Clinical and Prognostic Pan-Cancer Analysis of N6-Methyladenosine Regulators in Two Types of Hematological Malignancies: A Retrospective Study Based on TCGA and GTEx Databases

Abstract

N6-methyladenosine (m6A) is one of the most active modification factors of mRNA, which is closely related to cell proliferation, differentiation, and tumor development. Here, we explored the relationship between the pathogenesis of hematological malignancies and the clinicopathologic parameters. The datasets of hematological malignancies and controls were obtained from the TCGA [AML (n = 200), DLBCL (n = 48)] and GTEx [whole blood (n = 337), blood vascular artery (n = 606)]. We analyzed the m6A factor expression differences in normal tissue and tumor tissue and their correlations, clustered the express obvious clinical tumor subtypes, determined the tumor risk score, established Cox regression model, performed univariate and multivariate analysis on all datasets. We found that the AML patients with high expression of IGF2BP3, ALKBH5, and IGF2BP2 had poor survival, while the DLBCL patients with high expression of METTL14 had poor survival. In addition, "Total" datasets analysis revealed that IGF2BP1, ALKBH5, IGF2BP2, RBM15, METTL3, and ZNF217 were potential oncogenes for hematologic system tumors. Collectively, the expressions of some m6A regulators are closely related to the occurrence and development of hematologic system tumors, and the intervention of specific regulatory factors may lead to a breakthrough in the treatment in the future.

Keywords: hematological malignancies; m6A methylation regulators; pan-cancer analysis; prognosis; risk scores.

Figure 1

Clinical data and research process of the analysis of pan-carcinoma of m6A RNA methylation regulators in hematological malignancies.

Figure 2

Distribution of m6a RNA methylation regulators in hematological malignancies. (A) The “heatmaps” shows the expression levels of 23 m6A RNA methylation regulators in hematological malignancies. The higher expression, the darker the color (red for up-regulated, green for down-regulated), and the tree diagram above represents the clustering results of different samples from different experimental groups, while the tree on the left shows the clustering analysis results of different regulators from different samples; The “vioplot” visualized the differential m6A regulators (assume blue is normal tissue and red is tumor tissue). (B) The “corrplot” shows the correlation analysis of the expression of 23 m6A regulators in hematological malignancies.

Figure 3

Consistent clustering by m6a RNA methylated modulators in hematological malignancies. (A) Identification of consistent clustering by m6a RNA methylated modulators in AML datasets. Is the consistency clustering matrix of k = 2, and the cumulative distribution function of consistency clustering (CDF) when k = 2–9, and the relative change of the area under the CDF curve when k = 2–9 and the 3D principal component analysis (3D PCA) of total TCGA RNA expression profile datasets and the “cluster 1”subtype is marked in red and the “cluster 2”subtype is marked in blue. (B) Identification of consistent clustering by m6a RNA methylated modulators in DLBCL datasets. (C) Identification of consistent clustering by m6a RNA methylated modulators in “Total” datasets.

Figure 4

Differences in clinicopathologic features and overall survival of hematological malignancies. (A) The “Kaplan–Meier” overall survival (OS) curve of hematological malignancies in two clusters (cluster 1/2) defined by consistent expression of m6a RNA methylation regulators in hematological malignancies. (B) The “heatmap” and clinicopathologic features of two clusters defined by consistent expression of the m6A regulatory genes (clusters1/2).

Figure 5

Risk signatures with m6A RNA methylation regulators in hematological malignancies. (A) The process of building the signature containing 23 m6A RNA methylation regulators and used “glmnet” to filter out meaningful m6A methylation regulatory factor in AML, DLBCL and “Total” datasets; (B) The coefficients calculated by multivariate Cox regression using LASSO are shown and “Kaplan–Meier” overall survival (OS) curves for patients in the TCGA datasets assigned to high and low-risk groups according to the risk score.

Figure 6

Relationship between the risk score, clinicopathological features, and clusters subgroups in hematological malignancies. (A) Relationship between the risk score, clinicopathological features, and clusters subgroups in AML datasets. The “heatmap” shows the expression levels of the m6A RNA methylation regulators in low-risk and high-risk. The distribution of clinicopathological features was compared between the low- and high-risk groups; The ROC curve indicates the predictive efficiency of risk signatures. The Univariate Cox regression analyses of the correlation between clinicopathological factors (including the risk score) and overall survival of patients in the TCGA datasets, and the multivariate Cox regression analyses of the relationship between clinicopathological factors (including the risk score) and overall survival of patients in the TCGA datasets. (B) Relationship between the risk score, clinicopathological features, and clusters subgroups in DLBCL datasets. (C) Relationship between the risk score, clinicopathological features, and clusters subgroups in “Total” datasets.