Cuproptosis is a newly discovered form of apoptotic process that is thought to play an important role in cancer therapy.Long non-coding RNA(lncRNA)is involved in regulating many physiological and pathological activiti...Cuproptosis is a newly discovered form of apoptotic process that is thought to play an important role in cancer therapy.Long non-coding RNA(lncRNA)is involved in regulating many physiological and pathological activities of cells.The aim of this study was to investigate the prognostic significance of Cuproptosis-associated lncRNAs in osteosarcoma.Methods:The Gene expression profiling of osteosarcoma samples versus normal samples and corresponding clinical data were downloaded from the public databases UCSC Xena and GTEx,and the cuproptosis gene was obtained from the published literature,the prognostic model of osteosarcoma cuproptosis-related lncRNA was constructed by using coexpression network,minimum absolute contraction and selection algorithm(LASSO)and Cox regression model.Receiver operating characteristic(ROC)curves and nomograms were used to assess the predictive power of the model.Single-sample gene set enrichment analysis(ssGSEA)was used to explore the relationship between osteosarcoma immune cells and function in different risk groups.Results:181 cuproptosis-related lncRNAs were obtained by co-expression analysis of 19 cuproptosis genes collected.Ten lncRNAs were screened out by differential analysis and single-factor Cox analysis.Three cuproptosis-related lncrnas(AC124798.1,AC090152.1,AC090559.1)were screened by Lasso and multivariate Cox regression to construct the prognostic model.Patients were divided into high and low risk groups based on the median risk score.The results of overall survival,risk score distribution and survival status in the lowrisk group were better than those in the high-risk group,and were verified in the internal data.Univariate and multivariate Cox regression analyses showed that risk score was an independent prognostic factor.Nomograms and ROC curves showed that the prognostic model had good predictive ability.The results of ssGSEA suggest that immune cells and function may be inhibited in the high-risk group.Conclusion:The 3 cuproptosis-related lncRNAs may be helpful to guide the prognosis of osteosarcoma patients and provide some theoretical basis for clinical decision.展开更多
Introduction:Osteoarthritis(OA)is still an important health problem,and understanding its pathological mechanisms is essential for its diagnosis and treatment.There is evidence that autophagy may play a role in OA pro...Introduction:Osteoarthritis(OA)is still an important health problem,and understanding its pathological mechanisms is essential for its diagnosis and treatment.There is evidence that autophagy may play a role in OA progression,but the exact mechanism remains unclear.Methods:In this study,we adopted a multi-prong approach to systematically identify the key autophagy-related genes(ARGs)associated with OA.Through weighted gene coexpression network analysis,we initially identified significant gene modules associated with OA.Subsequent differential gene analysis performed on normal and OA specimens.Further analysis later using the MCC algorithm highlighted hub ARGs.These genes were then incorporated into the prediction model of OA.In addition,the expression patterns of these DEGs were verified by in vitro experiments.Results:A total of 104 differentially expressed genes(DEGs)were identified by differential gene analysis,of which 102 were up-regulated and 2 were down-regulated.These differentially expressed genes were closely related to key signaling pathways,such as PI3K-Akt signaling pathway,IL-17 signaling pathway and osteoclast differentiation.Further MCC analysis highlighted 10 hub ARGs,among which ATF3,CYCS,FOXO3,KLF6,NFKBIA and SOCS3 were particularly significant,which were then included in the prediction model of OA,which showed robust prediction ability with an area under the curve of 0.783.In vitro experiments confirmed that the expression pattern of these DEGs was consistent with our prediction.Conclusion:In summary,our comprehensive analysis not only provides new insights into the molecular basis of OA,but also suggests potential biomarkers for its diagnosis.展开更多
基金National Natural Science Foundation Project of China (No.81860793)Natural Science Foundation Project of Guangxi Province (No.2020JJA140375)Guangxi Graduate Education Innovation Program (No.YCSY2022027)。
文摘Cuproptosis is a newly discovered form of apoptotic process that is thought to play an important role in cancer therapy.Long non-coding RNA(lncRNA)is involved in regulating many physiological and pathological activities of cells.The aim of this study was to investigate the prognostic significance of Cuproptosis-associated lncRNAs in osteosarcoma.Methods:The Gene expression profiling of osteosarcoma samples versus normal samples and corresponding clinical data were downloaded from the public databases UCSC Xena and GTEx,and the cuproptosis gene was obtained from the published literature,the prognostic model of osteosarcoma cuproptosis-related lncRNA was constructed by using coexpression network,minimum absolute contraction and selection algorithm(LASSO)and Cox regression model.Receiver operating characteristic(ROC)curves and nomograms were used to assess the predictive power of the model.Single-sample gene set enrichment analysis(ssGSEA)was used to explore the relationship between osteosarcoma immune cells and function in different risk groups.Results:181 cuproptosis-related lncRNAs were obtained by co-expression analysis of 19 cuproptosis genes collected.Ten lncRNAs were screened out by differential analysis and single-factor Cox analysis.Three cuproptosis-related lncrnas(AC124798.1,AC090152.1,AC090559.1)were screened by Lasso and multivariate Cox regression to construct the prognostic model.Patients were divided into high and low risk groups based on the median risk score.The results of overall survival,risk score distribution and survival status in the lowrisk group were better than those in the high-risk group,and were verified in the internal data.Univariate and multivariate Cox regression analyses showed that risk score was an independent prognostic factor.Nomograms and ROC curves showed that the prognostic model had good predictive ability.The results of ssGSEA suggest that immune cells and function may be inhibited in the high-risk group.Conclusion:The 3 cuproptosis-related lncRNAs may be helpful to guide the prognosis of osteosarcoma patients and provide some theoretical basis for clinical decision.
文摘Introduction:Osteoarthritis(OA)is still an important health problem,and understanding its pathological mechanisms is essential for its diagnosis and treatment.There is evidence that autophagy may play a role in OA progression,but the exact mechanism remains unclear.Methods:In this study,we adopted a multi-prong approach to systematically identify the key autophagy-related genes(ARGs)associated with OA.Through weighted gene coexpression network analysis,we initially identified significant gene modules associated with OA.Subsequent differential gene analysis performed on normal and OA specimens.Further analysis later using the MCC algorithm highlighted hub ARGs.These genes were then incorporated into the prediction model of OA.In addition,the expression patterns of these DEGs were verified by in vitro experiments.Results:A total of 104 differentially expressed genes(DEGs)were identified by differential gene analysis,of which 102 were up-regulated and 2 were down-regulated.These differentially expressed genes were closely related to key signaling pathways,such as PI3K-Akt signaling pathway,IL-17 signaling pathway and osteoclast differentiation.Further MCC analysis highlighted 10 hub ARGs,among which ATF3,CYCS,FOXO3,KLF6,NFKBIA and SOCS3 were particularly significant,which were then included in the prediction model of OA,which showed robust prediction ability with an area under the curve of 0.783.In vitro experiments confirmed that the expression pattern of these DEGs was consistent with our prediction.Conclusion:In summary,our comprehensive analysis not only provides new insights into the molecular basis of OA,but also suggests potential biomarkers for its diagnosis.
