EEF1E1 promotes glioma proliferation by regulating cell cycle through PTEN/AKT signaling pathway.

The cell cycle, a pivotal regulator of cell proliferation, can be significantly influenced by the phosphatase and tensin homolog (PTEN)/AKT signaling pathway's modulation of cyclin-related proteins. In our study, we discovered the crucial role of EEF1E1 in this process, as it appears to downregulate PTEN expression. Furthermore, our findings affirmed that EEF1E1 modulates downstream cell cycle-related proteins by suppressing the PTEN/AKT pathway. Cell cycle assay results revealed that EEF1E1 downregulation stunted the advancement of glioma cells in both the G1 and S phases. A suite of assays-Cell Counting Kit-8, colony formation, and ethyl-2'-deoxyuridine-substantiated that the EEF1E1 downregulation markedly curtailed glioma proliferation. We further validated this phenomenon through animal studies and coculture experiments on brain slices. Our comprehensive investigation indicates that EEF1E1 knockdown can effectively inhibit the glioma cell proliferation by regulating the cell cycle via the PTEN/AKT signaling pathway. Consequently, EEF1E1 emerges as a potential therapeutic target for glioma treatment, signifying critical clinical implications.

SOX9 Promotes Collagen VI Secretion by Upregulating PCOLCE in Neurofibroma.

Neurofibromatosis type 1 (NF1) is caused by NF1 gene mutations. Patients with NF1 often have complications with tumors, such as neurofibroma. In order to investigate the pathogenesis of human neurofibroma, a systematic comparison of protein expression levels between Schwann cell-like sNF96.2 cells, which originated from malignant peripheral nerve sheath tumors (MPNST), and normal Schwann cells was performed using 4-D label-free proteomic analysis. In addition, the expression levels and localization of dysregulated proteins were confirmed using a Gene Expression Omnibus (GEO) transcriptomic dataset, Western blot analysis, and immunofluorescence labeling. The effects of SRY-box transcription factor 9 (SOX9) in the neurofibroma and surrounding microenvironment were evaluated in vivo using a tumor transplantation model. The present study observed that SOX9 and procollagen C-endopeptidase enhancer (PCOLCE) were significantly altered. NF1 mutation promoted the nuclear translocation and transcriptional activity of SOX9 in neurofibromas. SOX9 increased collagen VI secretions by enhancing the activation of PCOLCE in neurofibroma cells. These findings might provide new perspectives on the pathophysiological significance of SOX9 in neurofibromas and elucidate a novel molecular mechanism underlying neurofibromas.

Hybrid Surrogate-Based Constrained Optimization With a New Constraint-Handling Method.

Surrogate-based-constrained optimization for some optimization problems involving computationally expensive objective functions and constraints is still a great challenge in the optimization field. Its difficulties are of two primary types. One is how to handle the constraints, especially, equality constraints; another is how to sample a good point to improve the prediction of the surrogates in the feasible region. Overcoming these difficulties requires a reliable constraint-handling method and an efficient infill-sampling strategy. To perform inequality- and equality-constrained optimization of expensive black-box systems, this work proposes a hybrid surrogate-based-constrained optimization method (HSBCO), and the main innovation is that a new constraint-handling method is proposed to map the feasible region into the origin of the Euclidean subspace. Thus, if the constraint violation of an infeasible solution is large, then it is far from the origin in the Euclidean subspace. Therefore, all constraints of the problem can be transformed into an equivalent equality constraint, and the distance between an infeasible point and the origin in the Euclidean subspace represents the constraint violation of the infeasible solution. Based on the distance, the objective function of the problem can be penalized by a Gaussian penalty function, and the original constrained optimization problem becomes an unconstrained optimization problem. Thus, the feasible solutions of the original minimization problem always have a lower objective function value than any infeasible solution in the penalized objective space. To improve the optimization performance, kriging-based efficient global optimization (EGO) is used to find a locally optimal solution in the first phase of HSBCO, and starting from this locally optimal solution, RBF-model-based global search and local search strategies are introduced to seek global optimal solutions. Such a hybrid optimization strategy can help the optimization process converge to the global optimal solution within a given maximum number of function evaluations, as demonstrated in the experimental results on 23 test problems. The method is shown to achieve the global optimum more closely and efficiently than other leading methods.