HPCAL1 promotes glioblastoma proliferation via activation of Wnt/ß-catenin signalling pathway.

Glioblastoma (GBM) is the most prevalent primary malignancy of the central nervous system with obvious aggressiveness, and is associated with poor clinical outcome. Studies have indicated that calcium ion (Ca2+ ) can positively regulate the initiation of malignancy with regard to GBM by modulating quiescence, proliferation, migration and maintenance. Hippocalcin like-1 protein (HPCAL1) serves as a sensor of Ca2+ . However, the understanding of HPCAL1 activity in GBM is limited. The present study revealed that the gene HPCAL1 was up-regulated by Ca2+ in the tissues and cells of GBM. Ectopic expression of HPCAL1 promoted proliferation of cells. Exhaustion of HPCAL1 inhibited cell growth not only in vivo, but also in vitro. In addition, HPCAL1 enhanced the Wnt pathway by stimulating ß-catenin accumulation and nuclear translocation in GBM cells, while ß-catenin silencing significantly inhibited the proliferation and growth of the GBM cells. Our results showed that Ser9 phosphorylation of GSK3ß was significantly decreased after HPCAL1 knockdown in GBM cells, and knockdown of the gene GSK3ß in GBM cells enhanced cell proliferation and promoted transcription of the genes CCND1 and c-Myc. Furthermore, the phosphorylation of ERK was decreased in the cells with HPCAL1 knockdown, while it was promoted via overexpression of HPCAL1. The suppression or depletion of the gene ERK decreased proliferation triggered by overexpression of HPCAL1 and impaired transcription of the genes c-Myc and CCND1. These studies elucidate the tumour-promoting activity of HPCAL1. They also offer an innovative therapeutic strategy focusing on the HPCAL1-Wnt/ß-catenin axis to regulate proliferation and development of GBM.

A Modified Sine-Cosine Algorithm Based on Neighborhood Search and Greedy Levy Mutation.

For the deficiency of the basic sine-cosine algorithm in dealing with global optimization problems such as the low solution precision and the slow convergence speed, a new improved sine-cosine algorithm is proposed in this paper. The improvement involves three optimization strategies. Firstly, the method of exponential decreasing conversion parameter and linear decreasing inertia weight is adopted to balance the global exploration and local development ability of the algorithm. Secondly, it uses the random individuals near the optimal individuals to replace the optimal individuals in the primary algorithm, which allows the algorithm to easily jump out of the local optimum and increases the search range effectively. Finally, the greedy Levy mutation strategy is used for the optimal individuals to enhance the local development ability of the algorithm. The experimental results show that the proposed algorithm can effectively avoid falling into the local optimum, and it has faster convergence speed and higher optimization accuracy.