Research Paper Volume 14, Issue 1 pp 330—353
m6A regulator-mediated methylation modification patterns and tumor immune microenvironment in sarcoma
- 1 Traumatology and Orthopedics, Affiliated Hospital of Chengde Medical College, Chengde, Hebei 067000, China
- 2 Department of Bone and Soft Tissue Tumor, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
- 3 National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin 300060, China
- 4 Department of Orthopedics, Affiliated Hospital of Chengde Medical College, Chengde, Hebei 067000, China
- 5 General Surgery, Affiliated Hospital of Chengde Medical College, Chengde, Hebei 067000, China
Received: August 30, 2021 Accepted: December 25, 2021 Published: January 3, 2022https://doi.org/10.18632/aging.203807
How to Cite
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.
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.
A: adenine; AUC: area under the curve; ALKBH5: ALKB homolog 5; CBLL1: Cbl-like 1; CNV: copy number variation; CTLA-4: cytotoxic T lymphocyte-associated antigen-4; DC: Dendritic cell; DEG: differentially expressed gene; ELAVL1: ELAV-like 1; HAVCR2/TIM-3: hepatitis A virus cellular receptor 2/T cell immunoglobulin domain and mucin domain-3; FFPE: formalin-fixed, paraffin-embedded; FMR1: fragile-X mental retardation 1; FPKM: Fragments Per Kilobase Million; FTO: fat mass and obesity-associated; GEO: Gene Expression Omnibus; GO: Gene Ontology; GSVA: gene set variation analysis; HNRNP: heterogeneous nuclear ribonucleoprotein; ICIs: immunological checkpoint inhibitors; IGF2: insulin-like growth factor 2; IGF2BP2: IGF2 mRNA-binding protein 2; KEGG: Kyoto Encyclopedia of Genes and Genomes; K-M: Kaplan-Meier; LAG3: Lymphocyte activation gene 3; log FC: log fold change; LRPPRC: leucine-rich pentatricopeptide repeat containing; METTL: methyltransferase-like; MSigDB: Molecular Signatures Database; NMF: Nonnegative matrix factorization; m6A: N6-methyladenosine; PC: principal component; PCA: principal component analysis; PD-1: programmed cell death protein-1; PD-L1: programmed cell death protein-ligand 1; PI3K/AKT/mTOR: phosphoinositide 3-kinase/serine-threonine kinase/mammalian target of rapamycin; RBM15B: RNA-binding motif protein 15B; ROC: receiver operating characteristic curve; ssGSEA: single sample gene set enrichment analysis; STS: soft tissue sarcomas; TARGET: Therapeutically Applicable Research To Generate Effective Treatments; TCGA: The Cancer Genome Atlas; TCGA-SCAR: The Cancer Genome Atlas-Sarcoma; TIGIT: T cell immunoreceptor with immunoglobulin and ITIM domain; TIME: tumor immune microenvironment; TIMER: Tumor Immune Estimation Resource; TPM: transcripts per kilobase million; UCSC: University of California Santa Cruz; VIRMA: Vir-Like m6A methyltransferase associated; WTAP: Wilms tumor 1 (WT1)-associated protein; WES: whole-exome sequencing; YTHDC: YTH domain containing; YTHDF: YTH m6A RNA-binding protein; ZC3H13: zinc finger CCCH-type containing 13.