Radiotherapy Department of Shenzhen Baoan People's Hospital, Shenzhen 518101, Guangdong, China.
Oncology Department of ZhuJiang Hospital, Southern Medical University, Guangzhou 510280, Guangdong, China.
Cancer Biomark. 2018;23(1):79-93. doi: 10.3233/CBM-181420.
The purpose of this study was to establish a risk scoring system based on miRNAs to evaluate the prognosis in pancreatic adenocarcinoma.
Using a miRNA microarray dataset (179 pancreatic adenocarcinoma specimens and 4 normal control specimens) from TCGA, differentially expressed miRNAs were identified. Cox proportional hazards regression analysis was used to identify significant prognostic miRNAs, with which a risk scoring system was established and tested on a validation set. Cox regression analysis was performed to identify independent predictors of survival from clinical characteristics. Stratified Cox regression analyses were conducted to unravel the associations of clinical characteristics with survival. Differentially expressed genes (DEGs) were screened followed by functional annotation of the DEGs.
Eight miRNAs (miR-1301, miR-598, miR-1180, miR-155, miR-496, miR-203, miR-193b, miR-135b) were independent predictors for survival. A risk scoring system was established with the 8 signature miRNAs. Upon Cox multivariate regression analysis, risk score, new tumor and targeted molecular therapy were independent predictors of prognosis. Stratified Cox regression analyses found that targeted molecular therapy and new tumor are associated with survival of patients. Survival-related DEGs were significantly enriched with regulation of transforming growth factor beta receptor, potassium ion transport and MAPK signaling pathway.
The study proposes 8-miRNA expression-based risk scoring system to predict prognosis in pancreatic adenocarcinoma. New tumor and targeted molecular therapy were independent predictors of prognosis. Transforming growth factor beta receptor, potassium ion transport and MAPK signaling pathway may be related to prognosis in pancreatic adenocarcinoma.
本研究旨在建立基于 miRNA 的风险评分系统，以评估胰腺腺癌的预后。
使用 TCGA 的 miRNA 微阵列数据集（179 例胰腺腺癌标本和 4 例正常对照标本），鉴定差异表达的 miRNA。采用 Cox 比例风险回归分析鉴定有显著预后意义的 miRNA，由此建立风险评分系统，并在验证集上进行测试。采用 Cox 回归分析从临床特征中鉴定出生存的独立预测因子。进行分层 Cox 回归分析以揭示临床特征与生存的关联。筛选差异表达基因（DEGs），并对 DEGs 进行功能注释。
8 个 miRNA（miR-1301、miR-598、miR-1180、miR-155、miR-496、miR-203、miR-193b、miR-135b）是生存的独立预测因子。建立了一个包含 8 个特征 miRNA 的风险评分系统。经 Cox 多因素回归分析，风险评分、新发肿瘤和靶向分子治疗是预后的独立预测因子。分层 Cox 回归分析发现，靶向分子治疗和新发肿瘤与患者的生存有关。与生存相关的 DEGs 在转化生长因子β受体、钾离子转运和 MAPK 信号通路的调控中显著富集。
本研究提出了基于 8 个 miRNA 表达的风险评分系统，用于预测胰腺腺癌的预后。新发肿瘤和靶向分子治疗是预后的独立预测因子。转化生长因子β受体、钾离子转运和 MAPK 信号通路可能与胰腺腺癌的预后有关。
