Downregulation of RPL6 by siRNA Inhibits Proliferation and Cell Cycle Progression of Human Gastric Cancer Cell Lines

Our previous study revealed that human ribosomal protein L6 (RPL6) was up-regulated in multidrug-resistant gastric cancer cells and over-expression of RPL6 could protect gastric cancer from drug-induced apoptosis. It was further demonstrated that up-regulation of RPL6 accelerated growth and enhanced in vitro colony forming ability of GES cells while down-regulation of RPL6 exhibited the opposite results. The present study was designed to investigate the potential role of RPL6 in therapy of gastric cancer for clinic. The expression of RPL6 and cyclin E in gastric cancer tissues and normal gastric mucosa was evaluated by immunohistochemisty. It was found that RPL6 and cyclin E were expressed at a higher level in gastric cancer tissues than that in normal gastric mucosa and the two were correlative in gastric cancer. Survival time of postoperative patients was analyzed by Kaplan- Meier analysis and it was found that patients with RPL6 positive expression showed shorter survival time than patients that with RPL6 negative expression. RPL6 was then genetically down-regulated in gastric cancer SGC7901 and AGS cell lines by siRNA. It was demonstrated that down-regulation of RPL6 reduced colony forming ability of gastric cancer cells in vitro and reduced cell growth in vivo. Moreover, down-regulation of RPL6 could suppress G1 to S phase transition in these cells. Further, we evidenced that RPL6 siRNA down-regulated cyclin E expression in SGC7901 and AGS cells. Taken together, these data suggested that RPL6 was over-expressed in human gastric tissues and caused poor prognosis. Down-regulation of RPL6 could suppress cell growth and cell cycle progression at least through down-regulating cyclin E and which might be used as a novel approach to gastric cancer therapy

MiR-218 Inhibits Invasion and Metastasis of Gastric Cancer by Targeting the Robo1 Receptor

MicroRNAs play key roles in tumor metastasis. Here, we describe the regulation and function of miR-218 in gastric cancer (GC) metastasis. miR-218 expression is decreased along with the expression of one of its host genes, Slit3 in metastatic GC. However, Robo1, one of several Slit receptors, is negatively regulated by miR-218, thus establishing a negative feedback loop. Decreased miR-218 levels eliminate Robo1 repression, which activates the Slit-Robo1 pathway through the interaction between Robo1 and Slit2, thus triggering tumor metastasis. The restoration of miR-218 suppresses Robo1 expression and inhibits tumor cell invasion and metastasis in vitro and in vivo. Taken together, our results describe a Slit-miR-218-Robo1 regulatory circuit whose disruption may contribute to GC metastasis. Targeting miR-218 may provide a strategy for blocking tumor metastasis

Joint Probability Distribution of Typhoon Disaster Chain "Strong Wind-Rainstorm- Storm Surge" Based on C-Vine Copula Function

Typhoons and their associated disaster chains pose serious threats to the lives and property of coastal residents, and they remain a focal point for research and response. Previous studies on typhoon disaster chains often employed high-dimensional symmetric Copula models to establish the joint distribution of multiple hazard factors, however they failed to explore the complex nonlinear and asymmetric dependencies among them. This study aimed to depict these complex relationships more comprehensively and efficiently to provide a more accurate typhoon hazard assessment. Focusing on Zhoushan, a city comprising numerous islands in Zhejiang Province that faces multiple typhoon threats, this study employed the C-Vine Copula function to model the complex dependencies among "strong wind-rainstorm-storm surge" in the typhoon disaster chain. Utilizing observational data from 1979 to 2018, this study involves three main steps: first, fitting the marginal distribution of each hazard factor and identifying the best one from Lognormal, Gamma, GEV (Generalized Extreme Value), and Burr functions based on the K-S test; second, fitting the bivariate joint distributions of wind speed-rainfall and wind speed-storm surge using Gaussian, Clayton, Gumbel, Frank, and Joe Copula functions, and determining the best fit based on the AIC (Akaike Information Criterion); and finally, estimating the trivariate joint probability distribution and corresponding return periods for wind speed-rainfall-storm surge using the C-Vine Copula function. This revealed (1) a strong correlation between wind speed and rainfall observed within regular value ranges (non-extreme conditions), were best represented by the Frank Copula, In addition, wind speed and storm surge exhibit an upper-tail dependence, best captured by the Gumbel Copula. (2) The rainfall distribution under certain wind speed conditions revealed dual peaks, whereas the storm surge distribution maintained a uniform pattern, with the best joint distribution fitting the Gumbel Copula. (3) Considering a 100-year return period for individual variables, the bivariate return periods for wind speed-rainfall and wind speed-storm surge events were significantly reduced to 29 and 30 years, respectively, while the trivariate return period for the wind speed-rainfall-storm surge combination was further reduced to 17 years. Overall, the C-Vine Copula function effectively characterizes the complex nonlinear and asymmetric dependencies among the typhoon disaster chain "strong wind-rainstorm-storm surge", reducing high-dimensional parameter estimation complexity. This method provides new insights for constructing joint probability and return period models for multiple hazard factors and offers a scientific basis for disaster risk assessment and management strategies. Therefore, this enhances the accuracy of disaster prevention and mitigation efforts. Additionally, the application of the C-Vine Copula assists to deeply understand the mechanisms and development processes of natural disasters, providing new tools for on-site emergency response and decision-making

Probability Prediction Approach of Fatigue Failure for the Subsea Wellhead Using Bayesian Regularization Artificial Neural Network

The subsea wellhead (SW) system is a crucial connection between blowout preventors (BOPs) and subsea oil and gas wells. Excited by cyclical fatigue dynamic loadings, the SW is prone to fatigue failure, which would lead to the loss of well integrity and catastrophic accidents. Based on the Bayesian Regularization Artificial Neuron Network (BRANN), this paper proposes an efficient probability approach to predict the fatigue failure probability of SW during its entire life. In the proposed method, the BRANN fatigue damage (BRANN-FD) model reflecting the non-linear relationship between the input and output data was developed by the limited fatigue damage analysis data, which was utilized to generate thousands of non-numerical fatigue damage data of SW rapidly. Combining parametric and non-parametric estimation methods, the probability density function (PDF) of SW fatigue damage was determined to calculate the accumulation fatigue damage during service life. Using the logistic regression, the fatigue failure probability of SW was predicted. The application of the proposed approach was demonstrated by a case study. The results illustrated that the fatigue damage of SW would be viewed as obeying the Lognormal distribution, which could be used to obtain the accumulation fatigue damage in operation conveniently. Furthermore, the fatigue failure probability of SW nonlinearly increased with the increment in the accumulation fatigue damage of SW, which could be helpful to ensure the operation safety of SW in deepwater oil and gas development, especially for aged wellhead

Probability Prediction Approach of Fatigue Failure for the Subsea Wellhead Using Bayesian Regularization Artificial Neural Network

The subsea wellhead (SW) system is a crucial connection between blowout preventors (BOPs) and subsea oil and gas wells. Excited by cyclical fatigue dynamic loadings, the SW is prone to fatigue failure, which would lead to the loss of well integrity and catastrophic accidents. Based on the Bayesian Regularization Artificial Neuron Network (BRANN), this paper proposes an efficient probability approach to predict the fatigue failure probability of SW during its entire life. In the proposed method, the BRANN fatigue damage (BRANN-FD) model reflecting the non-linear relationship between the input and output data was developed by the limited fatigue damage analysis data, which was utilized to generate thousands of non-numerical fatigue damage data of SW rapidly. Combining parametric and non-parametric estimation methods, the probability density function (PDF) of SW fatigue damage was determined to calculate the accumulation fatigue damage during service life. Using the logistic regression, the fatigue failure probability of SW was predicted. The application of the proposed approach was demonstrated by a case study. The results illustrated that the fatigue damage of SW would be viewed as obeying the Lognormal distribution, which could be used to obtain the accumulation fatigue damage in operation conveniently. Furthermore, the fatigue failure probability of SW nonlinearly increased with the increment in the accumulation fatigue damage of SW, which could be helpful to ensure the operation safety of SW in deepwater oil and gas development, especially for aged wellhead

A Novel lncRNA, LINC00460, Affects Cell Proliferation and Apoptosis by Regulating KLF2 and CUL4A Expression in Colorectal Cancer

Emerging evidence has proven that long noncoding RNAs (lncRNAs) play important roles in human colorectal cancer (CRC) biology, although few lncRNAs have been characterized in CRC. Therefore, the functional significance of lncRNAs in the malignant progression of CRC still needs to be further explored. In this study, through analyzing TCGA RNA sequencing data and other publicly available microarray data, we found a novel lncRNA, LINC00460, whose expression was significantly upregulated in CRC tissues compared to adjacent normal tissues. Consistently, real-time qPCR results also verified that LINC00460 was overexpressed in CRC tissues and cells. Furthermore, high LINC00460 expression levels in CRC specimens were correlated with larger tumor size, advanced tumor stage, lymph node metastasis and shorter overall survival. In vitro and in vivo assays of LINC00460 alterations revealed a complex integrated phenotype affecting cell growth and apoptosis. Mechanistically, LINC00460 repressed Krüppel-like factor 2 (KLF2) transcription by binding to enhancer of zeste homolog 2 (EZH2). LINC00460 also functioned as a molecular sponge for miR-149-5p, antagonizing its ability to repress cullin 4A (CUL4A) protein translation. Taken together, our findings support a model in which the LINC00460/EZH2/KLF2 and LINC00460/miR-149-5p/CUL4A crosstalk serve as critical effectors in CRC tumorigenesis and progression, suggesting new therapeutic directions in CRC. Keywords: LINC00460, KLF2, CUL4A, proliferation, apoptosis, colorectal cance