Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
Central Laboratory, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
Biosci Rep. 2021 Jan 29;41(1). doi: 10.1042/BSR20203944.
Bladder cancer is a common malignant tumour worldwide. Epithelial-mesenchymal transition (EMT)-related biomarkers can be used for early diagnosis and prognosis of cancer patients. To explore, accurate prediction models are essential to the diagnosis and treatment for bladder cancer. In the present study, an EMT-related long noncoding RNA (lncRNA) model was developed to predict the prognosis of patients with bladder cancer. Firstly, the EMT-related lncRNAs were identified by Pearson correlation analysis, and a prognostic EMT-related lncRNA signature was constructed through univariate and multivariate Cox regression analyses. Then, the diagnostic efficacy and the clinically predictive capacity of the signature were assessed. Finally, Gene set enrichment analysis (GSEA) and functional enrichment analysis were carried out with bioinformatics. An EMT-related lncRNA signature consisting of TTC28-AS1, LINC02446, AL662844.4, AC105942.1, AL049840.3, SNHG26, USP30-AS1, PSMB8-AS1, AL031775.1, AC073534.1, U62317.2, C5orf56, AJ271736.1, and AL139385.1 was constructed. The diagnostic efficacy of the signature was evaluated by the time-dependent receiver-operating characteristic (ROC) curves, in which all the values of the area under the ROC (AUC) were more than 0.73. A nomogram established by integrating clinical variables and the risk score confirmed that the signature had a good clinically predict capacity. GSEA analysis revealed that some cancer-related and EMT-related pathways were enriched in high-risk groups, while immune-related pathways were enriched in low-risk groups. Functional enrichment analysis showed that EMT was associated with abundant GO terms or signaling pathways. In short, our research showed that the 14 EMT-related lncRNA signature may predict the prognosis and progression of patients with bladder cancer.
膀胱癌是一种常见的恶性肿瘤，在全球范围内都有发生。上皮-间充质转化（EMT）相关生物标志物可用于癌症患者的早期诊断和预后判断。为了进行探索，建立准确的预测模型对于膀胱癌的诊断和治疗至关重要。在本研究中，构建了一个 EMT 相关的长链非编码 RNA（lncRNA）模型，用于预测膀胱癌患者的预后。首先，通过 Pearson 相关分析鉴定 EMT 相关的 lncRNA，然后通过单因素和多因素 Cox 回归分析构建预后 EMT 相关 lncRNA 特征。接着，评估特征的诊断效能和临床预测能力。最后，通过生物信息学进行基因集富集分析（GSEA）和功能富集分析。构建了一个由 TTC28-AS1、LINC02446、AL662844.4、AC105942.1、AL049840.3、SNHG26、USP30-AS1、PSMB8-AS1、AL031775.1、AC073534.1、U62317.2、C5orf56、AJ271736.1 和 AL139385.1 组成的 EMT 相关 lncRNA 特征。通过时间依赖性接受者操作特征（ROC）曲线评估特征的诊断效能，其中 ROC 曲线下面积（AUC）的所有值均大于 0.73。通过整合临床变量和风险评分建立的诺模图证实，该特征具有良好的临床预测能力。GSEA 分析表明，一些癌症相关和 EMT 相关途径在高风险组中富集，而免疫相关途径在低风险组中富集。功能富集分析表明 EMT 与丰富的 GO 术语或信号通路相关。总之，我们的研究表明，14 个 EMT 相关 lncRNA 特征可能预测膀胱癌患者的预后和进展。
