Development of a 12-biomarkers-based Prognostic Model for Pancreatic Cancer using Multi-omics Integrated Analysis

Abstract

Pancreatic cancer is one of the most malignant tumors of the digestive system, with insidious, rapid onset and high mortality. The 5-year survival rate is only 10%. Therefore, in-depth exploration of the potential mechanism affecting the prognosis of pancreatic cancer, and search for biomarkers that can effectively predict the prognosis of pancreatic cancer are of practical clinical importance. The mRNA sequencing data, miRNA sequencing data, methylation data and SNP data of pancreatic cancer patients available in The Cancer Genome Atlas (TCGA) were used for analysis to identify biomarkers that significantly affect the prognosis for the patients. Finally, a prognostic prediction model was developed using principal component analysis (PCA) method. The genes that significantly affected the prognosis of pancreatic cancer were as follows: 5 DmiRNAs (hsa-mir-1179, hsa-mir-1224, hsa-mir-1251, hsa-mir-129-1 and hsa-mir-129-2), 6 DmRNAsDMsMethyCor database entries (MAPK8IP2, CPE, DPP6, MSI1, IL20RB and S100A2), and FMN2 gene from differential expressed mRNAs and differential single-nucleotide polymorphism (DmRNAsDSNPs) database. Prognostic index (Pl)=Xiwi xi - 0.717716. A patient was predicted as high/low risk if the PI was larger/smaller than 0.034045. Our study resulted in a comprehensive prognostic model for pancreatic cancer patients based on multi-omics analysis, which could offer better guidance for the clinical management of patients with early-stage pancreatic cancer.