ABSTRACT
Background: Glioblastoma (GBM), one of the most prevalent brain tumor types, is correlated with an extremely poor prognosis. The extracellular matrix (ECM) genes could activate many crucial pathways that facilitate tumor development. This study aims to provide online models to predict GBM survival by ECM genes. Methods: The associations of ECM genes with the prognosis of GBM were analyzed, and the significant prognosis-related genes were used to develop the ECM index in the CGGA dataset. Furthermore, the ECM index was then validated on three datasets, namely, GSE16011, TCGA-GBM, and GSE83300. The prognosis difference, differentially expressed genes, and potential drugs were obtained. Multiple machine learning methods were selected to construct the model to predict the survival status of GBM patients at 6, 12, 18, 24, 30, and 36 months after diagnosis. Results: Five ECM gene signatures (AEBP1, F3, FLNC, IGFBP2, and LDHA) were recognized to be associated with the prognosis. GBM patients were divided into high- and low-ECM index groups with significantly different overall survival rates in four datasets. High-ECM index patients exhibited a worse prognosis than low-ECM index patients. Four small molecules (podophyllotoxin, lasalocid, MG-262, and nystatin) that might reduce GBM development were predicted by the Cmap dataset. In the independent dataset (GSE83300), the maximum values of prediction accuracy at 6, 12, 18, 24, 30, and 36 months were 0.878, 0.769, 0.748, 0.720, 0.705, and 0.868, respectively. These machine learning models were provided on a publicly accessible, open-source website (https://ospg.shinyapps.io/OSPG/). Conclusion: In summary, our findings indicated that ECM genes were prognostic indicators for patient survival. This study provided an online server for the prediction of survival curves of GBM patients.
ABSTRACT
BACKGROUND: Graphene-related materials have good biocompatibility and can improve cartilage repair. At the same time, their excellent mechanical strength and electrical conductivity make them promising as cartilage replacement materials, which have been widely used in tissue engineering. OBJECTIVE: To review the general properties, biocompatibility and application of graphene in cartilage tissue engineering and cartilage repair. METHODS: A computer-based online search of CNKI and PubMed databases was performed using the search terms “graphene, tissue engineering, biocompatibility, cartilage” in Chinese and English to search related literatures published between January 2000 and January 2019. Preliminary screening was conducted by reading the titles and abstracts to exclude the literature irrelevant to the theme of the paper. According to inclusion and exclusion criteria, 67 literatures were included in the final analysis. RESULTS AND CONCLUSION: Graphene has good biocompatibility, and has low cytotoxicity to prokaryotic cells and eukaryotic cells, but the cytotoxicity can be further reduced by chemical modification or surface modification, so as not to affect the growth of cells. Graphene and its derivatives can promote the growth and chondrogenic differentiation of human bone marrow mesenchymal stem cells, as well as the proliferation and maturation of chondrocytes, and accelerate the repair of cartilage defects. Due to its mechanical strength and electrical conductivity, graphene can compound biomimetic cartilage material, which is suitable for cartilage tissue engineering. Graphene has several unresolved problems and challenges, but the application potential of graphene-related materials may pave the way for future breakthroughs in tissue engineering research.
