Osteosarcoma, an aggressive malignant cancer, has a high lung metastasis rate and lacks therapeutic target. Here, we reported that chromobox homolog 4 (CBX4) was overexpressed in osteosarcoma cell ...lines and tissues. CBX4 promoted metastasis by transcriptionally up-regulating Runx2 via the recruitment of GCN5 to the Runx2 promoter. The phosphorylation of CBX4 at T437 by casein kinase 1α (CK1α) facilitated its ubiquitination at both K178 and K280 and subsequent degradation by CHIP, and this phosphorylation of CBX4 could be reduced by TNFα. Consistently, CK1α suppressed cell migration and invasion through inhibition of CBX4. There was a reverse correlation between CK1α and CBX4 in osteosarcoma tissues, and CK1α was a valuable marker to predict clinical outcomes in osteosarcoma patients with metastasis. Pyrvinium pamoate (PP) as a selective activator of CK1α could inhibit osteosarcoma metastasis via the CK1α/CBX4 axis. Our findings indicate that targeting the CK1α/CBX4 axis may benefit osteosarcoma patients with metastasis.
The heterogeneous nature of tumour microenvironment (TME) underlying diverse treatment responses remains unclear in nasopharyngeal carcinoma (NPC). Here, we profile 176,447 cells from 10 NPC ...tumour-blood pairs, using single-cell transcriptome coupled with T cell receptor sequencing. Our analyses reveal 53 cell subtypes, including tumour-infiltrating CD8
T, regulatory T (Treg), and dendritic cells (DCs), as well as malignant cells with different Epstein-Barr virus infection status. Trajectory analyses reveal exhausted CD8
T and immune-suppressive TNFRSF4
Treg cells in tumours might derive from peripheral CX3CR1
CD8
T and naïve Treg cells, respectively. Moreover, we identify immune-regulatory and tolerogenic LAMP3
DCs. Noteworthily, we observe intensive inter-cell interactions among LAMP3
DCs, Treg, exhausted CD8
T, and malignant cells, suggesting potential cross-talks to foster an immune-suppressive niche for the TME. Collectively, our study uncovers the heterogeneity and interacting molecules of the TME in NPC at single-cell resolution, which provide insights into the mechanisms underlying NPC progression and the development of precise therapies for NPC.
This paper proposes two deep learning methods for remaining useful life (RUL) prediction of bearings. The methods have the advantageous end-to-end property that they take raw data as input and ...generate the predicted RUL directly. They are TSMC-CNN, which stands for the time series multiple channel convolutional neural network, and TSMC-CNN-ALSTM, which stands for the TSMC-CNN integrated with the attention-based long short-term memory (ALSTM) network. The proposed methods divide a time series into multiple channels and take advantage of the convolutional neural network (CNN), the long short-term memory (LSTM) network, and the attention-based mechanism for boosting performance. The CNN performs well for extracting features from data with multiple channels; dividing a time series into multiple channels helps the CNN extract relationship among far-apart data points. The LSTM network is excellent for processing temporal data; the attention-based mechanism allows the LSTM network to focus on different features at different time steps for better prediction accuracy. PRONOSTIA bearing operation datasets are applied to the proposed methods for the purpose of performance evaluation and comparison. The comparison results show that the proposed methods outperform the others in terms of the mean absolute error (MAE) and the root mean squared error (RMSE) of RUL prediction.
This study aimed to establish an effective prognostic nomogram with or without plasma Epstein-Barr virus DNA (EBV DNA) for nondisseminated nasopharyngeal carcinoma (NPC).
The nomogram was based on a ...retrospective study of 4630 patients who underwent radiotherapy with or without chemotherapy at Sun Yat-sen University Cancer Center from 2007 to 2009. The predictive accuracy and discriminative ability of the nomogram were determined by a concordance index (C-index) and calibration curve and were compared with EBV DNA and the current staging system. The results were validated using bootstrap resampling and a prospective cohort study on 1819 patients consecutively enrolled from 2011 to 2012 at the same institution. All statistical tests were two-sided.
Independent factors derived from multivariable analysis of the primary cohort to predict recurrence were age, sex, body mass index (BMI), T stage, N stage, plasma EBV DNA, pretreatment high sensitivity C-reactive protein (hs-CRP), lactate dehydrogenase (LDH), and hemoglobin level (HGB), which were all assembled into the nomogram with (nomogram B) or without EBV DNA (nomogram A). The calibration curve for the probability of recurrence showed that the nomogram-based predictions were in good agreement with actual observations. The C-index of nomogram B for predicting recurrence was 0.728 (P < .001), which was statistically higher than the C-index values for nomogram A (0.690), EBV DNA (0.680), and the current staging system (0.609). The C-index of nomogram B (0.730) and nomogram A (0.681) remained higher for predicting recurrence among patients treated with intensity-modulated radiotherapy (P < .001). The results were confirmed in the validation cohort.
The proposed nomogram with or without plasma EBV DNA resulted in more accurate prognostic prediction for NPC patients.
Non-alcoholic fatty liver disease (NAFLD) encompasses a spectrum of disease phenotypes which start with simple steatosis and lipid accumulation in the hepatocytes - a typical histological lesions ...characteristic. It may progress to non-alcoholic steatohepatitis (NASH) that is characterized by hepatic inflammation and/or fibrosis and subsequent onset of NAFLD-related cirrhosis and hepatocellular carcinoma (HCC). Due to the central role of the liver in metabolism, NAFLD is regarded as a result of and contribution to the metabolic abnormalities seen in the metabolic syndrome. Peroxisome proliferator-activated receptors (PPARs) has three subtypes, which govern the expression of genes responsible for energy metabolism, cellular development, inflammation, and differentiation. The agonists of PPARα, such as fenofibrate and clofibrate, have been used as lipid-lowering drugs in clinical practice. Thiazolidinediones (TZDs) - ligands of PPARγ, such as rosiglitazone and pioglitazone, are also used in the treatment of type 2 diabetes (T2D) with insulin resistance (IR). Increasing evidence suggests that PPARβ/δ agonists have potential therapeutic effects in improving insulin sensitivity and lipid metabolism disorders. In addition, PPARs ligands have been considered as potential therapeutic drugs for hypertension, atherosclerosis (AS) or diabetic nephropathy. Their crucial biological roles dictate the significance of PPARs-targeting in medical research and drug discovery. Here, it reviews the biological activities, ligand selectivity and biological functions of the PPARs family, and discusses the relationship between PPARs and the pathogenesis of NAFLD and metabolic syndrome. This will open new possibilities for PPARs application in medicine, and provide a new idea for the treatment of fatty liver and related diseases.
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Purpose
Breast cancer is the most common cancer in females and the leading cause of death worldwide. The effects of statins on breast cancer prognosis have long been controversial; thus, it is ...important to investigate the relationship between statin type, exposure time, and breast cancer prognosis. This study sought to explore the effect of statins, as well as the different effects of statin solubility and variable follow-up times, on breast cancer prognosis.
Methods
We searched the MEDLINE (via PubMed), EMBASE (via OvidSP), Cochrane Library, and ISI Web of Knowledge databases using combinations of the terms “breast neoplasmsMeSH,” “statins” or “lipid-lowering drug,” “prognosis” or “survival,” or “mortality” or “outcome” with no limit on the publication date. We searched the databases between inception and October 15, 2016. Reference lists of the included studies and relevant reviews were also manually screened. The initial search identified 71 publications, and 7 of these studies, which included a total of 197,048 women, met the selection criteria. Two authors independently screened each study for inclusion and extracted the data. The data were analyzed using Stata/SE 11.0.
Results
Overall statin use was associated with lower cancer-specific mortality and all-cause mortality, although the benefit appeared to be constrained by statin type and follow-up time. Lipophilic statins were associated with decreased breast cancer-specific and all-cause mortality; however, hydrophilic statins were weakly protective against only all-cause mortality and not breast cancer-specific mortality. Of note, one group with more than 4 years of follow-up did not show a significant correlation between statin use and cancer-specific mortality or all-cause mortality, whereas groups with less than 4 years of follow-up still showed the protective effect of statins against cancer-specific mortality and all-cause mortality.
Conclusions
Although statins can reduce breast cancer patient mortality, the benefit appears to be constrained by statin type and follow-up time. Lipophilic statins showed a strong protective function in breast cancer patients, whereas hydrophilic statins only slightly improved all-cause mortality. Finally, the protective effect of statins could only be observed in groups with less than 4 years of follow-up. These findings are meaningful in clinical practice, although some conclusions contradict conventional wisdom and will thus require further exploration.
With the rapid evolution of network traffic diversity, the understanding of network traffic has become more pivotal and more formidable. Previously, traffic classification and intrusion detection ...require a burdensome analyzing of various traffic features and attack-related characteristics by experts, and even, private information might be required. However, due to the outdated features labeling and privacy protocols, the existing approaches may not fit with the characteristics of the changing network environment anymore. In this paper, we present a light-weight framework with the aid of deep learning for encrypted traffic classification and intrusion detection, termed as deep-full-range (DFR). Thanks to deep learning, DFR is able to learn from raw traffic without manual intervention and private information. In such a framework, our proposed algorithms are compared with other state-of-the-art methods using two public datasets. The experimental results show that our framework not only can outperform the state-of-the-art methods by averaging 13.49% on encrypted traffic classification's <inline-formula> <tex-math notation="LaTeX">F1 </tex-math></inline-formula> score and by averaging 12.15% on intrusion detection's <inline-formula> <tex-math notation="LaTeX">F1 </tex-math></inline-formula> score but also require much lesser storage resource requirement.
Lysosomes are the main organelles in macrophages for killing invading bacteria. However, the precise mechanism underlying lysosomal biogenesis upon bacterial infection remains enigmatic. We ...demonstrate here that LPS stimulation increases IRG1-dependent itaconate production, which promotes lysosomal biogenesis by activating the transcription factor, TFEB. Mechanistically, itaconate directly alkylates human TFEB at cysteine 212 (Cys270 in mice) to induce its nuclear localization by antagonizing mTOR-mediated phosphorylation and cytosolic retention. Functionally, abrogation of itaconate synthesis by IRG1/Irg1 knockout or expression of an alkylation-deficient TFEB mutant impairs the antibacterial ability of macrophages in vitro. Furthermore, knockin mice harboring an alkylation-deficient TFEB mutant display elevated susceptibility to Salmonella typhimurium infection, whereas in vivo treatment of OI, a cell-permeable itaconate derivative, limits inflammation. Our study identifies itaconate as an endogenous metabolite that functions as a lysosomal inducer in macrophages in response to bacterial infection, implying the potential therapeutic utility of itaconate in treating human bacterial infection.
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•Itaconate alkylates TFEB at Cys212•Alkylated-Cys212 disrupts mTOR/14-3-3-mediated cytosolic retention of TFEB•Itaconate promotes TFEB-dependent lysosomal biogenesis and innate immunity•TFEB alkylation deficiency increases susceptibility of mice to bacterial infection
Zhang et al. report that the metabolite itaconate alkylates transcription factor TFEB, a master regulator of lysosomal biogenesis, at Cys212 to reduce its cytosolic retention modulated by mTOR/14-3-3. Itaconate-mediated alkylation activates TFEB-dependent lysosomal biogenesis, thereby facilitating the bacteria clearance during the antibacterial innate immune response.
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