | GSE185543 | GEO
Project description:Uterine leiomyosarcoma (ULMS) is a poorly understood gynecologic cancer with few effective treatments. This study explores molecular events involved in ULMS with the goal of identifying strategies. Genome-wide transcriptional profiling were used to compare clinically well-annotated specimens of myometrium, leiomyoma and leiomyosarcoma.
2012-04-26 | E-GEOD-36610 | biostudies-arrayexpress
Project description:Differential diagnosis between uterine leiomyoma and leiomyosarcoma using transcriptome analysis
| PRJNA917504 | ENA
Project description:Global protein coding gene and miRNA expression profiling studies in uterine leiomyoma showed their aberrant expression and involvement in the pathogenesis of uterine leiomyoma. But the global expression patterns and potential clinical value of long noncoding RNA (lncRNA) in uterine leiomyoma have not been explored.In this study, we performed lncRNA expression profiles analysis of uterine leiomyoma using microarray(Arraystar Human LncRNA Array v2.0) to evaluate the genome-wide expression of lncRNAs and mRNAs and their potential role in the pathogenesis of uterine leiomyoma.
2014-08-14 | GSE52618 | GEO
Project description:Global protein coding gene and miRNA expression profiling studies in uterine leiomyoma showed their aberrant expression and involvement in the pathogenesis of uterine leiomyoma. But the global expression patterns and potential clinical value of long noncoding RNA (lncRNA) in uterine leiomyoma have not been explored.In this study, we performed lncRNA expression profiles analysis of uterine leiomyoma using microarray(Arraystar Human LncRNA Array v2.0) to evaluate the genome-wide expression of lncRNAs and mRNAs and their potential role in the pathogenesis of uterine leiomyoma. Expression profiling analysis of the 15 samples including 5 large fibroids, 5 small fibroids and 5 matched myometrium by Arraystar Human LncRNA Array v2.0.
2014-08-14 | E-GEOD-52618 | biostudies-arrayexpress
Project description:To investigate molecular biological features of uterine leiomyosarcoma, microRNA and mRNA sequencing were performed. Here, we provide the microRNA sequencing data, and the mRNA data is GSE185543.
2022-06-30 | GSE200716 | GEO
Project description:Uterine leiomyosarcoma (ULMS) is a poorly understood gynecologic cancer with few effective treatments. This study explores molecular events involved in ULMS with the goal of identifying strategies.
2012-04-27 | GSE36610 | GEO