Level II, meta-analysis and systematic review.

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Reports of the ameliorative effect of angelicin on sex hormone deficiency-induced osteoporosis have highlighted this compound as a candidate for the treatment of osteoporosis. However, the molecular mechanisms of action of angelicin on osteoblast differentiation have not been thoroughly researched. The aim of the present study was to evaluate the effect of angelicin on the proliferation, differentiation and mineralization of rat calvarial osteoblasts using a Cell Counting Kit-8, alkaline phosphatase activity and the expression of osteogenic genes and proteins. Treatment with angelicin promoted the proliferation, matrix mineralization and upregulation of osteogenic marker genes including collagen type I α 1 and bone γ-carboxyglutamate in fetal rat calvarial osteoblasts. Furthermore, angelicin promoted the expression of β-catenin and runt related transcription factor 2, which serve a vital role in the Wnt/β-catenin signaling pathway. Consistently, the osteogenic effect of angelicin was attenuated by the use of a Wnt inhibitor. Moreover, angelicin increased the expression of estrogen receptor α (ERα), which also serves a key role in osteoblast differentiation. Taken together, these results demonstrated that angelicin may promote osteoblast differentiation through activation of ERα and the Wnt/β-catenin signaling pathway.

Background Pancreatic cancer (PC) is one of the most lethal and aggressive cancer malignancies. The lethality of PC is associated with delayed diagnosis, presence of distant metastasis, and its easy relapse. It is known that clinical treatment decisions are still mainly based on the clinical stage and pathological grade, which are insufficient to determine an appropriate treatment. Considering the significant heterogeneity of PC biological characteristics, the current clinical classificatory pattern relying solely on classical clinicopathological features identification needs to be urgently improved. In this study, we conducted in-depth analyses to establish prognosis-related molecular subgroups based on DNA methylation signature. Results DNA methylation, RNA sequencing, somatic mutation, copy number variation, and clinicopathological data of PC patients were obtained from The Cancer Genome Atlas (TCGA) dataset. A total of 178 PC samples were used to develop distinct molecular subgroups based on the 4227 prognosis-related CpG sites. By using consensus clustering analysis, four prognosis-related molecular subgroups were identified based on DNA methylation. The molecular characteristics and clinical features analyses based on the subgroups offered novel insights into the development of PC. Furthermore, we built a risk score model based on the expression data of five CpG sites to predict the prognosis of PC patients by using Lasso regression. Finally, the risk score model and other independent prognostic clinicopathological information were integrative utilised to construct a nomogram model. Conclusion Novel prognosis-related molecular subgroups based on the DNA methylation signature were established. The specific five CpG sites model for PC prognostic prediction and the derived nomogram model are effective and intuitive tools. Moreover, the construction of molecular subgroups based on the DNA methylation data is an innovative complement to the traditional classification of PC and may contribute to precision medicine development, therapeutic efficacy prediction, and clinical decision guidance.

contributed equally to this study.

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