Project description:miRNA expression profiling between GIST and leiomyoma specimens taken by Tunneling Bloc Biopsy Nine GIST patients and seven gastric leiomyoma patients underwent endoscopic biopsy called Tunnel Block Biopsy. MiRNAs were extracted from the tissues, then.
Project description:Gastrointestinal stromal tumors (GIST) are phenotypically and clinically heterogeneous mesenchymal tumors. Using the cDNA array technique, we analyzed the gene expression profiles of 22 GIST and 7 non-neoplastic gastrointestinal smooth muscle specimens, in order to detect molecular differences between GIST and non-neoplastic tissue, and to detect differences between GIST of various phenotypic and clinical subgroups. As a result, we found 796 differentially expressed genes and ESTs between GIST and smooth muscle tissue, including promising new candidate genes for the pathogenesis of GIST. Furthermore, we identified differences in gene expression between GIST of different site, size, and immunohistochemical expression of CD34 and SMA. Our data show that alterations in gene expression are associated with morphologically and clinically detectable features of GIST and provide new aspects for the understanding of these tumors. Keywords = Gastrointestinal Stromal Tumor (GIST) Keywords: other
Project description:Gastrointestinal stromal tumors (GIST) are phenotypically and clinically heterogeneous mesenchymal tumors. Using the cDNA array technique, we analyzed the gene expression profiles of 22 GIST and 7 non-neoplastic gastrointestinal smooth muscle specimens, in order to detect molecular differences between GIST and non-neoplastic tissue, and to detect differences between GIST of various phenotypic and clinical subgroups. As a result, we found 796 differentially expressed genes and ESTs between GIST and smooth muscle tissue, including promising new candidate genes for the pathogenesis of GIST. Furthermore, we identified differences in gene expression between GIST of different site, size, and immunohistochemical expression of CD34 and SMA. Our data show that alterations in gene expression are associated with morphologically and clinically detectable features of GIST and provide new aspects for the understanding of these tumors. Keywords = Gastrointestinal Stromal Tumor (GIST)
Project description:In this work, we undertake a comparative mass spectrometry-based proteomic analysis of intravenous leiomyoma (IVLM) and other smooth muscle tumours (uterine leiomyoma (uLM), soft tissue leiomyoma (stLM) and benign metastatic keiomyoma (BML). By utilising sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS), we quantified >2,400 proteins from FFPE samples and demonstrate that at the protein level, IVLM is characterised by the unique co-regulated expression of splicing factors that comprise the spliceosome.
Project description:Gastrointestinal stromal tumors (GISTs) have an impaired gene expression. As microRNAs (miRNAs) are involved in post-transcriptional regulation of gene expression we performed the first high-throughput miRNA profiling of 15 paired GIST formalin-fixed and paraffin-embedded samples (tumor and adjacent normal tissue) using small RNA sequencing approach.
Project description:Affymetrix GeneChip miRNA 3.0 microarrays were compared in gingival tissue biopsy samples from obese and normal weight patients with periodontitis
Project description:A growing body of literature has consolidated the important role of miRNA in a variety of biological processes, in cancer development, acting both as oncogenes and tumor suppressor genes, and in their ability to distinguish tumors according to their diagnostic and prognostic properties.To date, little is known, however, about differences in miRNA expression between KIT/PDGFRA mutant and KIT/PDGFRA WT GIST.
Project description:Purpose: The goals of this study are to characterize the transcriptional dysregulation of GIST. More importantly, the transcriptome differences betweeen Imatinib-sensitive and resistant patients are compared to identify RNAs that may impact Imatinib resistance. Methods: High-throughput RNA sequencing (RNA-seq) was employed to capture the transcriptional changes in GIST compared to normal samples. RSEM was used to quantify gene expression and DESeq2 was utilized to compared expression changes between different sample groups.
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.
