Abstract

BackgroundCompelling lines of evidence indicate that DNA methylation of non-coding RNAs (ncRNAs) plays critical roles in various tumour progression. In addition, the differential methylation of ncRNAs can predict prognosis of patients. However, little is known about the clear relationship between DNA methylation profile of ncRNAs and the prognosis of pancreatic adenocarcinoma (PAC) patients.MethodsThe data of DNA methylation, RNA-seq, miRNA-seq and clinical features of PAC patients were collected from TCGA database. The DNA methylation profile was obtained using the Infinium HumanMethylation450 BeadChip array. LASSO regression was performed to construct two methylation-based classifiers. The risk score of methylation-based classifiers was calculated for each patient, and the accuracy of the classifiers in predicting overall survival (OS) was examined by ROC curve analysis. In addition, Cox regression models were utilized to assess whether clinical variables and the classifiers were independent prognostic factors for OS. The targets of miRNA and the genes co-expressed with lncRNA were identified with DIANA microT-CDS and the Multi-Experiment Matrix (MEM), respectively. Moreover, DAVID Bioinformatics Resources were applied to analyse the functional enrichment of these targets and co-expressed genes.ResultsA total of 4004 CpG sites of miRNA and 11,259 CpG sites of lncRNA were screened. Among these CpG sites, 8 CpG sites of miRNA and 7 CpG sites of lncRNA were found with regression coefficients. By multiplying the sum of methylation degrees of the selected CpGs with these coefficients, two methylation-based classifiers were constructed. The classifiers have shown good performance in predicting the survival rate of PAC patients at varying follow-up times. Interestingly, both of these two classifiers were predominant and independent factors for OS. Furthermore, functional enrichment analysis demonstrated that aberrantly methylated miRNAs and lncRNAs are related to calcium ion transmembrane transport and MAPK, Ras and calcium signalling pathways.ConclusionIn the present study, we identified two methylation-based classifiers of ncRNA associated with OS in PAC patients through a comprehensive analysis of miRNA and lncRNA profiles. We are the first group to demonstrate a relationship between the aberrant DNA methylation of ncRNAs and the prognosis of PAC, and this relationship would contribute to individualized PAC therapy.

Highlights

  • Compelling lines of evidence indicate that DNA methylation of non-coding RNAs plays critical roles in various tumour progression

  • Differential methylation of CpG sites and methylation‐based classifier A total of 4004 CpG sites of miRNA and 11,259 CpG sites of long non-coding RNAs (lncRNAs) located within 2 kb upstream of the miRNA or lncRNA transcriptional start site (TSS) were screened according to the HumanMethylation450 BeadChip annotations in the The Cancer Genome Atlas (TCGA)

  • 511 CpG sites of miRNA and 1441 CpG sites of lncRNA with different methylation patterns between pancreatic adenocarcinoma (PAC) and normal adjacent tissues were screened and analysed using the “minfi” package in R software; 273 CpG sites of miRNA and 809 CpG sites of lncRNA had betavalue differences greater than 0.1. Among these CpG sites, the methylation levels of 86 CpG sites of miRNA and 348 CpG sites of lncRNA were negatively related to the expression levels of miRNA or lncRNA

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Summary

Introduction

Compelling lines of evidence indicate that DNA methylation of non-coding RNAs (ncRNAs) plays critical roles in various tumour progression. Little is known about the clear relationship between DNA methylation profile of ncRNAs and the prognosis of pancreatic adenocarcinoma (PAC) patients. Pancreatic adenocarcinoma (PAC) is one of the most prevalent type of highly lethal malignancy in the digestive system. It is the fourth most dominant cancer-associated death, with a 5-year survival rate less than 5% [1]. Ttreatment of PAC, including surgical resection, radiotherapy and chemotherapy, has been improved in the recent years [2]. Even with these disease management options, the overall survival (OS) rates of PAC patients are still far from satisfactory. To reduce mortality and improve the management of PAC patients, identification of prognosis biomarkers during the early stage of PAC is extremely important

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