Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive, fatal tumor. N6-methylandenosine (m6A) methylation is the major epigenetic modification of RNA including lncRNAs. The roles of m6A-related lncRNAs in PDAC have not been fully clarified. This study aims to assess gene signatures and prognostic value of m6A-related lncRNAs in PDAC. The Cancer Genome Atlas (TCGA) dataset and the International Cancer Genome Consortium (ICGC) dataset were explored to identify m6A-related lncRNAs. Univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression were performed to construct the m6A-related lncRNAs prognostic riskscore (m6A-LPR) model to predict the overall survival (OS) in the TCGA training cohort. Kaplan–Meier curve with log-rank test and receiver operating characteristic (ROC) curve were used to evaluate the prognostic value of the m6A-LPR. Furthermore, the robustness of the m6A-LPR was further validated in the ICGC cohort. Tumor immunity was evaluated using ESTIMATE and CIBERSORT algorithms. A total of 262 m6A-related lncRNAs were identified in two datasets. In the TCGA training cohort, 28 prognostic m6A-related lncRNAs were identified and the m6A-LPR including four m6A-related lncRNAs was constructed. The m6A-LPR was able to identify high-risk patients with significantly poorer OS and accurately predict OS in both the TCGA training cohort and the ICGC validation cohort. Analysis of tumor immunity revealed that high-risk groups had remarkably lower stromal, immune, and ESTIMATE scores. Moreover, high-risk groups were associated with significantly higher levels of plasma B cells and resting NK cells infiltration, and lower levels of infiltrating resting memory CD4 T cells, monocytes, and resting mast cells. Our study proposed a robust m6A-related prognostic signature of lncRNAs for predicting OS in PDAC, which provides some clues for further studies focusing on the mechanism process underlying m6A modification of lncRNAs.
Highlights
Abbreviations PDAC Pancreatic ductal adenocarcinoma m6A N6-methylandenosine TCGA The Cancer Genome Atlas ICGC International Cancer Genome Consortium least absolute shrinkage and selection operator (LASSO) Least absolute shrinkage and selection operator m6A-related lncRNAs prognostic riskscore (m6A-LPR) M6A-related lncRNAs prognostic riskscore overall survival (OS) Overall survival receiver operating characteristic (ROC) Receiver operating characteristic messenger RNAs (mRNAs) Messenger RNAs lncRNAs Long non-coding RNAs FPKM Fragments Per Kilobase of transcript per Million differentially expressed genes (DEGs) Differentially expressed genes FDR False Discovery Rate
N6-methylandenosine (m6A) RNA methylation is the main epigenetic modification of messenger RNAs and non-coding RNAs4, it has been confirmed to play critical regulatory roles in the modification of tumor R NAs5. m6A modifications are invertible and dynamical processes that are regulated by three kinds of m6A regulator, including methyltransferases (“writers”), signal transducers (“readers”) and demethylases (“erasers”)[6], for example, METTL14, an m6A methyltransferase, catalyzes m6A RNA methylation with METTL3, and these two proteins form a stable METTL3–METTL14 complex that functions in cellular m6A deposition on mammalian nuclear R NAs7
585 shared correlations (e.g., YTHDF1/ZFAS1/positive) were obtained in both two cohorts, because there were some duplicate lncRNAs in the 585 shared correlations, 262 unique m6A-related lncRNAs were extracted from the shared correlations after removing repetition
Summary
Abbreviations PDAC Pancreatic ductal adenocarcinoma m6A N6-methylandenosine TCGA The Cancer Genome Atlas ICGC International Cancer Genome Consortium LASSO Least absolute shrinkage and selection operator m6A-LPR M6A-related lncRNAs prognostic riskscore OS Overall survival ROC Receiver operating characteristic mRNAs Messenger RNAs lncRNAs Long non-coding RNAs FPKM Fragments Per Kilobase of transcript per Million DEGs Differentially expressed genes FDR False Discovery Rate. Understanding how m6A modifications of lncRNAs contribute to PDAC progression can help to identify novel biomarkers as potential therapeutic targets. The Cancer Genome Atlas (TCGA) dataset (n = 140) and the International Cancer Genome Consortium (ICGC) dataset (n = 63) were explored to identify 262 m6A-related lncRNAs in patients with PDAC. Our results would be helpful to assess the prognosis of patients with PDAC and might offer the promise of individualized therapeutic interventions
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