Objective Pancreatic cancer is one of the most deadly cancers, which is characterized by its high metastatic potential. S100A4 is a major prometastatic protein involved in tumor invasion and metastasis which precise r...Objective Pancreatic cancer is one of the most deadly cancers, which is characterized by its high metastatic potential. S100A4 is a major prometastatic protein involved in tumor invasion and metastasis which precise role in pancreatic cancer has not been fully investigated. We knocked down the S100A4 gene in the Bxpc-3 pancreatic cancer cell line via RNA interference to study the changes in cell behavior. Methods Real-time polymerase chain reaction and western blotting were used to detect mRNA and protein expression levels of S100A4, matrix metalloproteinase （MMP）-2, E-cadherin and thrombospondin （TSP）-I. Transwell chambers were used to detect the migration and invasion abilities; a cell adhesion assay was used to detect adhesion ability; colony forming efficiency was used to detect cell proliferation; flow cytometry was used to detect apoptosis. Results S100A4 mRNA expression was reduced to 17% after transfection with SIOOA4-siRNA, and protein expression had a similar trend, mRNA and protein expression of MMP-2 was reduced and that of E-cadherin and TSP-1 was elevated, indicating that S100A4 affects their expression. S100A4-silenced cells exhibited a marked decrease in migration and invasiveness and increased adhesion, whereas overall proliferation and apoptosis were not overtly altered. Conclusion S100A4 and its downstream factors play important roles in pancreatic cancer invasion, and silencing AIOOA4 can significantly contain the invasiveness of pancreatic cancer.展开更多
High-dimensional and sparse(HiDS)matrices commonly arise in various industrial applications,e.g.,recommender systems(RSs),social networks,and wireless sensor networks.Since they contain rich information,how to accurat...High-dimensional and sparse(HiDS)matrices commonly arise in various industrial applications,e.g.,recommender systems(RSs),social networks,and wireless sensor networks.Since they contain rich information,how to accurately represent them is of great significance.A latent factor(LF)model is one of the most popular and successful ways to address this issue.Current LF models mostly adopt L2-norm-oriented Loss to represent an HiDS matrix,i.e.,they sum the errors between observed data and predicted ones with L2-norm.Yet L2-norm is sensitive to outlier data.Unfortunately,outlier data usually exist in such matrices.For example,an HiDS matrix from RSs commonly contains many outlier ratings due to some heedless/malicious users.To address this issue,this work proposes a smooth L1-norm-oriented latent factor(SL-LF)model.Its main idea is to adopt smooth L1-norm rather than L2-norm to form its Loss,making it have both strong robustness and high accuracy in predicting the missing data of an HiDS matrix.Experimental results on eight HiDS matrices generated by industrial applications verify that the proposed SL-LF model not only is robust to the outlier data but also has significantly higher prediction accuracy than state-of-the-art models when they are used to predict the missing data of HiDS matrices.展开更多
文摘Objective Pancreatic cancer is one of the most deadly cancers, which is characterized by its high metastatic potential. S100A4 is a major prometastatic protein involved in tumor invasion and metastasis which precise role in pancreatic cancer has not been fully investigated. We knocked down the S100A4 gene in the Bxpc-3 pancreatic cancer cell line via RNA interference to study the changes in cell behavior. Methods Real-time polymerase chain reaction and western blotting were used to detect mRNA and protein expression levels of S100A4, matrix metalloproteinase （MMP）-2, E-cadherin and thrombospondin （TSP）-I. Transwell chambers were used to detect the migration and invasion abilities; a cell adhesion assay was used to detect adhesion ability; colony forming efficiency was used to detect cell proliferation; flow cytometry was used to detect apoptosis. Results S100A4 mRNA expression was reduced to 17% after transfection with SIOOA4-siRNA, and protein expression had a similar trend, mRNA and protein expression of MMP-2 was reduced and that of E-cadherin and TSP-1 was elevated, indicating that S100A4 affects their expression. S100A4-silenced cells exhibited a marked decrease in migration and invasiveness and increased adhesion, whereas overall proliferation and apoptosis were not overtly altered. Conclusion S100A4 and its downstream factors play important roles in pancreatic cancer invasion, and silencing AIOOA4 can significantly contain the invasiveness of pancreatic cancer.
基金supported in part by the National Natural Science Foundation of China(61702475,61772493,61902370,62002337)in part by the Natural Science Foundation of Chongqing,China(cstc2019jcyj-msxmX0578,cstc2019jcyjjqX0013)+1 种基金in part by the Chinese Academy of Sciences“Light of West China”Program,in part by the Pioneer Hundred Talents Program of Chinese Academy of Sciencesby Technology Innovation and Application Development Project of Chongqing,China(cstc2019jscx-fxydX0027)。
文摘High-dimensional and sparse(HiDS)matrices commonly arise in various industrial applications,e.g.,recommender systems(RSs),social networks,and wireless sensor networks.Since they contain rich information,how to accurately represent them is of great significance.A latent factor(LF)model is one of the most popular and successful ways to address this issue.Current LF models mostly adopt L2-norm-oriented Loss to represent an HiDS matrix,i.e.,they sum the errors between observed data and predicted ones with L2-norm.Yet L2-norm is sensitive to outlier data.Unfortunately,outlier data usually exist in such matrices.For example,an HiDS matrix from RSs commonly contains many outlier ratings due to some heedless/malicious users.To address this issue,this work proposes a smooth L1-norm-oriented latent factor(SL-LF)model.Its main idea is to adopt smooth L1-norm rather than L2-norm to form its Loss,making it have both strong robustness and high accuracy in predicting the missing data of an HiDS matrix.Experimental results on eight HiDS matrices generated by industrial applications verify that the proposed SL-LF model not only is robust to the outlier data but also has significantly higher prediction accuracy than state-of-the-art models when they are used to predict the missing data of HiDS matrices.
