Establishment Of A Prognostic Immune-Related LncRNA Screening And Prognostic Model For Gastric Cancer Based On TCGA Database And LASSO Regression

ObjectiveIn this study,data mining was performed using gastric cancer data from The Tumor Genome Atlas(TCGA,The Cancer Genome Atlas)database,and immune-related lncRNAs(long-stranded non-coding RNAs)associated with prognosis were screened using bioinformatics analysis,then,Cox regression analysis and LASSO regression algorithm were used to construct a prognostic model for gastric cancer and validate it.MethodsRNA data from gastric cancer patients were obtained using the TCGA database,and lncRNA expression data were screened by the human reference genome.The immune genes were taken from an immune database,and the immune-related lncRNAs were acquired through a gene co-expression network.Differential analysis identified immune-related lncRNAs with differential expression.Afterward,a prognostic risk model was constructed by utilizing Cox regression and LASSO regression algorithms to determine key lncRNAs.The risk model score was utilized to categorize gastric cancer patients into high-risk and low-risk groups based on the median value.Subsequently,survival analysis was conducted to verify the differences in survival,and the model’s validity was confirmed using internal data sets.Finally,univariate and multivariate Cox analyses were performed to evaluate the potential of the risk score as an independent prognostic factor for gastric cancer,in conjunction with each clinical factor.Results1.28 immune lncRNAs associated with prognosis of gastric cancer were obtained after one-way Cox regression analysis method.2.5 lncRNAs were identified for risk modeling using a multifactorial Cox regression analysis method,in which AL590666.2 was considered a protective effector for HR<1,in which AP000695.2,CD44-AS1,GLIS3-AS1,and AL356417.2 were considered risk effectors for HR>1(p<0.05).3.The risk model scores categorized gastric cancer patients into high and low risk groups,and the survival status disparity between the groups was found to be statistically significant(p<0.001).4.Cox regression showed that the risk score could be an independent prognostic factor for gastric cancer patients.ConclusionA risk score model for gastric cancer patients was constructed by screening immune-related lncRNAs associated with patient prognosis.This was accomplished using bioinformatics methods and data from the TCGA database and immune gene library.According to the risk score formula of this model,gastric cancer patients can be divided into high-risk and low-risk groups,and the model can help predict the prognosis of gastric cancer patients.