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Background: Colon cancer (CC) is one of the most common cancers with high morbidity globally. Ubiquitination is involved in the characterization of multiple biological processes, and some ubiquitinated enzymes are associated with the prognosis of CC. However, the prognostic model associated with ubiquitination-related genes (URGs) for CC is unavailable. Methods: Gene expression data, somatic mutations, transcriptome profiles, microsatellite instability status (MSI) status, and clinical information for CC were obtained from The Cancer Genome Atlas (TCGA) dataset. Seven URGs were used for establishing a prognostic prediction model, which was constructed and validated in GSE17538. Besides, genomic variance analysis (GSVA) was used to explore further the differences in biological pathway activation status between the high-risk and low-risk groups. Finally, the single-sample gene set enrichment analysis (ssGSEA) and ESTIMATE algorithm analysis were used to characterize the cellular infiltration in the microenvironment. Results: A seven-URG prognostic signature was established, based on which patients in the training and test groups could be divided into high-risk and low-risk groups. The results demonstrated that the model has a solid ability to predict the prognosis of CC patients. Conclusions: We established a prognostic prediction model for CC based on ubiquitination. Then we analyzed the genetic characteristics associated with ubiquitination and the tumor microenvironment (TME) cell infiltration in CC. These results are worthy of exploring new clinical treatment strategies for CC.
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Background: Gastric cancer (GC) is a multifactorial progressive disease with high mortality and heterogeneous prognosis. Effective prognostic biomarkers for GC were critically needed. Hippo signaling pathway is one of the critical mechanisms regulating the occurrence and development of GC, and has potential clinical application value for the prognosis and treatment of GC patients. However, there is no effective signature based on Hippo signaling pathway-related genes (HSPRGs) to predict the prognosis and treatment response of GC patients. Our study aimed to build a HSPRGs signature and explore its performance in improving prognostic assessment and drug therapeutic response in GC. Methods: Based on gene expression profiles obtained from The Cancer Genome Atlas (TCGA) database, we identified differentially expressed HSPRGs and conducted univariate and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis to construct a multigene risk signature. Subsequently, the Kaplan-Meier curve and receiver operating characteristic (ROC) were performed to evaluate the predictive value of the risk signature in both training and validation cohort. Furthermore, we carried out univariate and multivariate Cox regression analysis to investigate the independent prognostic factors and establish a predictive nomogram. The enriched signaling pathways in risk signature were analyzed by gene set enrichment analysis (GSEA). Tumor immune dysfunction and exclusion (TIDE) and drug sensitivity analysis were performed to depict therapeutic response in GC. Results: In total, 38 differentially expressed HSPRGs were identified, and final four genes (DLG3, TGFB3, TGFBR1, FZD6) were incorporated to build the signature. The ROC curve with average 1-, 3-, and 5-year areas under the curve (AUC) equal to .609, .634, and .639. Clinical ROC curve revealed that risk signature was superior to other clinicopathological factors in predicting prognosis. Calibration curves and C-index (.655) of nomogram showed excellent consistency. Besides, in the immunotherapy analysis, exclusion (p < 2.22 × 10-16) and microsatellite instability (p = .0058) performed significantly differences. Finally, our results suggested that patients in the high-risk group were more sensitive to specific chemotherapeutic agents. Conclusion: Results support the hypothesis that Hippo-related signature is a novel prognostic biomarker and predictor, which could help optimize GC prognostic stratification and inform clinical medication decisions.
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BACKGROUND: Human umbilical cord mesenchymal stem cells (hUCMSCs) are a type of pluripotent stem cell which are isolated from the umbilical cord of newborns. hUCMSCs have great therapeutic potential. We designed this experimental study in order to investigate whether the transplantation of hUCMSCs can improve the ovarian reserve function of perimenopausal rats and delay ovarian senescence. METHOD: We selected naturally aging rats confirmed by vaginal smears as models of perimenopausal rats, divided into the control group and the treatment group, and selected young fertile female rats as normal controls. hUCMSCs were transplanted into rats of the treatment group through tail veins. Enzyme-linked immunosorbent assay (ELISA) detected serum levels of sex hormones, H&E staining showed ovarian tissue structure and allowed follicle counting, immunohistochemistry and western blot analysis revealed ovarian expression of hepatocyte growth factor (HGF), vascular endothelial cell growth factor (VEGF), and insulin-like growth factor-1 (IGF-1), polymerase chain reaction (PCR) and western blot analysis revealed hUCMSCs expression of HGF, VEGF, and IGF-1. RESULTS: At time points of 14, 21, and 28 days after hUCMSCs transplantation, estradiol (E2) and anti-Müllerian hormone (AMH) increased while follicle-stimulating hormone (FSH) decreased; ovarian structure improved and follicle number increased; ovarian expression of HGF, VEGF, and IGF-1 protein elevated significantly. Meanwhile, PCR and western blot analysis indicated hUCMSCs have the capacity of secreting HGF, VEGF, and IGF-1 cytokines. CONCLUSIONS: Our results suggest that hUCMSCs can promote ovarian expression of HGF, VEGF, and IGF-1 through secreting those cytokines, resulting in improving ovarian reserve function and withstanding ovarian senescence.