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RNF6 is a little-studied ring finger protein. In the present study, we found that RNF6 was overexpressed in various leukemia cells and that it accelerated leukemia cell proliferation, whereas knockdown of RNF6 delayed tumor growth in xenografts. To find out the mechanism of RNF6 overexpression in leukemia, we designed a series of truncated constructs of RNF6 regulatory regions in the luciferase reporter system. The results revealed that the region between ؊144 and ؊99 upstream of the RNF6 transcription start site was critical and that this region contained a PBX1 recognition element (PRE). PBX1 modulated RNF6 expression by binding to the specific PRE. When PRE was mutated, RNF6 transcription was completely abolished. Further studies showed that PBX1 collaborated with PREP1 but not MEIS1 to modulate RNF6 expression. Moreover, RNF6 expression could be suppressed by doxorubicin, a major anti-leukemia agent, via down-regulating PBX1. This study thus suggests that RNF6 overexpression in leukemia is under the direction of PBX1 and that the PBX1/RNF6 axis can be developed as a novel therapeutic target of leukemia.The ring finger protein 6 (RNF6) belongs to the largest RING ubiquitin ligase family, and it is mapped to chromosome band 13q12.2, a harbor of several critical tumor suppressor genes (1). RNF6 is believed to be a tumor suppressor because of its chromosomal location and somatic mutations in esophageal squamous cell carcinomas (2), but confirmative evidence is not available. In contrast, recent studies suggest that RNF6 is probably an oncogene. RNF6 is found at a high level in prostate cancers. As a ubiquitin ligase, RNF6 interacts with androgen receptor (AR) 3 and mediates atypical polyubiquitination chains at Lys-6 and Lys-27, thus promoting the transcriptional activity of AR by facilitating its binding to the coactivators (3). By modulating AR function, RNF6 promotes prostate cancer cell growth. In contrast, mutations and specific knockdown of RNF6 alter AR transcriptional activity and delay prostate cancer growth in xenograft models (3). RNF6 is also elevated in cisplatin-resistant human lung adenocarcinoma cells (4). Therefore, RNF6 probably plays a critical role in tumorigenesis and chemoresistance. However, the studies on RNF6 are very limited, and the biological functions and modulation of RNF6 are largely unknown.In the present study, we evaluated the RNF6 function in leukemia cells and found that RNF6 is overexpressed in leukemia cells and contributes to leukemia cell proliferation. Furthermore, RNF6 overexpression in leukemia is found to be modulated by the transcription factor PBX1, the pre-B-cell leukemia homeobox 1.

Nuclear factor kappa B (NF‐κB) signaling pathway is activated in many colorectal cancer (CRC) cells and in the tumor microenvironment, which plays a critical role in cancer initiation, development, and response to therapies. In the present study, we found that the widely used antimalarial drug mefloquine was a NF‐κB inhibitor that blocked the activation of IκBα kinase, leading to reduction of IκBα degradation, decrease of p65 phosphorylation, and suppressed expression of NF‐κB target genes in CRC cells. We also found that mefloquine induced growth arrest and apoptosis of CRC cells harboring phosphorylated p65 in culture and in mice. Furthermore, expression of constitutive active IKKβ kinase significantly attenuated the cytotoxic effect of the compound. These results showed that mefloquine could exert antitumor action through inhibiting the NF‐κB signaling pathway, and indicated that the antimalarial drug might be repurposed for anti‐CRC therapy in the clinic as a single agent or in combination with other anticancer drugs.

In duty-cycled wireless sensor networks, the nodes switch between active and dormant states, and each node may determine its active/dormant schedule independently. This complicates the Minimum-Energy Multicasting (MEM) problem, which has been primarily studied in always-active wireless adhoc networks. In this paper, we study the duty-cycle-aware MEM problem in wireless sensor networks, and we present a formulation of the Minimum-Energy Multicast Tree Construction and Scheduling (MEMTCS) problem. We prove that the MEMTCS problem is NP-hard, and it is unlikely to have an approximation algorithm with a performance ratio of (1 − o(1)) ln ∆, where ∆ is the maximum node degree in a network. We propose a polynomial-time approximation algorithm for the MEMTCS problem with a performance ratio of O(H(∆ + 1)), where H(·) is the harmonic number. We also provide a distributed implementation of our algorithm. Finally, we perform extensive simulations and the results demonstrate that our algorithm significantly outperform other known algorithms in terms of both the total energy cost and the transmission redundancy.

Clioquinol is an anti-microbial drug, and it was recently found to induce cancer cell death. In the present study, clioquinol was found to trigger autophagy by inducing LC3 lipidation and autophagosome formation which was abolished by an autophagy inhibitor 3-methyladenine. Further study showed clioquinol displayed no effects on PI3KC3 or Beclin 1 expression but downregulated the expression and the enzymatic activity of mammalian target of Rapamycin (mTOR), a critical modulator of autophagy. Moreover, clioquinol inhibited the catalytic activity of the mTOR complex 1, thus suppressing phosphorylation of P70S6K and 4E-BP1, two major proteins associated with autophagy in the mTORC1 signaling pathway. Clioquinol induced leukemia and myeloma cell apoptosis, however, addition of autophagy inhibitor 3-methyladenine attenuated this kind of cell death. Therefore, this study demonstrated that clioquinol induces autophagy in associated with apoptosis in leukemia and myeloma cells by disrupting mTOR signaling pathway.

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BackgroundWe previously reported a PI3K inhibitor S14161 which displays a promising preclinical activity against multiple myeloma (MM) and leukemia, but the chiral structure and poor solubility prevent its further application.MethodsSix S14161 analogs were designed based on the structure–activity relationship; activity of the compounds in terms of cell death and inhibition of PI3K were analyzed by flow cytometry and Western blotting, respectively; anti-myeloma activity in vivo was performed on two independent xenograft models.ResultsAmong the six analogs, BENC-511 was one of the most potent compounds which significantly inhibited PI3K activity and induced MM cell apoptosis. BENC-511 was able to inactivate PI3K and its downstream signals AKT, mTOR, p70S6K, and 4E-BP1 at 1 μM but had no effects on their total protein expression. Consistent with its effects on PI3K activity, BENC-511 induced MM cell apoptosis which was evidenced by the cleavage of Caspase-3 and PARP. Notably, addition of insulin-like growth factor 1 and interleukin-6, two important triggers for PI3K activation in MM cells, partly blocked BENC-511-induced MM cell death, which further demonstrated that PI3K signaling pathway was critical for the anti-myeloma activity of BENC-511. Moreover, BENC-511 also showed potent oral activity against myeloma in vivo. Oral administration of BENC-511 decreased tumor growth up to 80% within 3 weeks in two independent MM xenograft models at a dose of 50 mg/kg body weight, but presented minimal toxicity. Suppression of BENC-511 on MM tumor growth was associated with decreased PI3K/AKT activity and increased cell apoptosis.ConclusionsBecause of its potent anti-MM activity, low toxicity (LD50 oral >1.5 g/kg), and easy synthesis, BENC-511 could be developed as a promising agent for the treatment of MM via suppressing the PI3K/AKT signaling pathway.

Given a social network G, the profit maximization (PM) problem asks for a set of seed nodes to maximize the profit, i.e., revenue of influence spread less the cost of seed selection. The target profit maximization (TPM) problem, which generalizes the PM problem, aims to select a subset of seed nodes from a target user set T to maximize the profit. Existing algorithms for PM mostly consider the nonadaptive setting, where all seed nodes are selected in one batch without any knowledge on how they may influence other users. In this paper, we study TPM in adaptive setting, where the seed users are selected through multiple batches, such that the selection of a batch exploits the knowledge of actual influence in the previous batches. To acquire an overall understanding, we study the adaptive TPM problem under both the oracle model and the noise model, and propose ADG and ADDATP algorithms to address them with strong theoretical guarantees, respectively. In addition, to better handle the sampling errors under the noise model, we propose the idea of hybrid error based on which we design a novel algorithm HATP that boosts the efficiency of ADDATP significantly. We conduct extensive experiments on real social networks to evaluate the performance, and the experimental results strongly confirm the superiorities and effectiveness of our solutions.