Establishment Of A Bladder Cancer Prognostic Risk Model Based On Endoplasmic Reticulum Stress-related LncRNAs

Objective:Bladder cancer is a common tumor in the urinary system,and its heterogeneity leads to significant differences in clinical manifestations among different patients.Although there has been progressing in the diagnosis and treatment of bladder cancer in recent years,the prognosis for bladder cancer patients remains to be resolved.With the deepening of basic research,the endoplasmic reticulum stress response has gradually attracted the attention of researchers.It participates in regulating the proliferation and apoptosis of tumor cells,and long non-coding RNAs play a crucial role in this process,which is closely related to the staging and prognosis of tumors.Therefore,this study aimed to construct and validate a prognosis model based on endoplasmic reticulum stress-related genes to evaluate the clinical prognosis of bladder cancer patients and to identify effective diagnostic indicators,providing a reference for developing personalized treatment strategies.Methods:This study utilized transcriptome expression profiles and clinical data from 412 bladder cancer patients and 19 adjacent normal tissues from the TCGA database.In this study,we retrieved the endoplasmic reticulum stress-related gene sets from the Molecular Signatures Database(MSig DB)and selected 13 sets of endoplasmic reticulum stress-related genes as the initial data.The study analyzed the correlation between endoplasmic reticulum stress-related genes and long non-coding RNAs(lncRNAs)in bladder cancer patients and identified endoplasmic reticulum stress-related lncRNAs.Following this,we conducted a correlation analysis between endoplasmic reticulum stress-related genes and lncRNAs in bladder cancer patients and identified lncRNAs that were associated with endoplasmic reticulum stress.Next,we randomly assigned bladder cancer patients to training and validating groups.The data from the training group was used to develop the model,while the data from the validating group and the entire population of patients were used to validate the model’s accuracy.We then utilized univariate and multivariate Cox proportional hazard regression analysis,as well as the LASSO method,to construct an endoplasmic reticulum stress-related lncRNA model.The model calculated the risk score,and patients were divided into high and low-risk groups based on the median risk score.Lastly,survival analysis was employed to validate the survival difference between the high and low-risk groups,and a comparative analysis was performed with other clinical factors to evaluate the prognostic value of the constructed model.Results:The study identified 45 endoplasmic reticulum stress-related lncRNAs that are associated with prognosis through univariate Cox regression analysis.By using multivariate Cox regression analysis,the study established a model consisting of 10 endoplasmic reticulum stress-related lncRNAs.According to the risk model,bladder cancer patients were categorized into high and low-risk groups based on the median risk score,and there was a statistically significant difference in the survival status between the two groups(p < 0.001).Conclusion:Based on the gene expression data and survival data of patients from TCGA,endoplasmic reticulum stress(ERS)-related lncRNAs associated with the prognosis of bladder cancer were screened by integrating the GSEA gene library.A bladder cancer prognosis risk model was constructed by using the screened ERS-related lncRNAs,and a corresponding risk-scoring formula was derived.This risk model can be utilized to predict the prognosis of bladder cancer patients.