A Study On The Construction Of A Prognostic Prediction Model For Bladder Cancer Based On Metabolism-Related LncRNA

Background: Bladder cancer(BLCA)is the most common urological tumor.Although there are currently abundant treatment methods for bladder cancer based on classification and grading,the prognosis of some patients undergoing radical surgery for bladder cancer is still unsatisfactory.An increasing number of research results have proved that long non-coding RNA(lncRNA)can influence the tumorigenesis and tumor development by regulating gene expression from multiple levels and ways.Generally,the tumorigenesis process is accompanied by changes in metabolic patterns,known as metabolic reprogramming,which is widely regarded as an emerging hallmark of cancer cells now.Objective: we constructed a metabolic-related lncRNA prognostic model and drew a nomogram to evaluate the prognosis of bladder cancer patients.Methods: We downloaded RNA-Seq data(including expression data of lncRNA and m RNA)as well as clinical information of BLCA patients from TCGA database.The metabolism-related genes were obtained from the Molecular Signatures Database(MSig DB).Using Pearson correlation analysis obtained metabolism-related lncRNAs in R(Version 4.0.3).Cox regression and least absolute shrinkage and selection operator(LASSO)regression were utilized to identify metabolism-related lncRNAs related to prognosis in BLCA.Cox regression and LASSO were utilized to identify metabolismrelated lncRNAs related to prognosis.Then,a prognostic signature based on the expression levels ofmetabolism-related lncRNAs and multivariate Cox regression coefficient was developed,and each patient’s risk score was calculated.The Wilcoxon signed-rank test was to assess the correlation between clinicopathological variables and risk score.We further constructed the prognostic nomogram and evaluated the predictive ability by ROC curve and C-index.Finally,we utilized Cytoscape(Version3.9.0)to construct a lncRNA-m RNA co-expression network to distinguish the effects of these lncRNAs on bladder cancer and performed GSEA to reveal the potential molecular mechanism.Result: In this study we identified 8 metabolism-related lncRNA(AC062017.1,AC073534.1,AC099518.2,AC104564.3,AP002884.1,LINC01637,MAFG-DT,USP30-AS1),and then constructed a prognostic signature.In both the TCGA training cohort and TCGA whole cohorts,the metabolism-related lncRNA prognostic signature was verified.The risk score based on prognostic signature was substantially correlated with advanced clinical stage,T stage,N stage,and M stage.The prognostic signature accurately categorized BLCA patients into high-and low-risk groups by median risk score,and it was an independent predictor for the prognosis of BLCA.Then,using metabolism-related lncRNA prognostic signature and clinicopathologic variables to construct a nomogram,which has high predictive accuracy(area under ROC curve and C-index of 0.761 and 0.729,respectively)and the ability to accurately predict 1-year,3-year,and 5-year survival probability of BLCA patients.Furthermore,we built an m RNA-lncRNA co-expression network with 14 m RNA-lncRNA pairs,AP002884.1and MAFG-DT were found to be risk factors for bladder cancer patients,while the remaining six metabolic-related lncRNAs were protective factors for bladder cancer patients.Finally,we utilized GSEA and discovered that the pathways related to cancer and metabolism were enriched in high-risk BLCA patients.Conclusion: 8-lncRNA prognostic signature can reliably predict the prognosis of BCLA patients and guide clinical decisions,and metabolism-related lncRNAs are promising to act as novel diagnostic biomarkers for BCLA patients.