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Research Paper|Volume 14, Issue 1|pp 330—353

m6A regulator-mediated methylation modification patterns and tumor immune microenvironment in sarcoma

Zhehong Li1, Junqiang Wei2,3,4, Honghong Zheng5, Xintian Gan1, Mingze Song1, Yafang Zhang1, Lingwei Kong1, Chao Zhang2,3, Jilong Yang2,3, Yu Jin1
  • 1Traumatology and Orthopedics, Affiliated Hospital of Chengde Medical College, Chengde, Hebei 067000, China
  • 2Department of Bone and Soft Tissue Tumor, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
  • 3National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin 300060, China
  • 4Department of Orthopedics, Affiliated Hospital of Chengde Medical College, Chengde, Hebei 067000, China
  • 5General Surgery, Affiliated Hospital of Chengde Medical College, Chengde, Hebei 067000, China
* Equal contribution and share first authorship
Received: August 30, 2021Accepted: December 25, 2021Published: January 3, 2022
https://doi.org/10.18632/aging.203807

Copyright: © 2021 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Background: Studies have shown that the RNA N6-methyladenosine (m6A) modification patterns are extensively involved in the development of multiple tumors. However, the association between the m6A regulator expression patterns and the sarcoma tumor immune microenvironment (TIME) remains unclear.

Methods: We systematically evaluated the m6A regulator expression patterns in patients with sarcoma based on known 23 m6A regulators. Different m6A regulator expression patterns were analyzed using gene set variation analysis and a single-sample gene set enrichment analysis algorithm. According to the results of consensus clustering, we classified the patients into four different clusters. Next, we subjected the four clusters to differential genetic analysis and established m6A-related differentially expressed genes (DEGs). We then calculated the m6A-related DEGs score and constructed the m6A-related gene signature, named m6A score. Finally, the 259 sarcoma samples were divided into high- and low-m6A score groups. We further evaluated the TIME landscape between the high- and low-m6A score groups.

Results: We identified four different m6A modification clusters and found that each cluster had unique metabolic and immunological characteristics. Based on the 19 prognosis-related DEGs, we calculated the principal component analysis scores for each patient with sarcoma and classified them into high- and low-m6A score groups.

Conclusions: The m6A regulator expression patterns and complexity of the sarcoma TIME landscape are closely related to each other. Systematic evaluation of m6A regulator expression patterns and m6A scores in patients with sarcoma will enhance our understanding of TIME characteristics.