Liu Yan, Zhu Dong Yan, Xing Hong Jian, Hou Yi, Sun Yan
1Anesthesiology Department, Jilin Univ, China Japan Union Hosp, 126 Xiantai St, Changchun, 130033 Jilin People's Republic of China.
2Vascular Surgery Department, Jilin Univ, China Japan Union Hosp, 126 Xiantai St, Changchun, 130033 Jilin People's Republic of China.
J Biol Res (Thessalon). 2020 May 19;27:6. doi: 10.1186/s40709-020-00116-3. eCollection 2020 Dec.
This study aimed to construct prognostic model by screening prognostic miRNA signature of bladder cancer.
The miRNA expression profile data of bladder cancer (BC) in The Cancer Genome Atlas (TCGA) were obtained and randomly divided into the training set and the validation set. Differentially expressed miRNAs (DEMs) between BC and normal control samples in the training set were firstly identified, and DEMs related to prognosis were screened by Cox Regression analysis. Then, the MiR Score system was constructed using X-Tile based cutoff points and verified in the validation set. The prognostic clinical factors are selected out by univariate and multivariate Cox Regression analysis. Finally, the mRNAs related to prognosis were screened and the biological pathway analysis was carried out.
We identified the 7-miRNA signature was significantly associated with the patient's Overall Survival (OS). A prognostic model was constructed based on the prognostic 7-miRNA signature, and possessed a relative satisfying predicted ability both in the training set and validation set. In addition, univariate and multivariate Cox Regression analysis showed that age, lymphovascular invasion and MiR Score were considered as independent prognostic factors in BC patients. Furthermore, based on MiR Score prognostic model, several differentially expressed genes (DEGs), such as and , as well as their related biological pathway(s), including cell-cell adhesion and neuroactive ligand-receptor interaction, were considered to be related to BC prognosis.
The prognostic model which was constructed based on the prognostic 7-miRNA signature presented a high predictive ability for BC.
本研究旨在通过筛选膀胱癌的预后性微小RNA特征构建预后模型。
获取癌症基因组图谱（TCGA）中膀胱癌（BC）的微小RNA表达谱数据，并随机分为训练集和验证集。首先在训练集中鉴定BC与正常对照样本之间的差异表达微小RNA（DEM），并通过Cox回归分析筛选与预后相关的DEM。然后，使用基于X-Tile的截断点构建MiR评分系统，并在验证集中进行验证。通过单变量和多变量Cox回归分析筛选出预后临床因素。最后，筛选出与预后相关的mRNA并进行生物学通路分析。
我们鉴定出7个微小RNA特征与患者的总生存期（OS）显著相关。基于预后性7个微小RNA特征构建了一个预后模型，该模型在训练集和验证集中均具有相对令人满意的预测能力。此外，单变量和多变量Cox回归分析表明，年龄、淋巴管浸润和MiR评分被认为是BC患者的独立预后因素。此外，基于MiR评分预后模型，一些差异表达基因（DEG），如 和 ，以及它们相关的生物学通路，包括细胞-细胞黏附和神经活性配体-受体相互作用，被认为与BC预后相关。
基于预后性7个微小RNA特征构建的预后模型对BC具有较高的预测能力。
