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

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.

TARGET Based m 6 A Methylation-Related Genes Predict Prognosis Relapsed B-cell Acute Lymphoblastic Leukemia

“…The LASSO algorithm examines multiple independent variables at once and identi es the most impactful ones. With the LASSO algorithm, it is possible to concurrently evaluate numerous independent variables and pinpoint the variables with the greatest in uence.According to the traditional regression methods are considerably less precise.Based on LASSO Cox analysis, prognostic factors for relapsed B-ALL were identi ed, including RBM15B, YTHDC1, YTHDC2, FTO, and HNRNPC among the 20 genes analyzed [11] .The ROC curve was used to evaluate the impact of m6A methylation related genes on the prognostic outcome of relapsed B-ALL.The ndings indicated that genes associated with m6A methylation played a role in the survival of relapsed B-ALL.The risk score of m 6 A methylation-related genes (YTHDC1 and FTO) might be a powerful biomarker for relapsed B-ALL survival. High expression levels of FTO predicted a poor prognosis, whereas YTHDC1 can be regarded as protective genes.…”

Integrated analysis of single-cell RNA-seq and bulk RNA-seq reveals RNA N6-methyladenosine modification associated with prognosis and drug resistance in acute myeloid leukemia

“…Numerous investigations have firmly established the critical role of m 6 A modification in the etiology of AML and have identified singular molecular subtypes of AML based on changes in the m 6 A modifying enzymes (40,41). Despite these advances, most of these studies focused on the bulk tissues, while largely neglecting the relevance of the cell hierarchy to AML initiation and progression (42).…”

Small molecule inhibitors targeting m6A regulators

“…Numerous studies have shown a well-established approach to develop tumor prognostic models based on m 6 A modification patterns for the evaluation and guidance of immunotherapy. By conducting comprehensive analyses of a substantial cohort of AML patients with a focus on m 6 A modifications [ 187 – 189 ], researchers systematically examined the overlaps and clustering within differentially expressed genes (DEGs). Based on these findings, they developed a scoring system known as the m 6 A score.…”