Background:Establishing an appropriate prognostic model for PCa is essential for its effective treatment.Glycolysis is a vital energy-harvesting mechanism for tumors.Developing a prognostic model for PCa based on glyc...Background:Establishing an appropriate prognostic model for PCa is essential for its effective treatment.Glycolysis is a vital energy-harvesting mechanism for tumors.Developing a prognostic model for PCa based on glycolysis-related genes is novel and has great potential.Methods:First,gene expression and clinical data of PCa patients were downloaded from The Cancer Genome Atlas(TCGA)and Gene Expression Omnibus(GEO),and glycolysis-related genes were obtained from the Molecular Signatures Database(MSigDB).Gene enrichment analysis was performed to verify that glycolysis functions were enriched in the genes we obtained,which were used in nonnegative matrix factorization(NMF)to identify clusters.The correlation between clusters and clinical features was discussed,and the differentially expressed genes(DEGs)between the two clusters were investigated.Based on the DEGs,we investigated the biological differences between clusters,including immune cell infiltration,mutation,tumor immune dysfunction and exclusion,immune function,and checkpoint genes.To establish the prognostic model,the genes were filtered based on univariable Cox regression,LASSO,and multivariable Cox regression.Kaplan–Meier analysis and receiver operating characteristic analysis validated the prognostic value of the model.A nomogram of the risk score calculated by the prognostic model and clinical characteristics was constructed to quantitatively estimate the survival probability for PCa patients in the clinical setting.Result:The genes obtained from MSigDB were enriched in glycolysis functions.Two clusters were identified by NMF analysis based on 272 glycolysis-related genes,and a prognostic model based on DEGs between the two clusters was finally established.The prognostic model consisted of LAMPS,SPRN,ATOH1,TANC1,ETV1,TDRD1,KLK14,MESP2,POSTN,CRIP2,NAT1,AKR7A3,PODXL,CARTPT,and PCDHGB2.All sample,training,and test cohorts from The Cancer Genome Atlas(TCGA)and the external validation cohort from GEO showed significant differences between the high-risk and low-risk groups.The area under the ROC curve showed great performance of this prognostic model.Conclusion:A prognostic model based on glycolysis-related genes was established,with great performance and potential significance to the clinical application.展开更多
This research explores factors influencing Chinese overseas students’career decision-making.Based on the social cognitive career theory,a semi-structured interview schedule was devised to qualitatively investigate ho...This research explores factors influencing Chinese overseas students’career decision-making.Based on the social cognitive career theory,a semi-structured interview schedule was devised to qualitatively investigate how Chinese students evaluated different factors and coped with career decision-making while studying abroad.The interview transcripts were addressed using thematic analysis.Family influences,overseas social life,and personal improvement were found to be the three key factors in shaping Chinese overseas students’career decision-making.Moreover,close interconnections were found among the three factors,and these had a combined impact on the decision-making process.The findings highlight the importance of a positive study abroad experience and its impact on international students’career decision-making.展开更多
Universal health coverage emphasises the value of the community-based delivery of health services to ensure that underserved populations have access to care.In areas where infectious diseases are endemic,there are oft...Universal health coverage emphasises the value of the community-based delivery of health services to ensure that underserved populations have access to care.In areas where infectious diseases are endemic,there are often few resources and limited capacity,and the introduction of effective and accessible strategies require innovation.In this special issue,the contributing authors emphasise the power of local responses to the circumstances that underpin diseases of poverty,and highlight the methodological and programme innovations necessary to support and sustain these responses.Through case studies,the authors illustrate how social innovations can address health inequities,and they identify the role of academics in the Social Innovation in Health Initiative to support this approach.展开更多
基金supported by the Public Health Research Project in Futian District,Shenzhen(Grant Nos.FTWS2020026,FTWS2021073).
文摘Background:Establishing an appropriate prognostic model for PCa is essential for its effective treatment.Glycolysis is a vital energy-harvesting mechanism for tumors.Developing a prognostic model for PCa based on glycolysis-related genes is novel and has great potential.Methods:First,gene expression and clinical data of PCa patients were downloaded from The Cancer Genome Atlas(TCGA)and Gene Expression Omnibus(GEO),and glycolysis-related genes were obtained from the Molecular Signatures Database(MSigDB).Gene enrichment analysis was performed to verify that glycolysis functions were enriched in the genes we obtained,which were used in nonnegative matrix factorization(NMF)to identify clusters.The correlation between clusters and clinical features was discussed,and the differentially expressed genes(DEGs)between the two clusters were investigated.Based on the DEGs,we investigated the biological differences between clusters,including immune cell infiltration,mutation,tumor immune dysfunction and exclusion,immune function,and checkpoint genes.To establish the prognostic model,the genes were filtered based on univariable Cox regression,LASSO,and multivariable Cox regression.Kaplan–Meier analysis and receiver operating characteristic analysis validated the prognostic value of the model.A nomogram of the risk score calculated by the prognostic model and clinical characteristics was constructed to quantitatively estimate the survival probability for PCa patients in the clinical setting.Result:The genes obtained from MSigDB were enriched in glycolysis functions.Two clusters were identified by NMF analysis based on 272 glycolysis-related genes,and a prognostic model based on DEGs between the two clusters was finally established.The prognostic model consisted of LAMPS,SPRN,ATOH1,TANC1,ETV1,TDRD1,KLK14,MESP2,POSTN,CRIP2,NAT1,AKR7A3,PODXL,CARTPT,and PCDHGB2.All sample,training,and test cohorts from The Cancer Genome Atlas(TCGA)and the external validation cohort from GEO showed significant differences between the high-risk and low-risk groups.The area under the ROC curve showed great performance of this prognostic model.Conclusion:A prognostic model based on glycolysis-related genes was established,with great performance and potential significance to the clinical application.
文摘This research explores factors influencing Chinese overseas students’career decision-making.Based on the social cognitive career theory,a semi-structured interview schedule was devised to qualitatively investigate how Chinese students evaluated different factors and coped with career decision-making while studying abroad.The interview transcripts were addressed using thematic analysis.Family influences,overseas social life,and personal improvement were found to be the three key factors in shaping Chinese overseas students’career decision-making.Moreover,close interconnections were found among the three factors,and these had a combined impact on the decision-making process.The findings highlight the importance of a positive study abroad experience and its impact on international students’career decision-making.
文摘Universal health coverage emphasises the value of the community-based delivery of health services to ensure that underserved populations have access to care.In areas where infectious diseases are endemic,there are often few resources and limited capacity,and the introduction of effective and accessible strategies require innovation.In this special issue,the contributing authors emphasise the power of local responses to the circumstances that underpin diseases of poverty,and highlight the methodological and programme innovations necessary to support and sustain these responses.Through case studies,the authors illustrate how social innovations can address health inequities,and they identify the role of academics in the Social Innovation in Health Initiative to support this approach.
