Dynamic landscape of m6A modifications and related post-transcriptional events in muscle-invasive bladder cancer

Lili Zhang1, Ziwei Chen1,2, Gaoyuan Sun1

  • 1Clinical Biobank, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.

PubMed
Abstract

Insights

This study reveals how N6-Methyladenosine (m6A) modifications impact RNA processing in muscle-invasive bladder carcinoma (MIBC). Findings highlight novel m6A sites and their roles in gene expression, offering potential therapeutic targets for MIBC.

Area of Science:

  • Oncology
  • Molecular Biology
  • Genomics

Background:

  • Muscle-invasive bladder carcinoma (MIBC) is an advanced stage of bladder cancer.
  • N6-Methyladenosine (m6A) is a crucial RNA modification affecting RNA stability and splicing.
  • Dysregulation of m6A is implicated in MIBC, but its specific roles in post-transcriptional regulation remain understudied.

Purpose of the Study:

  • To characterize the m6A RNA methylation landscape in MIBC.
  • To investigate the impact of m6A alterations on post-transcriptional events like differential transcript usage and polyadenylation.
  • To identify potential therapeutic targets for MIBC based on m6A modifications.

Main Methods:

  • Utilized Nanopore direct-RNA sequencing to profile m6A modifications in MIBC tissues.
  • Employed machine learning to construct a prognostic m6A-risk model for patient stratification.
  • Analyzed differential transcript usage, polyadenylation signal sites, and poly(A) tail length variations.

Main Results:

  • Developed a machine learning-based m6A-risk model for precise stratification of high and low-risk MIBC patients.
  • Identified a novel m6A site in the IGLL5 gene 3'UTR associated with altered transcript usage and poly(A) tail length.
  • Discovered specific transcripts (VWA1-203, CEBPB-201) with distinct m6A modification patterns and associated RNA processing changes.

Conclusions:

  • Nanopore direct-RNA sequencing and machine learning effectively identified m6A modifications in MIBC.
  • The study provides insights into m6A-driven post-transcriptional regulatory mechanisms in MIBC development and progression.
  • Findings suggest m6A modifications are potential targets for clinical intervention in MIBC.

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