long non-coding rnas (lncrnas) play critical roles in the development and progression of cancers. The present study aimed to identify novel lncrnas and associated micrornas (mirnas or mirs) and mrnas in gastric cancer. differentially expressed lncrnas (delncrnas) and differentially expressed mrnas (demrnas) of 6 paired gastric cancer and normal tissues were identified using microarray. The demirnas between gastric cancer and the normal control tissues were identified using mirna-seq data from cancer Genome atlas. common delncrnas from the cancer rna-Seq nexus database and circlncrnanet database were analyzed. a delncrnas-demirnas-demrnas network was constructed by target prediction. Functional enrichment analysis was employed to predict the function of demrnas in the network. The correlation between the expression of delncrnaS and demrnas in the network was analyzed. The expression levels of several genes were validated by reverse transcription-quantitative polymerase chain reaction (rT-qPcr). a total of 1,297 delncrnas, 2,037 demrnas and 171 DEmiRNAs were identified. Among the 4 lncRNAs common to the 3 datasets, prostate androgen-regulated transcript 1 (ParT1) was selected for further analysis. The analysis identified 5 DEmiRNAs and 13 DEmRNAs in the ParT1-mediated cerna network. The demrnas in the cerna network were markedly enriched in cancer-related biological processes (response to hypoxia, positive regulation of angiogenesis and positive regulation of endothelial cell proliferation) and pathways (cGMP-PKG signaling pathway, caMP signaling pathway and proteoglycans in cancer). out of the 13 demrnas, 11 were positively associated with ParT1. The downregulation of ParT1, myosin light chain 9 (MYl9), potassium calcium-activated channel subfamily M alpha 1 (KcnMa1), cholinergic receptor muscarinic 1 (cHrM1), solute carrier family 25 member 4 (SLC25A4) and ATPase na + /K + transporting subunit alpha 2 (aTP1a2) expression levels in gastric cancer was validated by rT-qPcr. on the whole, the current study identified a novel lncrna and associated mirnas and mrnas that are involved in the pathogenesis of gastric cancer that may serve as potential therapeutic targets for the treatment of gastric cancer.
Background: Annexin A3 (ANXA3) is overexpressed in various cancers and is a potential target for cancer treatment. However, clinical implication and biological function of ANXA3 in colon cancer remain unknown. This study aimed to investigate the relationship between hypoxia-inducible factor 1-alpha (HIF-1α) and ANXA3, and explore the function of ANXA3 in colon carcinoma.Methods: Expression levels of HIF-1α and ANXA3 in human colon carcinoma specimens and colon cancer cell lines were detected by immunohistochemistry, real-time PCR and Western blot analysis. The proliferation of colon cancer cells was examined. Nude mice were used for xenograft tumor model, and HIF-1α siRNA or control adenovirus was injected into the tumor.Results: HIF-1α and ANXA3 expression levels were higher in colon cancer tissues than their expression levels in normal colon tissues. In addition, HIF-1α and ANXA3 expression increased in colon cancer cells under hypoxic condition. Knockdown of HIF-1α decreased HIF-1α and ANXA3 expression, and inhibited the proliferation and growth of colon cancer cells. In nude mouse model, silencing HIF-1α decreased volume of xenograft tumor and ANXA3 expression.Conclusions: ANXA3 expression is upregulated by HIF-1α in colon cancer in response to hypoxic stress and contributes to colon tumor growth. ANXA3 may represent a new therapeutic target for colon carcinoma.
This study aimed to construct Bayesian networks (BNs) to analyze the network relationships between COPD and its influencing factors, and the strength of each factor's influence on COPD was reflected through network reasoning. Elastic Net and Max-Min Hill-Climbing (MMHC) algorithm were adopted to screen the variables on the surveillance data of COPD among residents in Shanxi Province, China from 2014 to 2015, and construct BNs respectively. 10 variables finally entered the model after screening by Elastic Net. The BNs constructed by MMHC showed that smoking status, household air pollution, family history, cough, air hunger or dyspnea were directly related to COPD, and Gender was indirectly linked to COPD through smoking status. Moreover, smoking status, household air pollution and family history were the parent nodes of COPD, and cough, air hunger or dyspnea represented the child nodes of COPD. In other words, smoking status, household air pollution and family history were related to the occurrence of COPD, and COPD would make patients’ cough, air hunger or dyspnea worse. Generally speaking, BNs could reveal the complex network linkages between COPD and its relevant factors well, making it more convenient to carry out targeted prevention and control of COPD.
Background Influenza is an acute respiratory infectious disease that is highly infectious and seriously damages human health. Reasonable prediction is of great significance to control the epidemic of influenza. Methods Our Influenza data were extracted from Shanxi Provincial Center for Disease Control and Prevention. Seasonal-trend decomposition using Loess (STL) was adopted to analyze the season characteristics of the influenza in Shanxi Province, China, from the 1st week in 2010 to the 52nd week in 2019. To handle the insufficient prediction performance of the seasonal autoregressive integrated moving average (SARIMA) model in predicting the nonlinear parts and the poor accuracy of directly predicting the original sequence, this study established the SARIMA model, the combination model of SARIMA and Long-Short Term Memory neural network (SARIMA-LSTM) and the combination model of SARIMA-LSTM based on Singular spectrum analysis (SSA-SARIMA-LSTM) to make predictions and identify the best model. Additionally, the Mean Squared Error (MSE), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were used to evaluate the performance of the models. Results The influenza time series in Shanxi Province from the 1st week in 2010 to the 52nd week in 2019 showed a year-by-year decrease with obvious seasonal characteristics. The peak period of the disease mainly concentrated from the end of the year to the beginning of the next year. The best fitting and prediction performance was the SSA-SARIMA-LSTM model. Compared with the SARIMA model, the MSE, MAE and RMSE of the SSA-SARIMA-LSTM model decreased by 38.12, 17.39 and 21.34%, respectively, in fitting performance; the MSE, MAE and RMSE decreased by 42.41, 18.69 and 24.11%, respectively, in prediction performances. Furthermore, compared with the SARIMA-LSTM model, the MSE, MAE and RMSE of the SSA-SARIMA-LSTM model decreased by 28.26, 14.61 and 15.30%, respectively, in fitting performance; the MSE, MAE and RMSE decreased by 36.99, 7.22 and 20.62%, respectively, in prediction performances. Conclusions The fitting and prediction performances of the SSA-SARIMA-LSTM model were better than those of the SARIMA and the SARIMA-LSTM models. Generally speaking, we can apply the SSA-SARIMA-LSTM model to the prediction of influenza, and offer a leg-up for public policy.
Background: Influenza is an acute respiratory infectious disease that is highly infectious and seriously damages human health. Reasonable prediction is of great significance to control the epidemic of influenza. Methods: Our Influenza data were extracted from Shanxi Provincial Center for Disease Control and Prevention. Seasonal-trend decomposition using Loess (STL) was adopted to analyze the season characteristics of the influenza in Shanxi Province, China, from the 1st week in 2010 to the 52nd week in 2019. To handle the insufficient prediction performance of the seasonal autoregressive integrated moving average (SARIMA) model in predicting the nonlinear parts and the poor accuracy of directly predicting the original sequence, this study established the SARIMA model, the combination model of SARIMA and Long-Short Term Memory neural network (SARIMA-LSTM) and the combination model of SARIMA-LSTM based on Singular spectrum analysis (SSA-SARIMA-LSTM) to make predictions and identify the best model. Additionally, the Mean Squared Error (MSE), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were used to evaluate the performance of the models. Results: The influenza time series in Shanxi Province from the 1st week in 2010 to the 52nd week in 2019 showed a year-by-year decrease with obvious seasonal characteristics. The peak period of the disease mainly concentrated from the end of the year to the beginning of the next year. The best fitting and prediction performance was the SSA-SARIMA-LSTM model. Compared with the SARIMA model, the MSE, MAE and RMSE of the SSA-SARIMA-LSTM model decreased by 38.12, 17.39 and 21.34%, respectively, in fitting performance; the MSE, MAE and RMSE decreased by 42.41, 18.69 and 24.11%, respectively, in prediction performances. Furthermore, compared with the SARIMA-LSTM model, the MSE, MAE and RMSE of the SSA-SARIMA-LSTM model decreased by 28.26, 14.61 and 15.30%, respectively, in fitting performance; the MSE, MAE and RMSE decreased by 36.99, 7.22 and 20.62%, respectively, in prediction performances. Conclusions: The fitting and prediction performances of theSSA-SARIMA-LSTM model were better than those of the SARIMA and theSARIMA-LSTM models. Generally speaking, we can apply the SSA-SARIMA-LSTM model to the prediction of influenza, and offer a leg-up for public policy.
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