Establishment and analysis of a novel miRNA prognostic risk model for bladder cancer based on TCGA database

膀胱癌是最常见的泌尿道肿瘤，并且由于缺乏可靠的诊断标记物，大多数膀胱癌被诊断为晚期。本研究在TCGA数据库中分析了膀胱癌患者的转录组和临床数据，经过预处理后将其分为试验组和验证组。首先，通过单变量Cox回归分析确定与膀胱癌预后密切相关的miRNA，然后通过多变量Cox回归分析建立基于miRNA表达谱的预后风险模型。通过ROC分析确定患者分类风险水平的最佳分界点，并在验证组和整个组中通过Kaplan-Meier估计来验证预后风险评分类型。 从miRTarBase，targetScan和miRDB的三个miRNA库中至少选择了两个可能的靶基因，并进行了KEGG途径和GO富集分析。所有这些交叉基因都是通过PPI网络和生存分析构建的。最后，建立了5-miRNA（hsa-miR-338-5p，hsa-miR-218-1-3p，hsa-miR-17-3p，hsa-miR-6728-5p，hsa-miR-10a- 3p）已成功构建。多变量Cox分析显示，它具有良好的可预测性，可作为预测膀胱癌患者预后和生存的新生物标志物，并可通过IGF1发挥其生物学功能。

Bladder cancer is the most common urinary tract neoplasm. Due to the lack of reliable diagnostic biomarkers, most cases of bladder cancer are diagnosed at an advanced stage. This study analyzed transcriptomic and clinical data of bladder cancer patients from the TCGA database. After preprocessing, the dataset was divided into a training cohort and a validation cohort. First, univariate Cox regression analysis was conducted to identify miRNAs closely associated with the prognosis of bladder cancer. Subsequently, a prognostic risk model based on miRNA expression profiles was established via multivariate Cox regression analysis. The optimal cutoff value for stratifying patients into risk groups was determined via ROC analysis, and the prognostic performance of the risk score was validated using Kaplan-Meier estimation in both the validation cohort and the entire cohort. Putative target genes were predicted using at least two of the three miRNA target prediction databases, namely miRTarBase, TargetScan, and miRDB. KEGG pathway and GO enrichment analyses were performed for these overlapping genes. All these overlapping genes were further screened via PPI network construction and survival analysis. Finally, a 5-miRNA prognostic risk model comprising hsa-miR-338-5p, hsa-miR-218-1-3p, hsa-miR-17-3p, hsa-miR-6728-5p, and hsa-miR-10a-3p was successfully established. Multivariate Cox regression analysis demonstrated that this model exhibits excellent predictive performance, serving as a novel biomarker for predicting the prognosis and survival of bladder cancer patients, and it exerts its biological functions via the IGF1 pathway.