Differential diagnosis between uterine leiomyoma and leiomyosarcoma using transcriptome analysis
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ABSTRACT:
PROVIDER: PRJNA917504 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR22954939_1.fastq.gz | Fastqsanger.gz | |||
| SRR22954939_2.fastq.gz | Fastqsanger.gz | |||
| SRR22954940_1.fastq.gz | Fastqsanger.gz | |||
| SRR22954940_2.fastq.gz | Fastqsanger.gz | |||
| SRR22954941_1.fastq.gz | Fastqsanger.gz |
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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.
2023-12-13 | GSE222045 | 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.
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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:The objective of this study was to estimate the accuracy of transcriptome-based classifier in differential diagnosis of uterine leiomyoma and leiomyosarcoma. 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, 17 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 GeneMANIA. 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). 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. 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 sensitivity 0.88, 0.77 (8/9, 7/9) while the specificity 1.0 (8/8, 8/8) in two test data set supporting that the selected genes in conjunction with DNN classifier are well discriminating the difference between leiomyosarcoma and leiomyoma in clinical sample. The transcriptome-based classifier accurately distinguished uterine leiomyosarcoma from leiomyoma. Our method can be helpful in clinical practice through the biopsy of sample in advance of surgery. Identification of leiomyosarcoma let the doctor avoid of laparoscopic surgery, thus it minimizes un-wanted tumor spread.
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Project description:Genomic and Histologic Analysis of Uterine Leiomyosarcoma from Leiomyoma with Bizarre Nuclei
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Project description:Uterine leiomyomas, or fibroids, represent the most common benign tumor of the female reproductive tract. Fibroids become symptomatic in 30% of all women and up to 70% of African American women of reproductive age. Epigenetic dysregulation of individual genes has been demonstrated in leiomyoma cells; however, the in vivo genome-wide distribution of such epigenetic abnormalities, however, remains unknown. Principal Findings: We characterized and compared genome-wide DNA methylation and mRNA expression profiles in uterine leiomyoma and matched adjacent normal myometrial tissues from 18 African-American women. We found 55 genes with differential promoter methylation and concominant differences in mRNA expression in uterine leiomyoma versus normal myometrium. Eighty percent of the identified genes showed an inverse relationship DNA methylation status and mRNA expression in uterine leiomyoma tissues, and the majority of genes (62%) displayed hypermethylation associated with gene silencing. We selected two genes, the known tumor suppressors KLF11 and DLEC1, and verified promoter hypermethylation and mRNA repression using bisulfite sequencing and real-time PCR. Incubation of primary leiomyoma smooth muscle cells with a DNA methyltransferase inhibitor restored KLF11 and DLEC1 mRNA levels.
2012-04-30 | GSE31699 | GEO
Project description:Uterine leiomyomas, or fibroids, represent the most common benign tumor of the female reproductive tract. Fibroids become symptomatic in 30% of all women and up to 70% of African American women of reproductive age. Epigenetic dysregulation of individual genes has been demonstrated in leiomyoma cells; however, the in vivo genome-wide distribution of such epigenetic abnormalities, however, remains unknown. Principal Findings: We characterized and compared genome-wide DNA methylation and mRNA expression profiles in uterine leiomyoma and matched adjacent normal myometrial tissues from 18 African-American women. We found 55 genes with differential promoter methylation and concominant differences in mRNA expression in uterine leiomyoma versus normal myometrium. Eighty percent of the identified genes showed an inverse relationship DNA methylation status and mRNA expression in uterine leiomyoma tissues, and the majority of genes (62%) displayed hypermethylation associated with gene silencing. We selected two genes, the known tumor suppressors KLF11 and DLEC1, and verified promoter hypermethylation and mRNA repression using bisulfite sequencing and real-time PCR. Incubation of primary leiomyoma smooth muscle cells with a DNA methyltransferase inhibitor restored KLF11 and DLEC1 mRNA levels. paired LM and MM tissue samples
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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.
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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.
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