Analysis of m6A-related signatures associated with the tumor immune microenvironment and predict survival in acute myeloid leukemia

Shushu Yuan; Zhirong Cong; Jiali Ji; Li Zhu; Qi Jiang; Ying Zhou; Qian Shen; Daniela Damiani; Xiaohong Xu; Bingzong Li

doi:10.21037/atm-22-3858

Analysis of m6A-related signatures associated with the tumor immune microenvironment and predict survival in acute myeloid leukemia

Ann Transl Med. 2022 Aug;10(16):902. doi: 10.21037/atm-22-3858.

Authors

Shushu Yuan^#^{1

2}, Zhirong Cong^#², Jiali Ji^#², Li Zhu², Qi Jiang², Ying Zhou², Qian Shen², Daniela Damiani³, Xiaohong Xu², Bingzong Li¹

Affiliations

¹ Department of Hematology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
² Department of Oncology, Affiliated Tumor Hospital of Nantong University, Nantong, China.
³ Division of Hematology and Stem Cell Transplantation, Azienda Sanitaria Universitaria Friuli Centrale-Ospedale S. M. Misericordia, Udine, Italy.

^# Contributed equally.

Abstract

Background: Most previous studies have focused on the intrinsic carcinogenic pathways of tumors; however, little is known about the potential role of N6-methyladenosine (m6A) methylation in the tumor immune microenvironment (TIME). To better diagnose and treat acute myeloid leukemia (AML), we sought to examine the correlation between m6A regulatory factors and immune infiltration in cases of AML. At the same time, a prognostic model was constructed to predict the survival of AML.

Methods: We extracted data from The Cancer Genome Atlas (TCGA) database, including ribonucleic acid sequencing (RNA-seq) transcriptome data and data on the corresponding clinical characteristics of AML patients. We identified two m6A modification patterns with distinct clinical outcomes and found a significant relationship between them. Simultaneous discovery of distinct m6A clusters associated with the tumor immune microenvironment [immune cell types and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm] are closely related. Next, we implemented Lasso (Least Absolute Shrinkage and Selection Operator) Cox regression to build a predictive model in the 2-m6A regulator TCGA dataset to further explore m6A prognostic features in AML, and perform correlation validation.

Results: We identified 2 molecular subtypes (Clusters 1 and 2) by the consistent clustering of significant m6A regulators in AML. Cluster 2 was associated with a higher immune score and obvious immune cell infiltration, and thus patients in Cluster 2 had a poorer prognosis than those in Cluster 1 (P<0.05). Additionally, the 2 m6A-related signatures representing the independent prognostic factors in AML were screened to construct a prognostic risk-score model. We found that patients with low-risk scores had higher immune scores than those with high-risk scores (P<0.05).

Conclusions: Our research confirmed that m6A methylation plays an important role in AML. Further provide new directions for the prognosis and treatment of AML.

Keywords: Tumor immune microenvironment (TIME); acute myeloid leukemia (AML); m6A; survival.