Project description:Gene expression profiling of immortalized human mesenchymal stem cells with hTERT/E6/E7 transfected MSCs. hTERT may change gene expression in MSCs. Goal was to determine the gene expressions of immortalized MSCs.
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.
Project description:Transcriptional profiling of human mesenchymal stem cells comparing normoxic MSCs cells with hypoxic MSCs cells. Hypoxia may inhibit senescence of MSCs during expansion. Goal was to determine the effects of hypoxia on global MSCs gene expression.
Project description:Gene expression profiling of immortalized human mesenchymal stem cells with hTERT/E6/E7 transfected MSCs. hTERT may change gene expression in MSCs. Goal was to determine the gene expressions of immortalized MSCs. One-condition experment, gene expression of 3A6
Project description:We have sequenced miRNA libraries from human embryonic, neural and foetal mesenchymal stem cells. We report that the majority of miRNA genes encode mature isomers that vary in size by one or more bases at the 3’ and/or 5’ end of the miRNA. Northern blotting for individual miRNAs showed that the proportions of isomiRs expressed by a single miRNA gene often differ between cell and tissue types. IsomiRs were readily co-immunoprecipitated with Argonaute proteins in vivo and were active in luciferase assays, indicating that they are functional. Bioinformatics analysis predicts substantial differences in targeting between miRNAs with minor 5’ differences and in support of this we report that a 5’ isomiR-9-1 gained the ability to inhibit the expression of DNMT3B and NCAM2 but lost the ability to inhibit CDH1 in vitro. This result was confirmed by the use of isomiR-specific sponges. Our analysis of the miRGator database indicates that a small percentage of human miRNA genes express isomiRs as the dominant transcript in certain cell types and analysis of miRBase shows that 5’ isomiRs have replaced canonical miRNAs many times during evolution. This strongly indicates that isomiRs are of functional importance and have contributed to the evolution of miRNA genes