Background:Lung adenocarcinoma is one of the most common pathological types of lung malignant tumor with high morbidity and mortality.Long non-coding RNAs are gradually recognized to play crucial roles in tumor occurr...Background:Lung adenocarcinoma is one of the most common pathological types of lung malignant tumor with high morbidity and mortality.Long non-coding RNAs are gradually recognized to play crucial roles in tumor occurrence and development.Necroptosis is a newly established way for cell programmed death,undertaking essential roles in anti-tumor.Therefore,identifying necroptosis-related l ong non-coding RNAs and based on them to evaluate the signatures of l ung adenocarcinoma is essential for patients’survival prediction and therapy.Methods:We collected data from the public database and performed the least absolute shrinkage to construct a 13-lncRNAs prognostic model.Based on the Consensus Clustering,ESTIMATE,CIRERSORT,and weighted gene co-expression network analysis to identify the immune signatures.Results:This study identified a 13-lncRNAs prognostic model.The model’s prediction accuracy was evaluated by receiver operating characteristic and independent-prognosis analysis;besides,a Gene Expression Omnibus dataset was applied for external validation.Furthermore,we analyzed the immune features of subgroups in multiple dimensions.A consensus clustering analysis based on the 41 genes was implemented to separate lung adenocarcinoma patients into two subgroups.We compared the features of subgroups in multiple dimensions,including survival,immune microenvironment,immune cells infiltration and gene co-expression network analysis.Conclusion:W e established a prognosis necroptosis-related risk model to predict lung adenocarcinoma patients’prognosis and systematically understood the correlation between immune and necroptosis.This study can applicate in clinical to predict the prognosis of lung adenocarcinoma patients and provide new insight into lung adenocarcinoma immune therapy.展开更多
文摘Background:Lung adenocarcinoma is one of the most common pathological types of lung malignant tumor with high morbidity and mortality.Long non-coding RNAs are gradually recognized to play crucial roles in tumor occurrence and development.Necroptosis is a newly established way for cell programmed death,undertaking essential roles in anti-tumor.Therefore,identifying necroptosis-related l ong non-coding RNAs and based on them to evaluate the signatures of l ung adenocarcinoma is essential for patients’survival prediction and therapy.Methods:We collected data from the public database and performed the least absolute shrinkage to construct a 13-lncRNAs prognostic model.Based on the Consensus Clustering,ESTIMATE,CIRERSORT,and weighted gene co-expression network analysis to identify the immune signatures.Results:This study identified a 13-lncRNAs prognostic model.The model’s prediction accuracy was evaluated by receiver operating characteristic and independent-prognosis analysis;besides,a Gene Expression Omnibus dataset was applied for external validation.Furthermore,we analyzed the immune features of subgroups in multiple dimensions.A consensus clustering analysis based on the 41 genes was implemented to separate lung adenocarcinoma patients into two subgroups.We compared the features of subgroups in multiple dimensions,including survival,immune microenvironment,immune cells infiltration and gene co-expression network analysis.Conclusion:W e established a prognosis necroptosis-related risk model to predict lung adenocarcinoma patients’prognosis and systematically understood the correlation between immune and necroptosis.This study can applicate in clinical to predict the prognosis of lung adenocarcinoma patients and provide new insight into lung adenocarcinoma immune therapy.
