Cuproptosis-related long noncoding RNAs predicts overall survival and reveal immune microenvironment of bladder...

Recently, a newly programmed cell death has been discovered, namely cuproptosis. It is considered a novel copper-dependent cell death model. Long non-coding RNA (lncRNA) influence the prognosis of bladder cancer. In this study, we established a scoring system based on 7 cuproptosis-related lncRNA to predict the prognosis and immune landscape of bladder cancer (BCa).

Gene expression and clinical data of 431 tissues were downloaded from The Cancer Genome Atlas (TCGA), including 19 normal samples and 419 cancer samples. All samples were randomly categorized into train and test cohorts. Cuproptosis-related lncRNA were distinguished. Then we conduct univariate COX and multivariate COX regression, paralleled with LASSO regression to cultivate a cuproptosis-related lncRNA risk model. Kaplan-Meier curves, scatter diagram, C-index, ROC curves, nomogram, PCA analysis and univariate and multivariate Cox regression were used to test the accuracy of risk model and to predict patient survival. Additional, gene mutation status between high- and low-risk groups was calculated.GO and KEGG were used to access the DEGs (different expression genes)-related pathway.The ssGSEA and ESTIMATE algorithms were used to assess the immune function in different tumor samples. Besides, patient's response to immunotherapy and drug susceptibility were also been estimated.

7 cuproptosis-related lncRNA (LINC01184, LINC00513, LINC02443, SMARCA5-AS1, BDNF-AS, SOD2-OT1, HYI-AS1) were selected to construct the risk model in the train cohort. This model can well predict the overall survival (OS) in test group and entire cohort with different stage. Despite no significant different is observed in gene mutation between high- and low-risk group, different immune infiltration, different survival and sensitivity to drugs are discovered.

We established a novel cuproptosis-related lncRNA risk model which can predict the outcome and immunotherapy response with satisfactory predictive effects. This risk model can provide a new insight into prognostic evaluation and may have potential to guide comprehensive treatment in bladder cancer.