Genetic Prognostic Risk Analysis of Lung Adenocarcinoma with Lasso-Cox Regression Model
Objective To screen differentially expressed genes in lung adenocarcinoma by constructing Lasso-Cox model to provide potential gene targets for the research of lung adenocarcinoma and new directions for clinical diagnosis,treatment and prognosis by calculating patient risk score and constructing prediction model of lung adenocarcinoma.Methods The gene expression and clinical data of lung adenocarcinoma were downloaded from the Cancer Genome Atlas(TCGA)and Gene Expression Omnibus database(GEO).The TCGA database was used to train model,and the two databases were combined for model validation.The screened differentially expressed genes(DEGs)of lung adenocarcinoma were analyzed by univariate Cox and multivariate Lasso-Cox to construct a risk score prognosis model.Risk score from the final Cox prediction model and clinical data were combined to determine independent prognostic factors.GO enrichment analysis,KEGG pathway analysis and CIBERSORTx immunoassay were used to evaluate the biological interpretation of differentially expressed genes in the risk model.Results The analysis using univarate Cox and Lasso-Cox regreesion identified 9 differentially expressed genes associated with the prognosis of lung adenocarcinoma.Multivariate Cox regression analysis,incorporating clinical data,revealed that a history of malignant tumors,N stage,T stage,and the risk score were independent prognostic factors.Conclusion The prognositic model of lung adenocarcinoma can effectively predict the prognosis risk and provide a theoretical basis for clinical decision-making and personalized treatment.
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