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. 2024 Apr 22;24(1):506.
doi: 10.1186/s12885-024-12235-4.

m1A regulator-mediated methylation modification patterns correlated with autophagy to predict the prognosis of hepatocellular carcinoma

Yingmin Wu #  1   2   3   4 , Lian Li #  5   6   7 , Long Wang #  5   6   7 , Shenjie Zhang  8 , Zhirui Zeng  5   8   6   7 , Jieyu Lu  5   6   7 , Zhi Wang  8 , Yewei Zhang  8 , Shilong Zhang  8 , Haiyang Li  9   10 , Tengxiang Chen  11   12   13   14
Affiliations

m1A regulator-mediated methylation modification patterns correlated with autophagy to predict the prognosis of hepatocellular carcinoma

Yingmin Wu et al. BMC Cancer. .

Abstract

Background: N1-methyladenosine (m1A), among the most common internal modifications on RNAs, has a crucial role to play in cancer development. The purpose of this study were systematically investigate the modification characteristics of m1A in hepatocellular carcinoma (HCC) to unveil its potential as an anticancer target and to develop a model related to m1A modification characteristics with biological functions. This model could predict the prognosis for patients with HCC.

Methods: An integrated analysis of the TCGA-LIHC database was performed to explore the gene signatures and clinical relevance of 10 m1A regulators. Furthermore, the biological pathways regulated by m1A modification patterns were investigated. The risk model was established using the genes that showed differential expression (DEGs) between various m1A modification patterns and autophagy clusters. These in vitro experiments were subsequently designed to validate the role of m1A in HCC cell growth and autophagy. Immunohistochemistry was employed to assess m1A levels and the expression of DEGs from the risk model in HCC tissues and paracancer tissues using tissue microarray.

Results: The risk model, constructed from five DEGs (CDK5R2, TRIM36, DCAF8L, CYP26B, and PAGE1), exhibited significant prognostic value in predicting survival rates among individuals with HCC. Moreover, HCC tissues showed decreased levels of m1A compared to paracancer tissues. Furthermore, the low m1A level group indicated a poorer clinical outcome for patients with HCC. Additionally, m1A modification may positively influence autophagy regulation, thereby inhibiting HCC cells proliferation under nutrient deficiency conditions.

Conclusions: The risk model, comprising m1A regulators correlated with autophagy and constructed from five DEGs, could be instrumental in predicting HCC prognosis. The reduced level of m1A may represent a potential target for anti-HCC strategies.

Keywords: Autophagy; Hepatocellular carcinoma (HCC); N1-methyladenosine (m1A); Prognosis; m1A regulators.

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Conflict of interest statement

The authors declare no potential conflict of interest.

Figures

Fig. 1
Fig. 1
Signatures of genetic variation of m1A regulators in hepatocellular carcinoma (HCC) cells. (A) Comparative analysis of m1A-related genes expressed in tumor and normal liver tissues from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC); (B) Frequency of CNV variation in TCGA-LIHC regulators. Column height represents CNV alteration frequency. Deletion frequency is indicated by green dots, and the amplification frequency is indicated by red dots; (C) Location of alterations in copy number variation (CNV) of m1A regulators on 23 chromosomes using the TCGA-LIHC cohort; (D) Analysis of 10 m1A regulator expression profiles in the TCGA-LIHC cohort by principal component analysis (PCA). On the basis of m1A regulator expression profiles, we identified two subgroups without intersection, indicating excellent differentiation between tumor samples and normal samples. Tumors are labeled in blue and normal samples in yellow; (E–N) Overall survival of subgroups of low m1A regulator expression and high m1A regulator expression, including TRMT6 (E), TRMT61A (F), TRMT61B (G), TRMT10C (H), ALKBH1 (I), ALKBH3 (J), YTHDF1 (K), YTHDF2 (L), YTHDF3 (M), and YTHDC1 (N) *p < 0.05, **p < 0.01, ***p < 0.001 compared with the control group; ns, not significant
Fig. 2
Fig. 2
A modification pattern for m1A is constructed based on the expression of ten regulators. (A) Cumulative distribution function (CDF) curve in The Cancer Genome Atlas–Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort; (B) Delta area curve of CDF in TCGA-LIHC cohort; (C) Clustering heatmap when consensus k = 2; (D) In the TCGA-LIHC cohort, Kaplan-Meier curves show a prognostic relationship between two different m1A patterns; (E–K) Survival status (E), grade (F), stage (G), T stage (H), age (I), M stage (J), and N stage (K) of the two different m1A patterns of cluster 1 and cluster 2 *p < 0.05, **p < 0.01, ***p < 0.001 compared with the control group; ns, not significant
Fig. 3
Fig. 3
High m1A modification inhibits HCC cell proliferation and migration. (A) ALKBH3 expression detected by western blotting (up) and quantitatively analyzed (down); (B) Levels of mRNA m1A modification in sh-ALKBH3 HepG2, sh-ALKBH3 QGY, and their corresponding control cells assessed through dot blot analysis; (C) Relative cell viability of sh-ALKBH3 HepG2, sh-ALKBH3 QGY, and their corresponding control cells; (D and E) Results of colony formation (D) and migration (E) assays to detect the effects of m1A modification on HepG2 and QGY cells; (F and G) Levels of mRNA m1A modification (F) and relative cell viability (G) of HepG2 cells transfected with vector control (PPB), ALKBH3, ALKBH3-R122S, or ALKBH3-L177A constructs for 24 h (H) Results of colony formation of HepG2 cells transfected with vector control (PPB), ALKBH3, ALKBH3-R122S, or ALKBH3-L177A. Data are presented as mean ± standard deviation from three independent experiments. *p < 0.05, **p < 0.01, ***p < 0.001 compared with the control group
Fig. 4
Fig. 4
Identification of autophagy clusters related to m1A modification patterns. (A) Gene set variation enrichment analysis under different m1A clusters; (B) The delta area curve for the cumulative distribution function (CDF) in the Liver Hepatocellular Carcinoma Cohort of The Cancer Genome Atlas (TCGA-LIHC); (C) Curves for the CDF in the TCGA-LIHC cohort; (D) Heatmap of clustering when consensus k = 2; (E) Based on the TCGA-LIHC cohort, the Kaplan-Meier curve shows a prognostic relationship between cluster A and cluster B; (F) Single-sample gene set enrichment analysis of autophagy under different autophagy clusters; (G and H) Kaplan–Meier curve of the prognostic relationship between B1 and B2 (G) or A1 and A2 (H). *p < 0.05, **p < 0.01, ***p < 0.001 compared with the control group; p > 0.05, ns, not significant
Fig. 5
Fig. 5
Exploration of mRNA m1A modification was associated with autophagy in hepatocellular carcinoma (HCC) cells. (A) Expression of LC3I, LC3II, Beclin 1, and p62 in Earle’s balanced salt solution (EBSS)–treated HepG2 or QGY cells for 6, 12 h and their corresponding control cells assessed using western blot (left) and quantitatively analyzed (right); (B) Cell proliferation assays to detect the effects of EBSS on HCC cells; (C) Levels of mRNA m1A modification in EBSS-treated HepG2 cells for 0, 6, 12, 18, 24, and 30 h assessed through dot blot assay. Expression of LC3I and LC3II in the above groups assessed through western blotting and quantitatively analyzed (right); (D) Expression of LC3I, LC3II, Beclin 1, and p62 in sh-ALKBH3 HepG2 cells and their corresponding control cells assessed through western blot (up) and quantitatively analyzed (down); (E) shNC and shALKBH3 HepG2 cells were transfected with vector control, ALKBH3, ALKBH3-R122S, or ALKBH3-L177A constructs for 48 h, the expression of LC3I, LC3II, Beclin 1, and p62 were checked (up) and quantitatively analyzed (down); (F and G) Relative fold of the proliferation (F) and colonization (G) of HepG2 cells treated with EBSS and the ALKBH3 overexpression group and the control group. Data are presented as mean ± standard deviation from three independent experiments. *p < 0.05, **p < 0.01, ***p < 0.001 compared with the control group
Fig. 6
Fig. 6
Construction of a prognostic model. (A) Common DEGs between various m1A modification patterns and autophagy clusters; (B) Trajectory change for each independent variable; (C) Confidence interval under each lambda; (D) Hazard ratio and p value of genes (including CDK5R2, TRIM36, DCAF8L1, CYP26B1, and PAGE1) under multivariate Cox regression analysis
Fig. 7
Fig. 7
Prognostic risk model showing a high prognostic value in The Cancer Genome Atlas (TCGA) training cohort. (A) TCGA training cohort samples of hepatocellular carcinoma (HCC) divided into low- and high-risk groups; (B) The overall survival rate of low-risk and high-risk patients with HCC in the TCGA training cohort was analyzed using Kaplan-Meier plots; (C–E) An indication of the diagnostic value of the risk model for 1-year (C), 3-year (D), and 5-year (E) survival rates in the TCGA training cohort for patients with HCC; (F) Survival time and survival status of each patient with HCC in the TCGA training cohort; (G) In the TCGA training cohort, the expression of TRIM36, CYP26B1, PAGE1, CDK5R2, and DCAF8L1 was examined in tissues from patients with HCC with high- and low-risk scores
Fig. 8
Fig. 8
Construction and examination of the risk model in the TCGA test cohort. (A) HCC samples from the TCGA test cohort were classified as low-risk or high-risk; (B) In the TCGA test cohort, a Kaplan-Meier plot was used to compare TCGA high-risk and low-risk patients with HCC. (C–E) The risk model provides a diagnostic value for predicting survival rates at 1-year, 3-year, and 5-year follow-up in TCGA cohorts with HCC (C, D, and E); (F) A comparison of survival time and survival status of all patients with HCC in the TCGA test cohort; (G) Expression levels of TRIM36, CYP26B1, PAGE1, CDK5R2, and DCAF8L1 in HCC tissues collected from low-risk and high-risk patients in the TCGA test cohort
Fig. 9
Fig. 9
Analysis of the expression characteristics of the five differentially expressed genes (DEGs) in hepatocellular carcinoma (HCC) tissues. (A and B) Immunohistochemistry (IHC) analysis was performed to detect the expression of CYP26B1, CDK5R2, PAGE1, TRIM36, DCAF8L1, and m1A in HCC tissues (n = 90) and para-carcinoma tissues (n = 90; magnifications: 100× and 200×); (C) Heatmaps of correlations demonstrating the spectrum of relationships among targeting m1A, CYP26B1, CDK5R2, PAGE1, TRIM36, and DCAF8L1. The bar ranging from blue to red (− 1 to 1) represents negative to positive correlations, respectively; (D) Heatmaps of correlations demonstrating the spectrum of relationships among targeting CYP26B1, CDK5R2, PAGE1, TRIM36, DCAF8L1, m1A, and clinical features. The bar ranging from blue to red (− 1 to 1) represents negative to positive correlations, respectively; (E) Expression of TRIM36, CYP26B1, PAGE1, CDK5R2, and DCAF8L1 in sh-ALKBH3 and shNC HepG2 cells assessed through western blot (left) and quantitatively analyzed (right); (F) HepG2 cells were transfected with vector control, ALKBH3, ALKBH3-R122S, or ALKBH3-L177A constructs for 48 h, the expression of TRIM36, CYP26B1, PAGE1, CDK5R2, and DCAF8L1 were checked by western blot (left) and quantitatively analyzed (right); *p < 0.05, **p < 0.01, ***p < 0.001; ns, not significant
Fig. 10
Fig. 10
Analysis of the diagnostic value of the signature of the five differentially expressed genes (DEG) in tissues of hepatocellular carcinoma (HCC) patients. (A) Using tissue chips, HCC samples were divided into low-risk and high-risk categories. Survival time and survival status of each patient with HCC in tissue chips (top). Expression levels of TRIM36, CYP26B1, PAGE1, CDK5R2, and DCAF8L1 in HCC tissues from patients with high-risk and low-risk scores in tissue chips (bottom); (B) Analysis of Kaplan-Meier plots of overall survival rates for patients with HCC according to their risk scores; (C) The receiver operating characteristic curves show the diagnostic value of the risk model for patients with HCC at 1-, 3-, and 5-years after diagnosis; (D) Histologic grade, tumor size, T stage, N stage, alpha-fetoprotein (AFP), and risk score were used to construct the nomogram. (E) A flowchart to illustrate our research
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