Abstract
Although it is widely accepted that N6-methyladenosine (m6A) RNA methylation plays critical roles in tumorigenesis and progression, the values of m6A modification are less known in hepatocellular carcinoma. The major purpose of our current studies is to investigate the role of m6A regulators in hepatocellular carcinoma and whether it can affect the prognosis of hepatocellular carcinoma. Here we demonstrate that most of the m6A regulators are highly expressed in hepatocellular carcinoma. Furthermore, we cluster hepatocellular carcinoma into two subgroups (cluster 1/2) by applying consensus clustering to m6A regulators. Compared with the cluster 1 subgroup, the cluster 2 subgroup was significantly associated with a higher pathological grade and survival. Based on these findings, we reveal a risk signature by using three m6A regulators, which are not only an independent prognostic marker but also a predictor of the clinicopathological features in hepatocellular carcinoma. In conclusion, m6A regulators are crucial participants in the malignant progression of hepatocellular carcinoma and are potential targets for prognosis.
Introduction
RNA modification was first discovered in the 1960s and was considered to be another epigenetic form analogous to DNA and histone modification (). Among more than 100 kinds of RNA modifications known so far, N6-methyladenosine (m6A) methylation is the most abundant RNA epigenetic modification in RNA, which is dynamically regulated by methyltransferases (“writers”), binding proteins (“readers”), and demethylases (“erasers”) (; ). The prominent methyltransferases complex catalyzes the formation of m6A, which contain at least six “writer” proteins: methyltransferase like 3 (METTL3), methyltransferase like 14 (METTL14), WT1-associated protein (WTAP), VIRMA (KIAA1429), zinc finger CCCH domain-containing protein 13 (ZC3H13), and RNA binding motif protein 15 (RBM15) (). The demethylases catalyze the demethylation of m6A, which mainly include fat mass- and obesity-associated protein (FTO) and α-ketoglutarate-dependent dioxygenase alkB homolog 5 (ALKBH5) (; ). The binding proteins, which recognize and bind with m6A, are consisting of YTH domain family proteins and heterogeneous nuclear ribonucleoprotein C (HNRNPC) (). The biological functions of m6A RNA methylation are involved in regulating all stages of the RNA life cycle, including pre-mRNA splicing, pri-miRNA processing, nuclear output, RNA translation regulation, and RNA degradation ().
The transcriptome-wide mapping of m6A focuses on investigating the landscapes and the functions of the reversible m6A modification in the last decade (). Recently, more and more scientists focus on exploring the association between m6A and human diseases, especially in tumors (; ). A growing appreciation of the biological significance of m6A RNA methylation implied that m6A contributed to tumorigenesis and tumor progression (). The dislocation of m6A is closely associated with various kinds of cancers, such as glioblastoma (GBM), colorectal carcinoma (CRC), pancreatic cancer (PC), and hepatocellular carcinoma (HCC) (; ; ; ). Notably, the roles of m6A regulators in tumors are controversial. METTL3 serves as a tumor suppressor gene in GBM and is considered as an oncogene in CRC or non-small cell lung carcinoma (; ; ). YTHDF2 acts as a tumor suppressor gene in lung cancer and supposed to be an oncogene in PC (; ). The controversial roles of m6A regulators in tumors suggest that the functions of m6A modification in tumors are complicated. Moreover, the literature does not have comprehensive m6A regulator expression and prognosis analysis in tumors.
In this study, we systematically analyze the expression data of 13 m6A modification regulators in HCC from The Cancer Genome Atlas (TCGA) datasets. We demonstrate that most of the 13 m6A regulators are highly expressed among HCC. Moreover, we also find that the m6A regulators are crucial participants in the malignant progression of HCC and a signature with three selected m6A regulators is designed to stratify the prognosis of HCC.
Materials and Methods
Data Acquisition and Processing
The RNA-seq transcriptome and clinical data of 407 HCC samples and 58 adjacent tissue samples were obtained from TCGA1. The workflow type is fragments per kilobase million. The R package “limma” was used to process and delete duplicate genes. The expression of m6A regulators in HCC was extracted from RNA-seq transcriptome. The Wilcoxon test was used to analyze the differential expression of these m6A regulators (p < 0.05 was considered as significant). Incomplete samples of survival data were removed, and finally, 403 samples with complete clinical information were obtained for subsequent analysis. The flow chart of this study is shown in Figure 1.
FIGURE 1
Identify the Role of m6A Regulators in HCC
Gene mutation and copy number variation data were downloaded from the cbioport database2. The interaction and the correlation among m6A regulators were analyzed using the R package “corrplot.” The HCC patients were divided into two subgroups based on the expression of m6A regulators using a cluster analysis method with “ConsensusClusterPlus”3. The R package “ggplot2” is used for principal component analysis (PCA). The R package “survival” was used to plot Kaplan–Meier survival curves. A p < 0.05 was considered as statistically significant.
Construction of a Signature Associated With Prognosis
The roles of m6A regulators in the prognosis of HCC patients were identified by univariate Cox regression analysis; p < 0.05 was considered as significant. A risk signature was built by the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm, and multivariate Cox regression analysis. The signature is expressed as follows: risk score = (coefficient gene 1 × gene 1 expression) + (coefficient gene 2 × expression of gene 2) + … + (coefficient gene n × expression gene n). The median risk score served as a cutoff value to classify patients into high-risk and low-risk groups. The R package “survival ROC” was used to perform time-dependent receiver operating characteristic (ROC) curve analysis to assess the accuracy of the predicted genetic features of time-dependent cancer death. The area under the curve (AUC) was calculated to evaluate the accuracy of the risk prediction model. The R package “survival” was used to plot Kaplan–Meier survival curves.
Independence of Prognostic Factors From Other Clinical Parameters in TCGA
Complete information on the 403 samples included relevant clinical data for univariate and multivariate Cox regression analyses. p < 0.05 was considered as statistically significant.
Construction of a Predictive Nomogram
The independent prognostic factors were chosen as the prognostic model to construct a nomogram in the entire TCGA cohort. The calibration plot and the concordance index (C-index) were used to investigate the calibration and the discrimination of the nomogram.
Results
m6A Regulators in HCC Patients Are Highly Expressed
More and more reports have shown that m6A regulators such as METTL14 (), YTHDF1 (), YTHDF2 (), and WTAP () are essential for the deterioration and the progression of HCC. To further confirm the role of all m6A regulators in HCC, we systematically investigated the expression of 13 m6A regulators (including six writers: KIAA1429, METTL3, METTL14, RBM15, WTAP, and ZC3H13; two erasers: ALKBH5 and FTO; and five readers: HNRNPC, YTHDC1, YTHDC2, YTHDF1, and YTHDF2) in 403 HCC samples and 58 adjacent normal tissue samples from the TCGA database. Information on these m6A regulators is shown in Table 1. Similar to the results of Li’s report (), we found that KIAA1429, METTL3, and HNRNPC are highly expressed in HCC tumor samples. Contrary to Li’s findings, our results show that the expression of METTL14, YTHDC1, YTHDC2, and FTO was also increased in HCC, while the expression of ZC3H13 has no difference between the tumor samples and the adjacent normal tissue samples. In detail, HNRNPC had the highest expression, followed by ALKBH5 and YTHDF1(p < 0.05) (Figures 2A,B). The inconsistent results between Li’s study and our research may be caused by different sample data.
TABLE 1
| Types | Gene symbol | HGNC symbol | Full name |
| Readers | HNRNPC | 5035 | Heterogeneous nuclear ribonucleoprotein C |
| YTHDC1 | 30626 | YTH domain containing 1 | |
| YTHDC2 | 24721 | YTH domain containing 2 | |
| YTHDF1 | 15867 | YTH N6-methyladenosine RNA binding protein 1 | |
| YTHDF2 | 31675 | YTH N6-methyladenosine RNA binding protein 2 | |
| Writers | KIAA1429 | 24500 | vir like m6A methyltransferase associated |
| METTL3 | 17563 | Methyltransferase like 3 | |
| METTL14 | 29330 | Methyltransferase like 14 | |
| RBM15 | 14959 | RNA binding motif protein 15 | |
| WTAP | 16846 | WT1 associated protein | |
| ZC3H13 | 20368 | Zinc finger CCCH-type containing 13 | |
| Erasers | ALKBH5 | 25996 | alkB homolog 5, RNA demethylase |
| FTO | 24678 | FTO alpha-ketoglutarate dependent dioxygenase |
Information on 13 m6A regulators.
FIGURE 2
Mutation and Copy Number Variation of m6A Regulatory Genes in HCC
We then completely analyzed the different mutation and copy number variation (CNV) patterns of m6A regulatory genes in HCC from the cbioport database4. It included gene mutation, amplification, deep deletion, mRNA expression change, and other multiple alterations. The result revealed that m6A regulators were highly expressed in most HCC samples; meanwhile, m6A regulators had gene mutations and CNV (Figure 3A). Specifically, the m6A “writer” gene VIRMA (KIAA1429) had the highest mutation and CNV frequency (40%). as well as “readers” YTHDF1 (18%), ALKBH5 (17%), and WTAP (17%), respectively (Figure 3B).
FIGURE 3
Interaction and Correlation Among m6A Regulators in HCC
Next, we evaluated the interaction and the correlation among m6A regulators. In the cbioport database, we found that there were close interactions among m6A regulators (Figure 3C). Furthermore, we analyzed the expression correlation of these genes in detail based on the expression profile of m6A regulators. The result showed that there was a significant positive correlation between the expressions of most m6A regulators. However, there might be no correlation between YTHDC2 and ALKBH5, ZC3H13 and ALKBH5, ZC3H13 and KIAA1429, and ZC3H13 and YTHDF1 (Figure 3D). These results reveal that, except for a few m6A regulators, most of them may play roles together in HCC.
Classification of HCC Samples Based on the Expression of m6A Regulators
To study whether m6A regulators type HCC samples well, by inputting the expression profile of the m6A regulators, we performed a cluster analysis with the R package “ConsensusClusterPlus” (k = 2–9, Figure 4A). The results revealed that it was most appropriate to divide the patients into two subtypes (Figure 4A). These two subtypes were defined as cluster 1 and cluster 2 in order to further verify the accuracy of the two subtypes. We input all gene expression profiles and subtype information and used the R packages “limma” and “ggplot2” for the PCA of HCC. The PCA results also showed that the HCC sample could be well divided into two subtypes (Figure 4B). Moreover, a significantly shorter survival curve in the cluster 2 subgroup was observed (Figure 4C). Furthermore, the clinical characteristics of the two subtypes are shown in Table 2. These two subgroups were significantly correlated with the WHO grade, gender, age, and lymph node metastasis (p < 0.05) (Figure 4D). These findings further indicate that m6A regulators have a key role in HCC categories. However, the specific molecular differences or other effects between these two subtypes needed further research.
FIGURE 4
TABLE 2
| Variables | Cluster 1 (n = 260) | Cluster 2 (n = 143) | High risk (n = 201) | Low risk (n = 202) |
| Age (years) | ||||
| ≤ 65 | 154 | 78 | 113 | 119 |
| > 65 | 105 | 33 | 56 | 82 |
| Unkonw | 1 | 32 | 32 | 1 |
| Gender | ||||
| Female | 76 | 64 | 77 | 63 |
| Male | 184 | 79 | 124 | 193 |
| Grade | ||||
| G1 + G2 | 183 | 49 | 86 | 146 |
| G3 + G4 | 72 | 61 | 81 | 52 |
| Unknown | 5 | 33 | 34 | 4 |
| Tumor invasion (T) | ||||
| T1 + T2 | 197 | 105 | 174 | 155 |
| T3 + T4 | 60 | 38 | 54 | 44 |
| Unknown | 3 | 0 | 0 | 3 |
| Lymph node (N) | ||||
| N0 | 177 | 100 | 147 | 130 |
| N1 + N2 | 1 | 7 | 6 | 2 |
| Unknown | 83 | 36 | 48 | 70 |
| Metastasis (M) | ||||
| M0 | 184 | 110 | 153 | 140 |
| M1 | 3 | 4 | 4 | 3 |
| Unknown | 73 | 30 | 44 | 59 |
| Tumor stage | ||||
| Stages I + II | 188 | 95 | 137 | 146 |
| Stages III + IV | 56 | 40 | 53 | 43 |
| Unknown | 16 | 8 | 11 | 13 |
The clinical features of hepatocellular carcinoma.
A Risk Signature Built Using Three Selected m6A Regulators
The previous results revealed that m6A regulators play an important role in HCC. In order to explore whether m6A regulators predict the survival prognosis of HCC patients, we combined the expression profile and the clinical data of m6A regulators for univariate Cox regression analysis. The results revealed that a total of seven genes (YTHDF2, KIAA1429, HNRNPC, WTAP, YTHDF1, YTHDC1, and METTL3) were significantly associated with survival prognosis (p < 0.05, Figure 5A). The hazard ratio values of these seven genes were all more than 1 (Figure 5A), indicating that they may be negative prognostic factors for HCC patients.
FIGURE 5
Then, we further analyzed these seven genes through LASSO regression analysis, and the results showed that three m6A regulators (YTHDF1, YTHDF2, and KIAA1429) might be able to construct a prognostic model (Figures 5B,C). A multivariate Cox regression analysis was used to construct a risk signature based on the expression of these three genes (Figure 5D). The univariate and multivariate Cox regression results are shown in Table 3. Risk score = 0.038 × expression of YTHDF1 + 0.064 × expression of YTHDF2 + 0.067 × expression of KIAA1429. The patients were divided into high-risk and low-risk groups by the median risk score (0.939), which served as the cutoff value. The model constructed with the risk signature showed that the AUC values of the time–ROC curve for 3-year overall survival (OS) was 0.665 (Figure 6A). As the risk score increased, the mortality rate increased gradually (Figure 6B). OS in the high-risk group was significantly shorter than in the low-risk group (p < 0.05, Figure 6C). The clinical characteristics of the high- and low-risk groups are shown in Table 2. The high- and low-risk groups were found to correlate significantly with age, grade, and lymph node metastasis in HCC (p < 0.05, Figure 6D). To further assess whether risk score can be used as an independent prognostic indicator, we performed univariate Cox and multivariate Cox regression analyses on the risk score. By univariate analysis, we found that the risk score, WHO grades, and TNM stages were all correlated with the OS (P < 0.001) (Figure 6E). Including these factors into the multivariate Cox regression, the risk score remained significantly associated with the OS (p < 0.001) (Figure 6F). All the results suggest that the prognostic survival models based on these three genes are useful for prognosis in HCC patients. The expression level of these three genes can be used as independent prognostic factors for HCC in the clinic.
TABLE 3
| Variables | Univariate analysis | Multivariate analysis | |||
| Hazard ratio (HR) (95% CI) | P-value | Coefficient | HR (95% CI) | P-value | |
| YTHDF2 | 1.105 (1.062–1.150) | <0.001 | 0.064 | 1.066 (1.016–1.118) | 0.008 |
| YTHDF1 | 1.072 (1.041–1.105) | <0.001 | 0.038 | 1.039 (1.002–1.078) | 0.039 |
| KIAA1429 | 1.140 (1.060–1.227) | <0.001 | 0.067 | 1.070 (0.997–1.159) | 0.099 |
Univariate and multivariate Cox regression analyses of three m6A regulators in hepatocellular carcinoma.
FIGURE 6
Construction of a Prognostic Nomogram
To further evaluate this risk signature, we used the ROC curve to evaluate the model to predict the survival status of HCC for 1, 3, and 5 years, respectively. The results showed that the AUC value for 1 year is 0.72, the AUC value for 3 years is 0.665, and the AUC value for 5 years is 0.599 (Figure 7A). This result shows that the risk signature has a good prognosis for 1 and 3 years, but for the 5-year survival status, the prediction is not so accurate. The reason may be that the number of HCC patients in the TCGA data set who survived more than 5 years is too small. It may be better to add more samples for analysis.
FIGURE 7
Then, we constructed a nomogram to predict OS in patients with HCC based on risk scores (Figure 7B). The calibration plots showed that the performance of the nomogram was best in predicting 1-, 3-, and 5-year OS (Figure 7C).
Consequently, an independent prognostic risk signature was built based on three m6A regulators (YTHDF1, YTHDF2, and KIAA1429) in HCC (Figure 8).
FIGURE 8
Discussion
Accumulating evidence shows that the m6A modification was observed in diverse cancers, which is important for cancer stem cells self-renewal, cancer cell proliferation, and radiotherapy or chemotherapy resistance (). The formation of m6A is catalyzed by the prominent “writer” proteins (). The downstream cellular functions of m6A rely on its “readers” (; ). In addition, HNRNPC is considered as an “m6A switch” to improve the accessibility of RNA binding proteins (). Some reports show that METTL14 is supposed to be an oncogene in acute myeloid leukemia (). WTAP also acts as an oncogene for the development of malignant tumors and a target for immunotherapy of cancer patients (). KIAA1429 acts as an oncogenic factor in breast cancer and contributes to liver cancer progression (; ).
Currently, increasing evidence indicates that m6A regulators are involved in the progression of HCC (; ). The “writer” METTL3 contributes to HCC progression by repressing SOCS2 expression (). The “writers” METTL14 acts as an adverse prognosis factor for HCC by promoting miR126 processing (). KIAA1429 is involved in liver cancer progression and regulates the invasion of HCC by altering the m6A modification of ID2 and GATA3 (; ). The “reader” YTHDF2 was closely associated with the malignancy of HCC modulated by MiR145 (). Our results are consistent with these reports. All m6A regulators, except ZC3H13, are highly expressed in HCC, indicating that m6A regulators have key roles in HCC. The PCA results show that m6A regulators can divide hepatocellular carcinoma patients into two types well, and two clustering subgroups have significant differences in WHO grade, gender, age, and lymph node metastasis. All these results suggest that m6A regulators may be a useful diagnostic classification tool for HCC. However, we only explore the relevance of these two types and clinical features. More detailed studies of m6A regulatory factors in the diagnostic classification of HCC are needed.
There is an important question of whether the m6A regulator expression level can act as a prognostic marker in HCC. Li et al. show that KIAA1429, METTL3, and HNRNPC are highly expressed in HCC tissues, while METTL14, ZC3H13, YTHDC1, YTHDC2, and FTO expressions are lower than those in normal tissues. A three-gene (CSAD, GOT2, and SOCS2) signature regulated by METTL14 is efficient for the prognostication of HCC (), which suggests that m6A regulators have a clinical prognostic impact in HCC. In our present study, we get similar results that the m6A regulator expression levels are essential for hepatocellular carcinoma prognosis. Differently, in our study, we derive the HCC prognostic signature from the expression of three m6A regulators (YTHDF1, YTHDF2, and KIAA1429). As we have observed, the three-gene signature generated by risk score can stratify the OS for HCC patients. In our results, the expression of all m6A regulators, except for ZC3H13, is higher in the tumor samples than in the adjacent normal tissue. Inconsistent results may result from different sample amounts and sources. More samples are used in our study than in their research, and all our study data of 407 samples are from the TCGA database, while 64 of 307 patients included in their report are from the GSE116174 dataset (others are from the TCGA database). Moreover, that report focuses on studying the function of METTL14 and establishing a METTL14-regulated three-gene (CSAD, GOT2, and SOCS2) signature and nomogram to predict the OS of HCC. However, in our study, the HCC prognostic signature derives from directly using three m6A regulators (YTHDF1, YTHDF2, and KIAA1429). The three regulators are considered to be useful markers for the diagnosis and the treatment of HCC patients in the clinic. Because the signature is generated based on the expression level of m6A regulators which do not involve the downstream target genes, additional trials are needed to find the target genes and the signaling pathways of these three regulators. That should be a good strategy to treat HCC by targeting YTHDF1, YTHDF2, and KIAA1429 combined with targeting their downstream genes.
In our results, a very surprising one is that ZC3H13 expression has no difference between tumor samples and adjacent tissue samples. In addition, ZC3H13 is not correlated with ALKBH5, KIAA1429, and YTHDF1. The previous report shows that the expression of ZC3H13 is lower than those in normal tissues (). ZC3H13 is a classical CCCH zinc finger protein localized in human chromosome 13q14.139 (). As an m6A methylation writer, the role of ZC3H13 in tumors is controversial. A report shows that ZC3H13 serves as a tumor suppressor protein in colon carcinoma and colorectal cancer by regulating the Ras-ERK signaling pathway (). Other reports consider it as an oncogenic protein by binding with K-ras and activating the NF-κB signal (). The controversial roles of ZC3H13 in tumors give us a clue that the essentiality and the functions of m6A RNA methylation in tumors are complicated, and further studies are needed to focus on its prognostic value in HCC.
Another interesting result is that RNA binding protein HNRNPC expression is elevated in HCC. This is consistent with the previous report (). The essentiality of HNRNPC in tumors is not clear. Certain studies show that HNRNPC promotes cell proliferation, apoptosis, and tumor growth (; ). In addition, a high expression of HNRNPC has a poor prognosis and may act as a candidate biomarker for chemoresistance in gastric cancer (). Besides that, HNRNPC also acts as a dengue virus NS1-interacting protein and plays an important role during the replication of the hepatitis C virus and hepatitis delta virus (; ). Our results imply that HNRNPC is a candidate biomarker for HCC. More work is needed to verify the relevant regulatory pathways.
Among 13 m6A RNA methylation regulators, the m6A methylation writer VIRMA (KIAA1429) has the most obvious mutation in HCC. VIRMA is identified as the component associated with WTAP in mammalian cells and involved in the regulation of m6A methylation events in 3’UTR and near the stop codon (). Certain studies show that KIAA1429 contributes to liver cancer progression through N6-methyladenosine-dependent post-transcriptional modification of GATA3 and regulates the migration and the invasion of HCC by altering the m6A modification of ID2 mRNA (; ). It is necessary to study the roles of obvious mutation of VIRMA in HCC occurrence and progression.
Conclusion
In conclusion, a high expression of m6A regulators implies that dysregulated m6A play important roles in HCC. Furthermore, two clustering subgroups indicate that m6A RNA methylation plays essential roles in the prognosis and the clinicopathological features of HCC. In addition, a prognostic risk signature with three selected m6A RNA methylation regulators gives us a clue that m6A RNA methylation regulators are potentially useful for prognostic stratification and targeting treatment in HCC.
Statements
Data availability statement
The RNA-seq transcriptome and clinicopathological datas of 407 HCC samples and 58 adjacent normal tissue samples were obtained from TCGA (https://portal.gdc.cancer.gov/).
Author contributions
WL and MT designed the study. WL and FX performed the analysis and drafted the manuscript. WL, MT, CZ, DL, and FX contributed to the editing of the manuscript. All authors contributed to the article and approved the submitted version.
Funding
This study was supported by the National Natural Science Foundation of China (no. 81503074), the Shenzhen Nanshan District Health and Family Planning Bureau (Shenzhen Nanshan District Science and Technology Project, no. 2018045), and the Scientific Research Project of Hunan Health Committee (no. 20200985).
Conflict of interest
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.
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Summary
Keywords
m6A modification, m6A regulators, hepatocellular carcinoma, a risk signature, prognostic marker
Citation
Liu W, Zhong C, Lv D, Tang M and Xie F (2020) N6-Methyladenosine RNA Methylation Regulators Have Clinical Prognostic Values in Hepatocellular Carcinoma. Front. Genet. 11:863. doi: 10.3389/fgene.2020.00863
Received
13 December 2019
Accepted
15 July 2020
Published
12 August 2020
Volume
11 - 2020
Edited by
Mattia Pelizzola, Italian Institute of Technology (IIT), Italy
Reviewed by
Deli Liu, Weill Cornell Medicine, Cornell University, United States; Nan Li, China-Japan Friendship Hospital, China; Zhuoxian Rong, Xiangya Hospital, Central South University, China
Updates
Copyright
© 2020 Liu, Zhong, Lv, Tang and Xie.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Mengjie Tang, tangmengjie@hnca.org.cnFeng Xie, xiefeng101787@126.com
This article was submitted to Computational Genomics, a section of the journal Frontiers in Genetics
Disclaimer
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