Aging
Navigate
Submit an Article
Research Paper|Volume 12, Issue 22|pp 22840—22858

Establishing and validating a pathway prognostic signature in pancreatic cancer based on miRNA and mRNA sets using GSVA

Junfeng Zhang1, Jianyou Gu2, Shixiang Guo1, Wenjie Huang2, Yao Zheng1, Xianxing Wang1, Tao Zhang1, Weibo Zhao3, Bing Ni4,5,6, Yingfang Fan2, Huaizhi Wang1
  • 1Institute of Hepatopancreatobiliary Surgery, Chongqing General Hospital, University of Chinese Academy of Sciences, Chongqing 401120, P R China
  • 2Department of Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, Guangdong Province, P R China
  • 3PLA Strategic Support Force Characteristic Medical Center (The 306th Hospital of PLA), Beijing 100101, P R China
  • 4Department of Pathophysiology, College of High Altitude Military Medicine, Third Military Medical University, Chongqing 400038, P R China
  • 5Key Laboratory of Extreme Environmental Medicine, Ministry of Education of China, Chongqing 400038, P R China
  • 6Key Laboratory of High Altitude Medicine, PLA, Chongqing 400038, P R China
* Equal contribution
Received: December 30, 2019Accepted: July 30, 2020Published: November 10, 2020
https://doi.org/10.18632/aging.103965

Copyright: © 2020 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Pancreatic cancer (PC) is a severe disease with the highest mortality rate among various cancers. It is urgent to find an effective and accurate way to predict the survival of PC patients. Gene set variation analysis (GSVA) was used to establish and validate a miRNA set-based pathway prognostic signature for PC (miPPSPC) and a mRNA set-based pathway prognostic signature for PC (mPPSPC) in independent datasets. An optimized miPPSPC was constructed by combining clinical parameters. The miPPSPC, optimized miPPSPC and mPPSPC were established and validated to predict the survival of PC patients and showed excellent predictive ability. Four metabolic pathways and one oxidative stress pathway were identified in the miPPSPC, whereas linoleic acid metabolism and the pentose phosphate pathway were identified in the mPPSPC. Key factors of the pentose phosphate pathway and linoleic acid metabolism, G6PD and CYP2C8/9/18/19, respectively, are related to the survival of PC patients according to our tissue microarray. Thus, the miPPSPC, optimized miPPSPC and mPPSPC can predict the survival of PC patients efficiently and precisely. The metabolic and oxidative stress pathways may participate in PC progression.