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

Aging
Junfeng ZhangHuaizhi Wang

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.

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Datasets Mentioned

BETA
GSE62452
GSE57495
GSE21501

Methods Mentioned

BETA
miRNA-seq
protein array
RNA-seq
biopsies

Software Mentioned

GSEA
miRWalk Pathways
miEAA
survivalROC
survminer R
GSVA
GEPIA2
R
pheatmap
gene set enrichment analysis ( GSEA )

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