Construction Of Inflammation-related LncRNA Model Based On TCGA Database To Predict The Prognosis Of Bladder Urothelial Carcinoma And Its Correlation Analysis

Objective:Bladder cancer is a prevalent urinary malignancy,and its tendency to recur and develop treatment resistance is a significant clinical problem.Identifying potential effective treatment targets and prognostic markers is crucial.This study aims to identify inflammation-related lncRNAs and develop a prognostic model to predict the clinical outcomes of bladder cancer patients.Methods:Gene expression profiles of normal and cancerous bladder tissue samples and the clinical status of the samples were obtained from the TCGA database.Two clusters were identified based on the univariate Cox regression analysis results,and a risk prognosis model of inflammation-related Lnc RNAs was identified and constructed using LASSO-COX regression analysis.The accuracy of the constructed model’s predictive ability was verified using survival analysis.Subsequently,a nomogram was established to predict the prognosis of bladder cancer patients.Biological differences were studied using GSEA.The composition and infiltration levels of 22 invasive immune cell types among different groups were analyzed using Ciber Sort.Results:Based on univariate cox regression,two subtypes were identified using 25 Lnc RNAs that were prognostically associated.Enrichment analysis showed that subtype 1 was associated with immune pathways.Further analysis revealed a negative correlation between immune infiltration levels and patient prognosis.Then,a risk model was constructed using a set of nine inflammation-related Lnc RNAs.KaplanMeier and ROC curves demonstrated that the model had reliable predictive performance in the training,testing,and validation cohorts.The inflammation-related Lnc RNA model had a higher predictive value than other clinical features.Analysis of immune cell infiltration and GSEA further confirmed that the predictive model was significantly associated with the immune status of bladder cancer patients.Conclusions:The constructed ILRs can serve as independent prognostic factors with strong predictive ability.This model may help evaluate the prognosis and immune status of bladder cancer patients and has the potential to become a therapeutic target.The risk prognosis model can accurately predict the clinical outcomes of bladder cancer patients,and the nomogram can further enhance its predictive ability.The research results provide valuable insights into bladder cancer’s pathogenesis and immunological characteristics.