Daoqin Dou1, Shaohua Yang1, Yunzheng Lin1, Jiren Zhang2. 1. Radiotherapy Department of Shenzhen Baoan People's Hospital, Shenzhen 518101, Guangdong, China. 2. Oncology Department of ZhuJiang Hospital, Southern Medical University, Guangzhou 510280, Guangdong, China.
Abstract
PURPOSE: The purpose of this study was to establish a risk scoring system based on miRNAs to evaluate the prognosis in pancreatic adenocarcinoma. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
PURPOSE: The purpose of this study was to establish a risk scoring system based on miRNAs to evaluate the prognosis in pancreatic adenocarcinoma. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Authors: Sameer Abdallah Dhayat; Max Michael Traeger; Jan Rehkaemper; Anda Jana Stroese; Konrad Steinestel; Eva Wardelmann; Iyad Kabar; Norbert Senninger Journal: Cancers (Basel) Date: 2018-09-13 Impact factor: 6.639