Danna Huang, Lu Chen, Yu Liu, Wuwei Wu, Yingying Liu, Yu Pan
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引用次数: 0
Abstract
Background: Glioblastoma (GBM) severely disrupts the quality of life of patients. Anoikis represents a significant mechanism in cancer invasion and metastasis. Our study focused on the prognostic relationship between the anoikis-associated gene and GBM and its effect on GBM cell progression.
Methods: We downloaded 656 and 979 GBM sample data from TCGA and CGGA cohort datasets, respectively. Fifteen anoikis-associated genes were obtained from the GeneCards database and were subsequently clustered to identify differential genes associated with them. After LAASO screening, the expression values of the 5 differential genes were the sum of LASSO regression coefficients. Survival analysis and ROC curve analysis of anoikis scores were performed using the TCGA training and CGGA validation sets. The prognostic factors were analyzed using Cox regression analysis in GBM. Moreover, CCK-8, colony formation, wound healing, and transwell assay were used to evaluate GBM cell proliferation and migration.
Results: Significant differences were observed in the 5-year survival of GBM patients between the two subgroups. Then, our analysis demonstrated that high OCIAD2, FTLP3, IGFBP2, and H19 levels were associated with lower 5-year GBM survival rates, whereas high SFRP2 levels were associated with higher survival rates. Univariate Cox analysis indicated that GBM risk was linked to both anoikis score and grade, while multivariate Cox analysis indicated that GBM risk was associated with both anoikis score and age. Additionally, OCIAD2 was highly expressed in U251MG and T98G cells. Moreover, OCIAD2 silencing inhibited GBM cell proliferation and migration.
Conclusion: This study demonstrated the potential of the anoikis-associated gene OCIAD2 as a prognostic biomarker for GBM. Furthermore, we validated in vitro that OCIAD2 promoted GBM cell progression.
期刊介绍:
Combinatorial Chemistry & High Throughput Screening (CCHTS) publishes full length original research articles and reviews/mini-reviews dealing with various topics related to chemical biology (High Throughput Screening, Combinatorial Chemistry, Chemoinformatics, Laboratory Automation and Compound management) in advancing drug discovery research. Original research articles and reviews in the following areas are of special interest to the readers of this journal:
Target identification and validation
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High throughput/high content/in silico screening and associated technologies
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Combinatorial chemistry (e.g. small molecules, peptide, nucleic acid or phage display libraries)
Chemical library design and chemical diversity
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Compound management
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Natural Product Analytical Studies
Bipharmaceutical studies of Natural products
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Laboratory automation, robotics, microfluidics, signal detection technologies
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