目的:通过生物信息学方法研究PAICS对胃癌患者预后影响及其临床意义。方法:应用Kaplan-Meier (https://kmplot.com/analysis/)网站数据库研究PAICS表达与胃癌患者临床预后的相关性。使用TIMER2.0数据库、UALCAN数据库阐明PAICS在胃癌组...目的:通过生物信息学方法研究PAICS对胃癌患者预后影响及其临床意义。方法:应用Kaplan-Meier (https://kmplot.com/analysis/)网站数据库研究PAICS表达与胃癌患者临床预后的相关性。使用TIMER2.0数据库、UALCAN数据库阐明PAICS在胃癌组织中的表达与免疫细胞浸润和相关基因标记物的相关性。结果:PAICS在多种癌症中表达水平高于正常组织,且具有统计学意义(P Objective: This study investigates the prognostic impact and clinical significance of PAICS in gastric cancer patients through bioinformatics approaches. Methods: The Kaplan-Meier (https://kmplot.com/analysis/) database was used to study the correlation between PAICS expression and the clinical prognosis of gastric cancer patients. TIMER2.0 database and UALCAN database were used to clarify the correlation between the expression of PAICS in gastric cancer tissues and immune cell invasion and related gene markers. Results: The expression level of PAICS was higher than that of normal tissues in a variety of cancers and was statistically significant (P < 0.001), including BLCA (bladder urothelial carcinoma), BRCA (invasive carcinoma of the breast), COAD (colon cancer), ESCA (esophageal cancer), LIHC (hepatocellular carcinoma), LUAD (lung adenocarcinoma), LUSC (lung squamous cell carcinoma), PRAD (prostate cancer), STAD (gastric cancer), THCA (thyroid cancer), UCEC (endometrial cancer). These results suggest that high expression of PAICS is associated with poor prognosis of gastric cancer. PAICS expression was negatively correlated with the infiltration of gastric cancer immune cells (B cells, CD4+T cells, CD8+T cells, neutrophils, macrophages and dendritic cells) (P < 0.01). Conclusion: PAICS significantly affects the prognosis of gastric cancer and can be used as a prognostic biomarker for gastric cancer.展开更多
Background:Glioma is a common malignant brain tumor.The purpose of this study was to investigate the role of the transcription factor SPI1 in glioma.Methods:SPI1 expression in glioma was identified using qRT-PCR and W...Background:Glioma is a common malignant brain tumor.The purpose of this study was to investigate the role of the transcription factor SPI1 in glioma.Methods:SPI1 expression in glioma was identified using qRT-PCR and Western blotting.Cell proliferation was assessed using the CCK8 assay.Transwell and wound healing assays were utilized to evaluate cell migration.Additionally,cell cycle and apoptosis were detected using flow cytometry.Results:We observed that the expression level of SPI1 was up-regulated in glioma tissues,compared to normal tissues.Furthermore,we found that SPI1 is able to promote proliferation and migration of glioma cells in vitro.Flow cytometry results demonstrate that,compared to si-NC cells,si-SPI1 cells stagnated in the G1 phase,and downregulation of SPI1 expression is able to increase rates of apoptosis.Double luciferase activity and chromatin immunoprecipitation assay results indicated that SPI1 can bind to the promoter sites and promote the proliferation and migration of glioma cells by regulating the expression of oncogenic PAICS.Conclusions:Our results suggest that SPI1 can promote proliferation and migration of glioma.Furthermore,SPI1 can be utilized as a potential diagnostic marker and therapeutic target for glioma.展开更多
Hardware prefetching and replacement policies are two techniques to improve the performance of the memory subsystem.While prefetching hides memory latency and improves performance,interactions take place with the cach...Hardware prefetching and replacement policies are two techniques to improve the performance of the memory subsystem.While prefetching hides memory latency and improves performance,interactions take place with the cache replacement policies,thereby introducing performance variability in the application.To improve the accuracy of reuse of cache blocks in the presence of hardware prefetching,we propose Prefetch-Adaptive Intelligent Cache Replacement Policy(PAIC).PAIC is designed with separate predictors for prefetch and demand requests,and uses machine learning to optimize reuse prediction in the presence of prefetching.By distinguishing reuse predictions for prefetch and demand requests,PAIC can better combine the performance benefits from prefetching and replacement policies.We evaluate PAIC on a set of 27 memory-intensive programs from the SPEC 2006 and SPEC 2017.Under single-core configuration,PAIC improves performance over Least Recently Used(LRU)replacement policy by 37.22%,compared with improvements of 32.93%for Signature-based Hit Predictor(SHiP),34.56%for Hawkeye,and 34.43%for Glider.Under the four-core configuration,PAIC improves performance over LRU by 20.99%,versus 13.23%for SHiP,17.89%for Hawkeye and 15.50%for Glider.展开更多
文摘目的:通过生物信息学方法研究PAICS对胃癌患者预后影响及其临床意义。方法:应用Kaplan-Meier (https://kmplot.com/analysis/)网站数据库研究PAICS表达与胃癌患者临床预后的相关性。使用TIMER2.0数据库、UALCAN数据库阐明PAICS在胃癌组织中的表达与免疫细胞浸润和相关基因标记物的相关性。结果:PAICS在多种癌症中表达水平高于正常组织,且具有统计学意义(P Objective: This study investigates the prognostic impact and clinical significance of PAICS in gastric cancer patients through bioinformatics approaches. Methods: The Kaplan-Meier (https://kmplot.com/analysis/) database was used to study the correlation between PAICS expression and the clinical prognosis of gastric cancer patients. TIMER2.0 database and UALCAN database were used to clarify the correlation between the expression of PAICS in gastric cancer tissues and immune cell invasion and related gene markers. Results: The expression level of PAICS was higher than that of normal tissues in a variety of cancers and was statistically significant (P < 0.001), including BLCA (bladder urothelial carcinoma), BRCA (invasive carcinoma of the breast), COAD (colon cancer), ESCA (esophageal cancer), LIHC (hepatocellular carcinoma), LUAD (lung adenocarcinoma), LUSC (lung squamous cell carcinoma), PRAD (prostate cancer), STAD (gastric cancer), THCA (thyroid cancer), UCEC (endometrial cancer). These results suggest that high expression of PAICS is associated with poor prognosis of gastric cancer. PAICS expression was negatively correlated with the infiltration of gastric cancer immune cells (B cells, CD4+T cells, CD8+T cells, neutrophils, macrophages and dendritic cells) (P < 0.01). Conclusion: PAICS significantly affects the prognosis of gastric cancer and can be used as a prognostic biomarker for gastric cancer.
基金National Natural Science Foundation of China(Grant No.81802470)Joint construction project of Henan Medical Science and technology research plan(Grant No.LHGJ20190452)+1 种基金the Doctoral Scientific Research Foundation of Xinxiang Medical University(Grant No.XYBSKYZZ201632)Natural Science Foundation of Henan Province(Grant No.202300410326)
文摘Background:Glioma is a common malignant brain tumor.The purpose of this study was to investigate the role of the transcription factor SPI1 in glioma.Methods:SPI1 expression in glioma was identified using qRT-PCR and Western blotting.Cell proliferation was assessed using the CCK8 assay.Transwell and wound healing assays were utilized to evaluate cell migration.Additionally,cell cycle and apoptosis were detected using flow cytometry.Results:We observed that the expression level of SPI1 was up-regulated in glioma tissues,compared to normal tissues.Furthermore,we found that SPI1 is able to promote proliferation and migration of glioma cells in vitro.Flow cytometry results demonstrate that,compared to si-NC cells,si-SPI1 cells stagnated in the G1 phase,and downregulation of SPI1 expression is able to increase rates of apoptosis.Double luciferase activity and chromatin immunoprecipitation assay results indicated that SPI1 can bind to the promoter sites and promote the proliferation and migration of glioma cells by regulating the expression of oncogenic PAICS.Conclusions:Our results suggest that SPI1 can promote proliferation and migration of glioma.Furthermore,SPI1 can be utilized as a potential diagnostic marker and therapeutic target for glioma.
基金supported by the Natural Science Foundation of Beijing under Grant No.4192007the National Natural Science Foundation of China under Grant No.61202076.
文摘Hardware prefetching and replacement policies are two techniques to improve the performance of the memory subsystem.While prefetching hides memory latency and improves performance,interactions take place with the cache replacement policies,thereby introducing performance variability in the application.To improve the accuracy of reuse of cache blocks in the presence of hardware prefetching,we propose Prefetch-Adaptive Intelligent Cache Replacement Policy(PAIC).PAIC is designed with separate predictors for prefetch and demand requests,and uses machine learning to optimize reuse prediction in the presence of prefetching.By distinguishing reuse predictions for prefetch and demand requests,PAIC can better combine the performance benefits from prefetching and replacement policies.We evaluate PAIC on a set of 27 memory-intensive programs from the SPEC 2006 and SPEC 2017.Under single-core configuration,PAIC improves performance over Least Recently Used(LRU)replacement policy by 37.22%,compared with improvements of 32.93%for Signature-based Hit Predictor(SHiP),34.56%for Hawkeye,and 34.43%for Glider.Under the four-core configuration,PAIC improves performance over LRU by 20.99%,versus 13.23%for SHiP,17.89%for Hawkeye and 15.50%for Glider.
