Issue 2, 2024

Systematic characterization of m6A proteomics across 12 cancer types: a multi-omics integration study

Abstract

The modification patterns of N6-methyladenosine (m6A) regulators and interacting genes are deeply involved in tumors. However, the effect of m6A modification patterns on human proteomics remains largely unknown. We evaluated the molecular characteristics and clinical relevance of m6A modification proteomics patterns among 1013 pan-cancer samples from the Clinical Proteomic Tumor Analysis Consortium (CPTAC). More than half of the m6A proteins were expressed at higher levels in tumor tissues and presented oncogenic characteristics. Furthermore, we performed multi-omics analyses integrating with transcriptomics data of m6A regulators and interactive coding and non-coding RNAs and developed a m6A multi-omics signature to identify potential m6A modification target proteins across global proteomics. It was significantly associated with overall survival in nine cancer types, tumor mutation burden (P = 0.01), and immune checkpoints including PD-L1 (P = 4.9 × 10−8) and PD-1 (P < 0.01). We identified 51 novel proteins associated with the multi-omics signature (PFDR < 0.05). These proteins were functional through pathway enrichment analyses. The protein with the highest hit frequency was CHORDC1, which was significantly up-regulated in tumor tissues in nine cancer types. Its higher abundance was significantly associated with a poorer prognosis in seven cancer types. The identified m6A target proteins might provide infomation for the study of molecular mechanism of cancer.

Graphical abstract: Systematic characterization of m6A proteomics across 12 cancer types: a multi-omics integration study

Supplementary files

Article information

Article type
Research Article
Submitted
31 Aug 2023
Accepted
26 Oct 2023
First published
09 Nov 2023

Mol. Omics, 2024,20, 103-114

Systematic characterization of m6A proteomics across 12 cancer types: a multi-omics integration study

H. Li, Y. Jiang, J. Chen, Z. Li, R. Zhang, Y. Wei, Y. Zhao, S. Shen and F. Chen, Mol. Omics, 2024, 20, 103 DOI: 10.1039/D3MO00171G

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