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Pancreatic cancer ranks seventh in terms of cancer‑related mortality in men and women worldwide, where the most common subtype is pancreatic ductal adenocarcinoma (PDAC). To date, the pathogenesis of PDAC remains incompletely understood and the prognosis of PDAC is poor. In the present study, the expression of interleukin‑28 receptor α subunit (IL‑28RA) in PDAC tissues was detected using immunofluorescence staining and western blotting. IL‑28RA recombinant plasmids and control pCMV6‑entrymammalian expression plasmid, short hairpin (sh)IL‑28RA plasmids and control pRS scrambled shRNA vector purchased were used to produce stably transfected PANC‑1 cells overexpressing IL‑28RA or with IL‑28RA expression knocked down. MTS assays were used to measure cell viability and wound healing assay was used to assess the cell migratory ability in vitro. Flow cytometry analysis was performed to determine the proportion of cells in each phase of the cell cycle whereas total protein and phosphorylated protein levels were assessed using western blotting. Xenograft models of subcutaneous tumors were established by injecting PANC‑1 cells hypodermically into nude mice to investigate the effect of IL‑28RA on tumorigenesis and tumor growth. The results showed that the expression of IL‑28RA in PDAC tissues was lower compared with that in normal tissues. IL‑28RA overexpression in vitro resulted in the activation of the IL‑28RA pathway, which reduced cell viability and decreased the proportion of cells in the G2/M phase by reducing cyclin B1 expression. In addition, IL‑28RA overexpression inhibited migration of PDAC cells. By contrast, an increased proportion of cells in G2/M phase, upregulated cyclin B1 expression and enhanced cell viability and migratory ability along with inhibition of the IL‑28RA pathway were observed in PANC‑1 cells following IL‑28RA knockdown. The inhibitory effect of IL‑28RA was observed by tumor size in a nude mouse model induced by PANC‑1 cells with stable IL‑28RA overexpression or knockdown. The tumor size induced by PANC‑1 cells with stable IL‑28RA overexpression were smaller, whilst larger tumors induced by PANC‑1 cells were observed following stable IL‑28RA knockdown, when compared to control. Further studies showed that the effect of IL‑28RA on PDAC cells was exerted by regulating the phosphorylation levels of STAT1 and AKT. In conclusion, lower IL‑28RA expression may contribute to the pathogenesis of PDAC, where results from the present may provide further insights into the progression of PDAC, in addition to highlighting potentially novel therapeutic targets for this disease.

In recent decades, heart disease threatens people’s health seriously because of its prevalence and high risk of death. Therefore, predicting heart disease through some simple physical indicators obtained from the regular physical examination at an early stage has become a valuable subject. Clinically, it is essential to be sensitive to these indicators related to heart disease to make predictions and provide a reliable basis for further diagnosis. However, the large amount of data makes manual analysis and prediction taxing and arduous. Our research aims to predict heart disease both accurately and quickly through various indicators of the body. In this paper, a novel heart disease prediction model is given. We propose a heart disease prediction algorithm that combines the embedded feature selection method and deep neural networks. This embedded feature selection method is based on the LinearSVC algorithm, using the L1 norm as a penalty item to choose a subset of features significantly associated with heart disease. These features are fed into the deep neural network we built. The weight of the network is initialized with the He initializer to prevent gradient varnishing or explosion so that the predictor can have a better performance. Our model is tested on the heart disease dataset obtained from Kaggle. Some indicators including accuracy, recall, precision, and F1-score are calculated to evaluate the predictor, and the results show that our model achieves 98.56%, 99.35%, 97.84%, and 0.983, respectively, and the average AUC score of the model reaches 0.983, confirming that the method we proposed is efficient and reliable for predicting heart disease.

// Peng Chen 1, * , Xiaohu Zheng 2, * , Yonggang Zhou 2 , Yechuan Xu 1 , Lixin Zhu 3 , Yeben Qian 1 1 Department of General Surgery, First Affiliated Hospital of Anhui Medical University, Hefei, China 2 Institute of Immunology and the CAS Key Laboratory of Innate Immunity and Chronic Disease, School of Life Sciences and Medical Center, University of Science and Technology of China, Hefei, China 3 Center Laboratory, First Affiliated Hospital of Anhui Medical University, Hefei, China * These authors contributed equally to this work Correspondence to: Yeben Qian, email: qianyeben@hotmail.com Keywords: Talin-1, hepatocellular carcinoma, tumor growth and metastasis, ion transport, membrane depolarization Received: October 09, 2016 Accepted: January 09, 2017 Published: January 16, 2017 ABSTRACT Talin-1 is a known oncogene-associated protein. In this study, we set out to determine its role and mechanisms in hepatocellular carcinoma (HCC) progression. We found Talin-1 to be highly expressed in HCC cells relative to non-cancer liver epithelial cells and to promote tumor growth and metastasis. We used Whole Human Genome Oligo Microarray analysis with HCC cells and HCC cells in which Talin-1 was knocked down using shRNA to identify transcripts regulated by Talin-1. Of the 40,000 tested genes, 3099 were differentially expressed after Talin-1 knockdown; expression of 1924 genes was increased, while expression of 2175 was decreased. Gene ontology (GO) profiling indicated that Talin-1 promotes many HCC progression-related activities, including ion transport and membrane depolarization, cell growth, and cell adhesion. We also characterized the network of gene transcripts regulated by Talin-1 and their role in promoting HCC progression. Our findings confirm the role of Talin-1 in carcinogenesis and provided a set of novel therapeutic targets for the treatment of HCC.