SH3BP1 Regulates Melanoma Progression Through Race1/Wace2 Signaling Pathway

Background: SH3-domain binding protein-1 (SH3BP1), which specifically inactivates Rac1 and its target protein Wave2, has been shown to be an important regulator of cancer metastasis. However, the effects of SH3BP1 in melanoma progression remain unclear. The current study aimed to explore the function of SH3BP1 in melanoma and its possible molecular mechanism. Methods: TCGA database was used to analyze the expression of SH3BP1 in melanoma. Then, reverse transcription–quantitative polymerase chain reaction was performed to detect the expression of SH3BP1 in melanoma tissues and cells. Next, genes related to SH3BP1 were analyzed by LinkedOmics database, and protein interactions were analyzed by STRING database. These genes were further subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis. In addition, the signaling pathway of SH3BP1 action was screened by bioinformatics analysis. Finally, the function of SH3BP1 and its mediated signaling pathway in melanoma progression were investigated in vitro and in vivo. Results: SH3BP1 was significantly upregulated in melanoma tissues and cells. The pathways regulated by SH3BP1 are closely related to the occurrence and development of tumors. And we found that overexpression of SH3BP1 promoted the proliferation, migration, and invasion of melanoma cells by increasing Rac1 activity and Wave2 protein levels in vitro. Similarly, overexpression of SH3BP1 facilitated melanoma progression by upregulating Wave2 protein expression in vivo. Conclusion: In summary, this study revealed for the first time that SH3BP1 promoted melanoma progression through Rac1/Wave2 signaling pathway, providing a new therapeutic target for melanoma

Towards Autonomic Science Infrastructure: Architecture, Limitations, and Open Issues

Scientific computing systems are becoming increasingly complex and indeed are close to reaching a critical limit in manageability when using current human-in-the-loop techniques. In order to address this problem, autonomic, goal-driven management actions based on machine learning must be applied end to end across the scientific computing landscape. Even though researchers proposed architectures and design choices for autonomic computing systems more than a decade ago, practical realization of such systems has been limited, especially in scientific computing environments. Growing interest and recent developments in machine learning have spurred proposals to apply machine learning for goal-based optimization of computing systems in an autonomous fashion. We review recent work that uses machine learning algorithms to improve computer system performance, identify gaps and open issues. We propose a hierarchical architecture that builds on the earlier proposals for autonomic computing systems to realize an autonomous science infrastructure