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The m6A RNA methylation regulators related transcriptome for identification of pancreatic cancer subtypes and prognostic markers

Published: 25 February 2022 Publication History
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  • Abstract

    N6-methyladenosine (m6A) methylation is a major epigenetic modification of RNA that affects processes such as translation of related mRNAs and non-coding RNAs. A large number of recent studies have shown that m6A modifications play a crucial role in cancer development, however, the prognostic value of the m6A associated transcriptome in pancreatic cancer has rarely been investigated. The purpose of this study is to investigate the prognostic markers and prognostic subtypes of m6A RNA methylation regulators related transcriptome in pancreatic ductal adenocarcinoma (PDAC). First, we identified the m6A RNA methylation regulators related prognostic transcriptome by Pearson correlation analysis and univariate cox regression. Subsequently, to explore key prognostic markers from the prognostic transcriptome, we proposed a G-P model based on greedy algorithms and pruning algorithms to obtain a set of key genes CASC11, KRT14, PDZD4, and identified two high/low-risk subtypes of PDAC with significant prognostic differences based on key genes. The clustering Silhouette coefficients was 0.99 for the key genes. In addition, CASC11 and KRT14 were strongly upregulated in the high-risk subtype and PDZD4 was upregulated in the low-risk subtype, and their differential expression was significantly associated with survival. In conclusion, we revealed the typing role and prognostic value of the m6A RNA methylation regulators-associated transcriptome in PDAC and provided new insights for identifying predictive biomarkers and therapeutic targets for PDAC.

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    cover image ACM Other conferences
    ACAI '21: Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence
    December 2021
    699 pages
    ISBN:9781450385053
    DOI:10.1145/3508546
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 25 February 2022

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    Author Tags

    1. Biomarker
    2. Greedy algorithm
    3. Pruning algorithm
    4. clustering
    5. m6A

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