Inhibition of EGFR nuclear shuttling decreases irradiation resistance in HeLa cells

Introduction. Cervical cancer is a leading cause of mortality in women worldwide. The resistance to irradiation at the advanced stage is the main reason for the poor prognosis and high mortality. This work aims to elucidate the molecular mechanism underlying the radio-resistance. Material and methods. In this study, we determined the pEGFR-T654 and pDNA-PK-T2609 expression level changes in irradiated HeLa cells treated with T654 peptide, a nuclear localization signal (NLS) inhibitor, to inhibit EGFR nuclear transport. Cell viability, cell cycle and migratory capacity were analyzed. Xenograft animal model was used to evaluate the effect of EGFR nuclear transport inhibition on the tumor growth in vivo. Results. The enhanced translocation of nuclear EGFR in the irradiated HeLa cells correlated with the increasing level of pEGFR-T654 and pDNA-PK-T2609. Inhibition of EGFR nuclear translocation by NLS peptide inhibitor attenuated DNA damage repair in the irradiated HeLa cells, decreased cell viability and promoted cell death through arrest at G0 phase. NLS peptide inhibitor impaired the migratory capacity of irradiated HeLa cells, and negatively affected tumorigenesis in xenograft mice. Conclusions. This work puts forward a potential molecular mechanism of the irradiation resistance in cervical cancer cells, providing a promising direction towards an efficient therapy of cervical cancer

Inhibition of EGFR nuclear shuttling decreases irradiation resistance in HeLa cells

Introduction. Cervical cancer is a leading cause of mortality in women worldwide. The resistance to irradiation at the advanced stage is the main reason for the poor prognosis and high mortality. This work aims to elucidate the molecular mechanism underlying the radio-resistance. Material and methods. In this study, we determined the pEGFR-T654 and pDNA-PK-T2609 expression level changes in irradiated HeLa cells treated with T654 peptide, a nuclear localization signal (NLS) inhibitor, to inhibit EGFR nuclear transport. Cell viability, cell cycle and migratory capacity were analyzed. Xenograft animal model was used to evaluate the effect of EGFR nuclear transport inhibition on the tumor growth in vivo. Results. The enhanced translocation of nuclear EGFR in the irradiated HeLa cells correlated with the increasing level of pEGFR-T654 and pDNA-PK-T2609. Inhibition of EGFR nuclear translocation by NLS peptide inhibitor attenuated DNA damage repair in the irradiated HeLa cells, decreased cell viability and promoted cell death through arrest at G0 phase. NLS peptide inhibitor impaired the migratory capacity of irradiated HeLa cells, and negatively affected tumorigenesis in xenograft mice. Conclusions. This work puts forward a potential molecular mechanism of the irradiation resistance in cervical cancer cells, providing a promising direction towards an efficient therapy of cervical cancer. (Folia Histochemica et Cytobiologica 2017, Vol. 55, No. 2, 43–51

On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs

Graph neural networks (GNNs) have been widely used in various graph-related problems such as node classification and graph classification, where the superior performance is mainly established when natural node features are available. However, it is not well understood how GNNs work without natural node features, especially regarding the various ways to construct artificial ones. In this paper, we point out the two types of artificial node features,i.e., positional and structural node features, and provide insights on why each of them is more appropriate for certain tasks,i.e., positional node classification, structural node classification, and graph classification. Extensive experimental results on 10 benchmark datasets validate our insights, thus leading to a practical guideline on the choices between different artificial node features for GNNs on non-attributed graphs. The code is available at https://github.com/zjzijielu/gnn-exp/.Comment: This paper has been accepted to the Sixth International Workshop on Deep Learning on Graphs (DLG-KDD'21) (co-located with KDD'21

Histocompatibility and Long-Term Results of the Follicular Unit-Like Wigs after Xenogeneic Hair Transplantation: An Experimental Study in Rabbits

Objective. This study was designed to observe the histocompatibility and long-term results of wigs after xenogeneic hair transplantation and to explore the possibility of industrial products in clinical application. Methods. The human hair and melted medical polypropylene were preceded into the follicular unit-like wigs according to the natural follicular unit by extrusion molding. 12 New Zealand rabbits were used as experimental animals for wigs transplantation. The histocompatibility of polypropylene and human hair was observed by H&E staining and scanning electron microscope. The loss rate of wigs was calculated to evaluate the long-term result after transplantation. Results. Mild infiltration by inflammatory cells around the polypropylene and human hair were seen during the early period after transplantation, accompanied with local epithelial cell proliferation. The inflammatory cells were decreased after 30 days with increased collagen fibers around the polypropylene and human hair. The follicular unit-like wigs maintained a good histocompatibility in one year. The degradation of hair was not significant. The loss rate of wigs was 4.1 ± 4.0% in one year. The appearance of hair was satisfactory. Conclusions. We successfully developed a follicular unit-like wigs, which were made of xenogeneic human hair with medical polypropylene, showing a good histocompatibility, a low loss rate, and satisfactory appearance in a year after transplantation. The follicular unit-like wigs may have prospective industrial products in clinical application

Efficient Secure Multiparty Computation for Multidimensional Arithmetics and Its Application in Privacy-Preserving Biometric Identification

Over years of the development of secure multi-party computation (MPC), many sophisticated functionalities have been made pratical and multi-dimensional operations occur more and more frequently in MPC protocols, especially in protocols involving datasets of vector elements, such as privacy-preserving biometric identification and privacy-preserving machine learning. In this paper, we introduce a new kind of correlation, called tensor triples, which is designed to make multi-dimensional MPC protocols more efficient. We will discuss the generation process, the usage, as well as the applications of tensor triples and show that it can accelerate privacy-preserving biometric identification protocols, such as FingerCode, Eigenfaces and FaceNet, by more than 1000 times

Identification of hub genes and construction of prognostic nomogram for patients with Wilms tumors

BackgroundIn children, Wilms’ tumors are the most common urological cancer with unsatisfactory prognosis, but few molecular prognostic markers have been discovered for it. With the rapid development of high-throughput quantitative proteomic and transcriptomic approaches, the molecular mechanisms of various cancers have been comprehensively explored. This study aimed to uncover the molecular mechanisms underlying Wilms tumor and build predictive models by use of microarray and RNA-seq data.MethodsGene expression datasets were downloaded from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases. Bioinformatics methods wereutilized to identified hub genes, and these hub genes were validated by experiment. Nomogram predicting OS was developed using genetic risk score model and clinicopathological variables.ResultsCDC20, BUB1 and CCNB2 were highly expressed in tumor tissues and able to affect cell proliferation and the cell cycle of SK-NEP-1 cells. This may reveal molecular biology features and a new therapeutic target of Wilms tumour.7 genes were selected as prognostic genes after univariate, Lasso, and multivariate Cox regression analyses and had good accuracy, a prognostic nomogram combined gene model with clinical factors was completed with high accuracy.ConclusionsThe current study discovered CDC20,BUB1 and CCNB2 as hub-genes associated with Wilms tumor, providing references to understand the pathogenesis and be considered a novel candidate to target therapy and construct novel nomogram, incorporating both clinical risk factors and gene model, could be appropriately applied in preoperative individualized prediction of malignancy in patients with Wilms tumor