Epstein–Barr virus (EBV) is positively related to the morbidity of nasopharyngeal carcinoma (NPC) in Asia. After infection, EBV can produce several proteins, including viral interleukin‐10 (vIL‐10). But the mechanism by which vIL‐10 contributes to NPC cell proliferation and cell cycle progression is not well understood. In this study, EBV negative and positive cell lines, and the JAK2/STAT3 signal pathway inhibitor AG490 were used to illustrate the role of vIL‐10 in NPC. Cell proliferation and cell cycle were measured by CCK‐8 and flow cytometry. The expression levels of related protein were measured by Western blotting. High concentrations of vIL‐10 and IL‐6 were found in the EBV positive patients. The expression level of IL‐6 was positively related to the presence of concentration of vIL‐10. vIL‐10 can promote cancer cell proliferation and G1 to S phase transmission via upregulating the IL‐6 protein level by activating the JAK2/STAT3 signal pathway. Furthermore, EBV can induce the formation of cytotoxic T cells, whereas vIL‐10 can block the function of cytotoxic T cells. Taken together, these results suggest that vIL‐10 promotes cell proliferation and cell cycle progression via JAK2/STAT3 signaling pathway in NPC.

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IntroductionCancer in patients of childbearing age continues to become increasingly common. The purpose of this study was to explore the impact of metastatic breast cancer (MBC) on overall survival (OS) and cancer-specifific survival (CSS) in patients of childbearing age and to construct prognostic nomograms to predict OS and CSS.MethodsData from MBC patients of childbearing age were obtained from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015, and the patients were randomly assigned into the training and validation cohorts. Univariate and multivariate Cox analyses were used to search for independent prognostic factors impacting OS and CSS, and these data were used to construct nomograms. The concordance index (C-index), area under the curve (AUC), and calibration curves were used to determine the predictive accuracy and discriminative ability of the nomograms. Additional data were obtained from patients at the Yunnan Cancer Hospital to further verify the accuracy of the nomograms.ResultsA total of 1,700 MBC patients of childbearing age were identifified from the SEER database, and an additional 92 eligible patients were enrolled at the Yunnan Cancer Hospital. Multivariate Cox analyses identifified 10 prognostic factors for OS and CSS that were used to construct the nomograms. The calibration curve for the probabilities of OS and CSS showed good agreement between nomogram prediction and clinical observations. The C-index of the nomogram for OS was 0.735 (95% CI = 0.725–0.744); the AUC at 3 years was 0.806 and 0.794 at 5 years.The nomogram predicted that the C-index of the CSS was 0.740 (95% CI = 0.730– 0.750); the AUC at 3 years was 0.811 and 0.789 at 5 years. The same results were observed in the validation cohort. Kaplan– Meier curves comparing the low-,medium-, and high-risk groups showed strong prediction results for the prognostic nomogram.ConclusionWe identifified several independent prognostic factors and constructed nomograms to predict the OS and CSS for MBC patients of childbearing age.These prognostic models should be considered in clinical practice to individualize treatments for this group of patients.

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Objectives To identify key long non-coding (lnc)RNAs responsible for the epithelial–mesenchymal transition (EMT) of CNE1 nasopharyngeal carcinoma cells and to investigate possible regulatory mechanisms in EMT. Methods CNE1 cells were divided into transforming growth factor (TGF)-β1-induced EMT and control groups. The mRNA and protein expression of EMT markers was determined by real-time quantitative PCR and western blotting. Differentially expressed genes (DEGs) between the two groups were identified by RNA sequencing analysis, and DEG functions were analyzed by gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses. EMT marker expression was re-evaluated by western blotting after knockdown of a selected lncRNA. Results TGF-β1-induced EMT was characterized by decreased E-cadherin and increased vimentin, N-cadherin, and Twist expression at both mRNA and protein levels. Sixty lncRNA genes were clustered in a heatmap, and mRNA expression of 14 dysregulated lncRNAs was consistent with RNA sequencing. Knockdown of lnc-PNRC2-1 increased expression of its antisense gene MYOM3 and reduced expression of EMT markers, resembling treatment with the TGF-β1 receptor inhibitor LY2109761. Conclusion Various lncRNAs participated indirectly in the TGF-β1-induced EMT of CNE1 cells. Lnc-PNRC2-1 may be a key regulator of this and is a potential target to alleviate CNE1 cell EMT.