Characterizing the invasive tumor front of aggressive uterine adenocarcinoma and leiomyosarcoma
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PROVIDER: PRJNA718540 | ENA |
REPOSITORIES: ENA
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Project description:Characterizing the invasive tumor front of aggressive uterine adenocarcinoma and leiomyosarcoma
| PRJNA721868 | ENA
Project description:Invasive tumor front (the tumor-host interface) is vitally important in malignant cell progression and metastasis. Tumor cell interactions with resident and infiltrating host cells and with the surrounding extracellular matrix and secreted factors ultimately determine the fate of the tumor. In this study we focus on the invasive tumor front, making an in-depth characterization of reticular fiber scaffolding, infiltrating immune cells, gene expression and methylation profiles of classified aggressive primary uterine adenocarcinomas and leiomyosarcomas.
2021-06-28 | GSE171142 | GEO
Project description:Invasive tumor front (ITF, the tumor-host interface) is vitally important in malignant cell progression and metastasis. Tumor cell interactions with resident and infiltrating host cells and with the surrounding extracellular matrix and secreted factors ultimately determine the fate of the tumor. Herein we focus on the invasive tumor front, making an in-depth characterization of reticular fiber scaffolding, infiltrating immune cells, gene expression and epigenetic profiles of classified aggressive primary uterine adenocarcinomas (uADC) and leiomyosarcomas (uLMS).
2021-04-15 | GSE172043 | GEO
Project description:The invasive tumor front (the tumor-host interface) is vitally important in malignant cell progression and metastasis. Tumor cell interactions with resident and infiltrating host cells and with the surrounding extracellular matrix and secreted factors ultimately determine the fate of the tumor. Herein we focus on the invasive tumor front, making an in-depth characterization of reticular fiber scaffolding, infiltrating immune cells, gene expression, and epigenetic profiles of classified aggressive primary uterine adenocarcinomas (24 patients) and leiomyosarcomas (11 patients). Sections of formalin-fixed samples before and after microdissection were scanned and studied. Reticular fiber architecture and immune cell infiltration were analyzed by automatized algorithms in colocalized regions of interest. Despite morphometric resemblance between reticular fibers and high presence of macrophages, we found some variance in other immune cell populations and distinctive gene expression and cell adhesion-related methylation signatures. Although no evident overall differences in immune response were detected at the gene expression and methylation level, impaired antimicrobial humoral response might be involved in uterine leiomyosarcoma spread. Similarities found at the invasive tumor front of uterine adenocarcinomas and leiomyosarcomas could facilitate the use of common biomarkers and therapies. Furthermore, molecular and architectural characterization of the invasive front of uterine malignancies may provide additional prognostic information beyond established prognostic factors.
| S-EPMC8209546 | biostudies-literature
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: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. 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: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: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
Project description:Soft-tissue sarcomas are group of rare, tremendously heterogeneous, and highly aggressive malignancies. Characterizing inter-tumor heterogeneity is crucial for selecting suitable sarcoma therapy, as the presence of diverse molecular subgroups of patients can be associated with disease outcome or response to treatment. While cancer subtypes are often characterized by differences in gene expression, the mechanisms that drive these differences are generally unknown. We therefore set out to model the regulatory mechanisms driving sarcoma heterogeneity. We subtyped soft-tissue sarcomas based on patient-specific, genome-wide gene regulatory networks and found pronounced regulatory heterogeneity in leiomyosarcoma—one of the most common soft-tissue sarcomas subtypes that arises in smooth muscle tissue.
2023-07-25 | GSE218533 | GEO