RANKL, OPG, and RUNX2 expression and epigenetic modifications in giant cell tumour of bone in 32 patients.

Aims: The present study investigated receptor activator of nuclear factor kappa-Β ligand (RANKL), osteoprotegerin (OPG), and Runt-related transcription factor 2 (RUNX2) gene expressions in giant cell tumour of bone (GCTB) patients in relationship with tumour recurrence. We also aimed to investigate the influence of CpG methylation on the transcriptional levels of RANKL and OPG. Methods: A total of 32 GCTB tissue samples were analyzed, and the expression of RANKL, OPG, and RUNX2 was evaluated by quantitative polymerase chain reaction (qPCR). The methylation status of RANKL and OPG was also evaluated by quantitative methylation-specific polymerase chain reaction (qMSP). Results: We found that RANKL and RUNX2 gene expression was upregulated more in recurrent than in non-recurrent GCTB tissues, while OPG gene expression was downregulated more in recurrent than in non-recurrent GCTB tissues. Additionally, we proved that changes in DNA methylation contribute to upregulating the expression of RANKL and downregulating the expression of OPG, which are critical for bone homeostasis and GCTB development. Conclusion: Our results suggest that the overexpression of RANKL/RUNX2 and the lower expression of OPG are associated with recurrence in GCTB patients.

Enhanced meta-heuristic optimization of resource efficiency in multi-relay underground wireless sensor networks.

Achieving a balanced energy and spectral resource utilization is an interesting key design to extend the lifetime of underground wireless sensor networks (UWSNs) where sensor nodes are equipped with small limited energy batteries and communicate through a challenging soil environment. In this article, we apply an improved meta-heuristic algorithm, based on the Salp Swarm Algorithm (SSA), for multi-relay UWSNs where cooperative relay nodes amplify and forward sensed data, received from the buried source nodes, to the aboveground base station. Hence, the optimal nodes transmission powers, maximizing the network resource efficiency, are obtained and used to select beneficial relay nodes. The algorithm enhances the standard SSA by considering the chaotic map for salps population initialization and the uniform crossover technique for salps positions updates. Simulation results show that the proposed algorithm significantly outperforms the SSA in resource efficiency optimization and network lifetime extension. The obtained gain increases when the number of cooperative relay nodes increases. Furthermore, simulations prove the efficiency of the proposed algorithm against other meta-heuristic algorithms.