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. 2022 Mar 3;22(1):93.
doi: 10.1186/s12876-022-02160-w.

m1A methylation modification patterns and metabolic characteristics in hepatocellular carcinoma

Chengcheng Tong #  1 , Wei Wang #  2 , Chiyi He  3
Affiliations

m1A methylation modification patterns and metabolic characteristics in hepatocellular carcinoma

Chengcheng Tong et al. BMC Gastroenterol. .

Abstract

Background: The dysregulation of RNA methylation has been demonstrated to contribute to tumorigenicity and progression in recent years. However, the alteration of N1-methyladenosine (m1A) methylation and its role in hepatocellular carcinoma (HCC) remain unclear.

Methods: We systematically investigated the modification patterns of 10 m1A regulators in HCC samples and evaluated the metabolic characteristics of each pattern. A scoring system named the m1Ascore was developed using principal component analysis. The clinical value of the m1Ascore in risk stratification and drug screening was further explored.

Results: Three m1A modification patterns with distinct metabolic characteristics were identified, corresponding to the metabolism-high, metabolism-intermediate and metabolism-excluded phenotypes. Patients were divided into high- or low-m1Ascore groups, and a significant survival difference was observed. External validation confirmed the prognostic value of the m1Ascore. A nomogram incorporating the m1Ascore and other clinicopathological factors was constructed and had good performance for predicting survival. Two agents, mitoxantrone and doxorubicin, were determined to be potential therapeutic drugs for the high-risk group.

Conclusion: This study provided novel insights into m1A modification and metabolic heterogeneity in cancer, promoted risk stratification in the clinic from the perspective of m1A modification, and further guided individual treatment strategies.

Keywords: Hepatocellular carcinoma; Metabolism; Prognosis; m1A.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Fig. 1
Fig. 1
Genetic and expression landscapes of m1A regulators in HCC. a CNV frequency of m1A regulators in TCGA cohort. b The location of CNV of m1A regulators on chromosomes. c The mutation frequency of m1A regulators in TCGA cohort. Each column represents a patient. The upper barplot indicated the tumor mutation burden. The right barplot indicated the proportion of each mutation type. The lower barplot indicated fraction of conversions in each patient. d Expression levels of m1A regulators in HCC and normal tissues
Fig. 2
Fig. 2
The m1A methylation modification patterns in HCC. a Network plot visualizing the interaction between m1A regulators in TCGA cohort. Each circle represents a m1A regulator gene and the color of circle represents the functional category. The circle size represents the impact of corresponding m1A regulator in survival and the lines between pairs circles indicate that there is an expression correlation between two regulators. Red line shows a positive correlation and thickness indicate the correlation strength. b Expression levels of m1A regulators in subtype 1–3. c Enrichment scores of mRNA methylation in subtype 1–3. d Overall survival of subtype 1–3 and the survival difference was evaluated by log-rank test
Fig. 3
Fig. 3
Biological characteristics involved in three m1A modification patterns. a, b Heatmap visualizing the activation states of biological pathways in subtype 1–3. The pathways achieved from the HALLMARK gene sets. c-f Gene set variation analysis enrichment scores of metabolic-related pathways in subtype 1–3. c Amino acid metabolism. d Carbohydrate metabolism. e Fatty acid metabolism. f Others metabolism. g The correlation plot
Fig. 4
Fig. 4
The m1A gene clusters in HCC. a Expression levels of m1A regulators in gene cluster 1–3. b Enrichment scores of mRNA methylation in gene cluster 1–3. c Overall survival of gene cluster 1–3 and the survival difference was evaluated by log-rank test. d Heatmap visualizing the gene set variation analysis enrichment scores of metabolic-related pathways in three gene clusters
Fig. 5
Fig. 5
Correlation between the known signatures and m1Ascore. a Alluvial diagram visualizing the connection between m1Ascore, subtype1-3, and gene cluster 1–3. b Distribution of m1Ascore in subtype 1–3. c Distribution of m1Ascore in gene cluster 1–3
Fig. 6
Fig. 6
Characteristics of m1Ascore and drug screening. a Overall survival of high and low m1Ascore groups and the survival difference was evaluated by log-rank test. b Clinicopathological characteristics in high and low m1Ascore groups. c Heatmap visualizing the activation states of biological pathways in high and low m1Ascore groups. The pathways achieved from the HALLMARK gene sets. d Somatic mutation landscapes in high and low m1Ascore groups. e–f Drug response analysis of the potential compounds e derived from PRISM and f CTRP
Fig. 7
Fig. 7
Construction of nomogram for predicting survival of HCC in TCGA cohort. a Forest plot showing the results of univariate and multivariate Cox analyses. b Nomogram. cCalibration plot. d Time-dependent receiver operating characteristic analysis and the areas under the curve at 1, 2, and 3-years. e, f Decision curve analyses. e Net benefit analyses. f Net reduction analyses
Fig. 8
Fig. 8
Pan-cancer analyses. a Heatmap showing the expression levels of m1A regulators in tumor tissues compared with normal tissues in 18 cancer types. b Expression correlation of m1A regulators in 33 cancer types. c Forest plots showing the results of univariate Cox analysis in 33 cancer types
Fig. 9
Fig. 9
A schematic of m1A regulators in liver cancer cells. A methyl residue is added to mRNA by methyltransferases, removed by demethylases, and recognized by binding proteins. The modification of m1A methylation finally results in metabolic reprogramming in hepatocellular carcinoma. Images used in the current schematic are freely taken from Servier Medical Art (https://smart.servier.com/)
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