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To investigate extracellular vesicles (EVs), biomarkers for predicting lymph node invasion (LNI) in patients with high-risk prostate cancer (HRPCa), plasma, and/or urine samples were prospectively collected from 45 patients with prostate cancer (PCa) and five with benign prostatic hyperplasia (BPH). Small RNA sequencing was performed to identify miRNAs in the EVs. All patients with PCa underwent radical prostatectomy and extended pelvic lymph node dissection. Differentially expressed miRNAs were identified in patients with and without pathologically-verified LNI. The candidate miRNAs were validated in low-risk prostate cancer (LRPCa) and BPH. Four miRNA species (e.g., miR-126-3p) and three miRNA species (e.g., miR-27a-3p) were more abundant in urinary and plasma EVs, respectively, of patients with PCa. None of these miRNA species were shared between urinary and plasma EVs. miR-126-3p was significantly more abundant in patients with HR PCa with LNI than in those without (P = 0.018). miR-126-3p was significantly more abundant in the urinary EVs of patients with HRPCa than in those with LRPCa (P = 0.017) and BPH (P = 0.011). In conclusion, urinary EVs-derived miR-126-3p may serve as a good biomarker for predicting LNI in patients with HRPCa. [ABSTRACT FROM AUTHOR]

Neuroendocrine prostate cancer is a rapidly progressive and lethal disease characterized by early visceral metastasis, poor prognosis, and limited treatment options. Uncovering the oncogenic mechanisms could lead to the discovery of potential therapeutic avenues. Here, we demonstrate that the RNA-binding protein ELAVL3 is specifically upregulated in neuroendocrine prostate cancer and that overexpression of ELAVL3 alone is sufficient to induce the neuroendocrine phenotype in prostate adenocarcinoma. Mechanistically, ELAVL3 is transcriptionally regulated by MYCN and subsequently binds to and stabilizes MYCN and RICTOR mRNA. Moreover, ELAVL3 is shown to be released in extracellular vesicles and induce neuroendocrine differentiation of adenocarcinoma cells via an intercellular mechanism. Pharmacological inhibition of ELAVL3 with pyrvinium pamoate, an FDA-approved drug, effectively suppresses tumor growth, reduces metastatic risk, and improves survival in neuroendocrine prostate cancer mouse models. Our results identify ELAVL3 as a critical regulator of neuroendocrine differentiation in prostate cancer and propose a drug repurposing strategy for targeted therapies. Neuroendocrine prostate cancer (NEPC) is an aggressive subtype with limited effective therapeutic options. Here, the authors identify the RNA-binding protein ELAVL3 as a driver of differentiation into NEPC via a positive feedback loop with MYCN and demonstrate the use of the repurposed drug pyrvinium pamoate to target this axis using preclinical models of NEPC. [ABSTRACT FROM AUTHOR]

As an important part of the new infrastructure, the cloud data center is developing rapidly, and its energy consumption problem is becoming more and more prominent. Therefore, research on energy-saving technologies for cloud data centers has attracted widespread attention from academia and industry. Some studies have reviewed energy-saving optimization methods and technologies for data centers, but recently, many state-of-the-art optimization methods of energy consumption and energy-saving technologies have sprung out, which are still worth analyzing and discussing. Depending on the in-depth investigation and analysis of related research status, this article firstly focuses on analyzing and discussing the energy-saving technologies of the two components: IT equipment and cooling systems, both of which bring about the largest energy consumption in cloud data centers. As for IT equipment, its energy-saving technologies mainly include the energy saving of servers, storage systems, and network systems. While as for cooling systems, airflow organization in the computer room, thermal-aware scheduling technology, and other new energy-saving technologies are involved. Secondly, on the basis of analyzing the energy-saving technologies of the two major components, a new optimization scheme of energy consumption for the jointing computing system and cooling system is explained. Throughout this work, various energy-saving strategies and technologies have been described and compared. Finally, the future trends and development directions of energy saving for data centers are further promoted, such as integral optimization of energy consumption jointing multiple components, energy saving using artificial intelligence methods, energy saving based on novel hardware equipment, hybrid cooling energy saving, and comprehensive energy conservation with various energy technologies. [ABSTRACT FROM AUTHOR]