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Abstract

In China, hepatocellular carcinoma (HCC) is the most commonly diagnosed cancer and the leading cause of cancer death in men, followed by lung and stomach cancer. There was an urgent need to identify novel prognostic biomarkers for HCC. We explored the expression pattern of m6A related proteins in HCC tissues by using TCGA in this study. We found that the m6A ‘reader’ YTHDF1 was significantly upregulated in HCC and was positive correlated with pathology stage. Kaplan-Meier analysis showed that Lower YTHDF1 expression level was associated with better survival of HCC patients. Furthermore, we performed GO and KEGG pathway analysis of YTHDF1 co-expressed genes and found YTHDF1 played an important role in regulating HCC cell cycle progression and metabolism. We believed that this study will provide a potential new therapeutic and prognostic target for HCC.

Keywords

YTHDF1 HCC m6A prognostic biomarker

1. Introduction

In China, hepatocellular carcinoma (HCC) is the most commonly diagnosed cancer and the leading cause of cancer death in men, followed by lung and stomach cancer. Moreover, according to Cancer Statistics in China of 2015 [1], HCC is the second and fifth most common cause of cancer death in males and females, respectively. Recently a series of therapies including surgery, chemotherapy, and radiotherapy were developed for patients with HCC, however, the mortality rate of HCC was still very high [2, 3, 4, 5]. Although tumor-node-metastasis (TNM) classification systems was the basic prognostic model, there was still an urgent need to identify novel prognostic biomarkers for HCC.

N ${}^{6}$ -methyladenosine (m6A), a kind of post-transcri- ptional mRNA modification, was the most prevalent and internal modification that occurs in the messenger RNAs (mRNA) of most eukaryotes [6, 7, 8, 9]. m6A modification played crucial roles in regulating mRNA splicing, export, localization, translation, and stability [10, 11, 12, 13]. Recent studies had reported m6A modification were involved in the progression of various of human diseases, such as obesity [14, 15, 16], cancer [17, 18] and embryonic development [19]. The m6A methylation is mediated by a series of protein factors, including m6A ‘writer’ (METTL3 and METTL14 [20, 21]), ‘erasers’ (FTO [22] and ALKBH5 [23]) and ‘readers’ (YTHDF1 [24], YTHDF2 [25] and YTHDF3 [26]). Previous reports had showed METTL3 [27], FTO and ALKBH5 [28] were dysregulated in human glioma and breast cancers. However, the special expression patterns of these m6A related proteins in HCC were never reported.

In this study, we explored the expression pattern of m6A related proteins in HCC tissues by using TCGA. We evaluated the correlations between m6A related proteins and HCC patients’ clinical features including age, pathological stage and recurrence free survival. We believed that this study will provide a potential new therapeutic and prognostic target for hepatocellular carcinoma.

2. Materials and methods

2.1 Patients and clinicopathological data

The level-3 expression data (RNA-seqV2) and clinicopathological data of 373 HCC patients and 50 normal samples were downloaded from The Cancer Genome Atlas (TCGA, https://tcga-data.nci.nih.gov/tcga/) data portal. The data on patients’ clinical features including age at diagnosis, days to last follow-up, pathology T stage and pathology N stage were retrospectively abstracted from patient records. All the patients were staged using the 2009 TNM classification of American Joint Committee on Cancer/International Union Against Cancer.

A total of 372 patients with survival data in the TCGA database were given a follow-up exam ranging from 0 to 120.73 months, and the median survival was 19.66 months. For the analysis of survival and follow-up, the date of surgery was used as the beginning of the follow-up period. Overall survival (OS) and disease-free survival (DFS) were calculated in months from the data of diagnosis to the time of death and to the time of tumor progression or recurrence, or death of the patient from HCC, respectively. All patients that died from diseases other than HCC or unexpected events were excluded from the cohort. In this study, we focused on exploring whether m6A related protein were associated with 5-years OS and DFS. In present study, the upper quarter m6A related mRNA expression in all HCC tissues was used as the cutoff point to divide all cases into gene-high and gene-low groups.

2.2 GO and KEGG pathway analysis

To identify functions of m6A related proteins in HCC, we performed GO function enrichment analysis by using MAS 3.0 (http://bioinfo.capitalbio.com/mas3/project/index). KEGG pathway enrichment analysis was also performed to identify pathways enriched in HCC using DAVID system (https://david.ncifcrf.gov/home.jsp) [29]. The q-value $<$ 0.05 was considered as significantly.

2.3 Co-expression network construction and analysis

In present study, we performed co-expression analysis of candidate genes by calculating the Pearson correlation coefficient of paired genes. The co-expressed gene pairs with the absolute value of Pearson correlation coefficient $\geqslant$ 0.3 were selected for further study.

Figure 1.

The expression level of seven m6A related genes (YTHDF1, YTHDF2, ALKBH1, ALKBH5, FTO, METTL3, and METTL14) in a TCGA dataset. YTHDF1 (A) and METTL3 (G) were up-regulated and YTHDF2 (B), ALKBH1 (D), ALKBH5 (E), FTO (F) and METTL14 (H) were down-regulated in hepatocellular carcinoma. Significance was defined as $p<$ 0.05 (*, $p<$ 0.05; ***, $p<$ 0.001).

2.4 PPI network construction

The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING; string-db.org/) database is a pre-computed global resource for the investigation and analysis of associations between proteins. We used STRING to analyze the interactions between DEGs and constructed a PPI network. The database reveals protein interactions, including experimental and predicted protein interaction information. In this study, only experimentally validated interactions with a combined score $\geqslant$ 0.4 were selected as significant. Then, PPI networks were constructed using the Cytoscape software.

Figure 2.

The correlation between the expression levels of m6A related proteins and clinicopathological characteristics. YTHDF1 (A) (but not YTHDF2 (B), YTDHF3 (C), ALKBH1 (D), ALKBH5 (E), FTO (F), METTL3 (G), METTL14 (H)) expression was significantly up-regulated in Stage III/ IV HCC compared to Stage I and Stage II HCC. Significance was defined as $p<$ 0.05 (*, $p<$ 0.05; ***, $p<$ 0.001).

2.5 Statistical analysis

Numerical data were presented as the mean $\pm$ standard deviation of at least three determinations. Statistical comparisons between groups of normalized data were performed using Student’s t-test using SPSS 13.0 software (SPSS, Inc., Chicago, IL, USA) or Mann-Whitney U-test according to the test condition. Statistical comparisons among multiple groups of normalized data were performed using analysis of v one-way ANOVA according to the test condition. The log-rank test was used to compare differences in survival times. Kaplan Meier and Cox regression analyses were utilized to assess the association between candidate genes and overall survival as well as the prognosis of HCC. $P<$ 0.05 was considered to indicate a statistically significant difference with a 95% confidence level.

Figure 3.

The correlation between the expression levels of m6A related proteins and 5-year survival rate. Upregulation of YTHDF1 (A) and YTHDF2 (B) and down-regulation of FTO (F) was correlated with significantly shorter 5-year survival rate.

Figure 4.

The correlation between the expression levels of m6A related proteins and DFS rates. Upregulation of YTHDF1 (A) and down-regulation of YTHDF3 (C), ALKBH5 (E), FTO (F) and METTL14 (H) was correlated with shorter DFS rate.

3. Results

3.1 m6A related proteins were dysregulated in hepatocellular carcinoma

In present study, we analyzed TCGA data to explore the expression patterns of m6A related proteins in hepatocellular carcinoma. A total of 373 HCC patients and 50 normal samples were included in TCGA data. As shown in Fig. 1, we observed seven m6A related genes (YTHDF1, YTHDF2, ALKBH1, ALKBH5, FTO, METTL3, and METTL14) were differentially expressed in HCC compared to match normal patients. Of these genes, YTHDF1 (Fig. 1A, $p<$ 0.001) and METTL3 (Fig. 1G, $p<$ 0.001) were up-regulated and YTHDF2 (Fig. 1B, $p<$ 0.05), ALKBH1 (Fig. 1D, $p<$ 0.001), ALKBH5 (Fig. 1E, $p<$ 0.001), FTO (Fig. 1F, $p<$ 0.05) and METTL14 (Fig. 1H, $p<$ 0.001) were down-regulated in hepatocellular carcinoma (Fig. 1).

3.2 YTHDF1 was positive correlated with pathology stage in hepatocellular carcinoma

We next analyzed the correlation between the expression levels of m6A related proteins and clinicopathological characteristics. As shown in Fig. 2, we found that YTHDF1 expression was significantly up-regulated in Stage III/IV HCC compared to Stage I (Fig. 2A, $p<$ 0.001) and Stage II (Fig. 2A, $p<$ 0.05) HCC. However, we did not observe the difference of expression levels of other m6A related genes among different stages of HCC.

3.3 Overexpression of YTHDF1 is associated with poor prognosis in patients with hepatocellular carcinoma

OS curves were plotted according to candidate gene expression level by the Kaplan-Meier method and log-rank tests. As shown in Fig. 3, overexpression of YTHDF1 (Fig. 3A, $p=$ 0.013) and YTHDF2 (Fig. 3B, $p=$ 0.0235) and down-regulation of FTO (Fig. 3F, $p=$ 0.0038) was correlated with significantly shorter 5-year survival rate. In a multivariate analysis (Table 1), YTHDF1 expression remained an independent prognostic variable for

Table 1
Multivariate analysis of BCR-free survival for all HCC patients

Comparison	Hazard ratio	95% CI	$P$ -value
	for recurrence
pTNM stage	1.375	1.112, 2.694	0.007
YTHDF1 expression	1.571	1.023, 2.188	0.005

overall survival. We also explored whether these m6A related genes were associated with disease free survival of HCC patients. Similar to OS analysis, we also found the DFS rates were lower in YTHDF1-high patients in both datasets comparing to YTHDF1-low patients (Fig. 4A, $p=$ 0.0131). Interestingly, we found down-regulation of YTHDF3 (Fig. 4C, $p=$ 0.038), ALKBH5 (Fig. 4E, $p=$ 0.0132), FTO (Fig. 4F, $p=$ 0.0450) and METTL14 (Fig. 4H, $p=$ 0.0306) were correlated with shorter DFS rate (Fig. 4).

Figure 5.

(A) The protein-protein interaction network of YTHBF1 co-expressed genes in the HCC. GO (B) and pathway (C) analysis of YTHBF1 co-expressed genes.

3.4 Functional annotation of YTHDF1

Collectively, our results had showed overexpression of YTHDF1 was associated with the progression and could predict poor prognosis of HCC. However, the potential roles of YTHDF1 in HCC remains unclear. To predict the functions of the YTHBF1, we first constructed gene co-expression networks to identify interactions among differentially expressed mRNAs and YTHBF1 by using TCGA. The co-expressed YTHBF1-gene pairs with the absolute value of Pearson correlation coefficient $\geqslant$ 0.3 were selected and the co-expression network was established. Based on the information in the STRING database, we constructed a protein-protein interaction network of YTHBF1 co-expressed genes in the HCC (Fig. 5A). We could observe three PPI hubs were collected using these genes.

MAS 3.0 system was used to perform GO and pathway analysis on the co-expressed genes. A total of 413 differentially expressed genes were classified according to GO term. Our analysis revealed that these co-expressed genes were associated with transcription, cell cycle, cell division, mitosis, oxidation reduction, response to DNA damage stimulus, DNA repair, DNA replication, RNA splicing, chromatin modification, microtubule-based movement, protein amino acid phosphorylation, modification-dependent protein catabolism, proteolysis, lipid metabolism (Fig. 5B). KEGG pathway enrichment analyses showed co-expre- ssed genes of YTHBF1 mainly participate in regulating Valine, leucine and isoleucine degradation, Cell cycle, Complement and coagulation cascades, Propanoate metabolism, Fatty acid metabolism, Butanoate metabolism, Lysine degradation, beta-Alanine metabolism, Tryptophan metabolism, Caprolactam de- gradation, DNA polymerase, PPAR signaling pathway, Homologous recombination, Limonene and pinene degradation, Fatty acid elongation in mitochondria, etc. (Fig. 5C).

4. Discussion

Hepatocellular carcinoma is one of the most frequently diagnosed cancer worldwide [30]. In the past years, a series of therapies including surgery, chemo- therapy, and radiotherapy were developed for HCC treatment. Meanwhile many efforts were payed to identify prognostic biomarkers of HCC. A few proteins including HAO2 [31], ACTL6A [32], and RBMY [33, 34] were reported to be differentially expressed in HCC. Of note, long non-ding RNAs (such as ZEB1-AS1 [35], SNHG20 [36, 37] and ATB [38]) were also showed to be associated with HCC progression. However, the prognosis of HCC patients still remains dismal. Therefore, there was still an urgent need to identify novel prognostic biomarkers for HCC. In present study, we explored whether m6A related genes could serve as novel biomarkers for HCC. We found many m6A related proteins, including YTHDF1, YTHDF2 and FTO, were involved in HCC progression. For example, seven m6A related genes (YTHDF1, YTHDF2, ALKBH1, ALKBH5, FTO, METTL3, and METTL14) were differentially expressed in HCC compared to match normal patients. Moreover, we observed YTHDF1, YTHDF2 and FTO were correlated with 5-year survival rate. Furthermore, we found YTHDF1, YTHDF3, ALKBH5, FTO and METTL14 were correlated with DFS rate.

N6-methyladenosine (m6A), the most prevalent and internal modification of mRNAs, played crucial roles in regulating mRNA splicing, export, localization, translation, and stability. Several proteins, including YTHDF1, YTHDF2, YTHDF3, ALKBH1, ALKBH5, FTO, METTL3 and METTL14 were reported to mediated the m6A methylation. Previous studies had showed expression of these m6A related genes were dysregulated in several cancers (e.g., breast cancer, lung cancer and glioma). For example, overexpression of ALKBH5 were associated with lower survival time of glioma [39]. METTL3 was significantly up-regulated in non-small-cell lung carcinoma (NSCLC). These results suggest that these proteins were involved cancer development and progression. Moreover, Chen et al. found RNA N6-methyladenosine methyltransferase METTL3 promoted liver cancer progression through YTHDF2 dependent post-transcriptional silencing of SOCS2 [40]. In this study, we observed that YTHDF1 and METTL3 were up-regulated and YTHDF2, ALKBH1, ALKBH5, FTO and METTL14 were down-regulated in hepatocellular carcinoma.Moreover, we found that upregulation of YTHDF1 was significantly up-regulated in high grade of HCC. Kaplan-Meier analysis showed that overexpression of YTHDF1 and down-regulation of FTO was correlated with significantly shorter OS and DFS survival rate of HCC. These results showed YTHDF1 could serve as a biomarker for HCC. To the best of our knowledge, it was firstly reported that m6A related genes was involved in the prognosis of HCC.

YTHDF1, containing a YTH RNA-binding domain, could bind to m6A and was involved in Protein recruitment. In present study, we also explored the potential function roles of YTHDF1 in HCC by using GO and KEGG pathway analysis. Our analysis revealed that these co-expressed genes were associated with transcription, cell cycle, cell division, mitosis, oxidation reduction, response to DNA damage stimulus, DNA repair, DNA replication, RNA splicing, chromatin modification, microtubule-based move-ment, protein amino acid phosphorylation, modification- dependent protein catabolism, proteolysis, lipid meta- bolism (Fig. 5B). KEGG pathway enrichment analyses showed co-expressed genes of YTHBF1 mainly participate in regulating Valine, leucine and isoleucine degradation, Cell cycle, Complement and coagulation cascades, Propanoate metabolism, Fatty acid metabo- lism, Butanoate metabolism, Lysine degradation, beta-Alanine metabolism, Tryptophan metabolism, Caprolactam degradation, DNA polymerase, PPAR signaling pathway, Homologous recombination, Limonene and pinene degradation, Fatty acid elongation in mitochondria, etc. Of note, we found YTHDF1 was also significantly associated with PPAR signaling pathway, p53 signaling pathway, and Notch signaling pathway. Emerging studies had showed these pathways played important roles in HCC progression [41, 42, 43, 44]. This analysis was added as supplementary Table 1. All these results suggested that YTHDF1 may play an important role in regulating HCC cell cycle progression, and were consist with previous reported that YTHDF1was a cell-cycle-regulated gene.

5. Conclusion

In conclusion, our study showed for the first time that YTHDF1 was significantly upregulated in HCC. Furthermore. our results showed YTHDF1was upregulated in high pathology stage HCC. Kaplan-Meier analysis showed that Lower YTHDF1expression level was associated with better survival of HCC patients. Furthermore, we performed GO and KEGG pathway analysis of YTHDF1 co-expressed genes and found YTHDF1 played an important role in regulating HCC cell cycle progression and metabolism. We believed that this study will provide a potential new therapeutic and prognostic target for HCC.

Footnotes

Conflict of interest

None.

Supplementary data

Supplementary Table 1

The important pathways in HCC progression

Pathway	Count	p-value	q-value
Valine, leucine and isoleucine	22	1.09E-29	4.60E-28
degradation
Cell cycle	29	1.18E-28	4.14E-27
Complement and coagulation	21	2.25E-23	5.27E-22
cascades
Propanoate metabolism	15	5.67E-20	9.21E-19
Fatty acid metabolism	16	2.21E-19	2.74E-18
Butanoate metabolism	13	9.71E-16	8.20E-15
Lysine degradation	13	7.33E-14	4.83E-13

Table 1, continued
Pathway	Count	p-value	q-value
Beta-Alanine metabolism	10	1.17E-13	7.46E-13
Tryptophan metabolism	11	5.95E-12	3.40E-11
Caprolactam degradation	5	7.23E-09	2.40E-08
DNA polymerase	8	1.61E-08	5.01E-08
PPAR signaling pathway	10	2.35E-08	7.18E-08
Homologous recombination	7	5.38E-08	1.55E-07
Limonene and pinene degradation	6	7.32E-08	2.09E-07
Fatty acid elongation in	5	8.40E-08	2.35E-07
mitochondria
Pyrimidine metabolism	10	3.67E-07	9.05E-07
Gap junction	10	3.67E-07	9.05E-07
Purine metabolism	12	7.53E-07	1.69E-06
Pyruvate metabolism	7	8.83E-07	1.94E-06
Benzoate degradation via CoA	5	9.49E-07	2.00E-06
ligation
Bile acid biosynthesis	6	1.66E-06	3.27E-06
Retinol metabolism	8	1.94E-06	3.68E-06
Ubiquitin mediated proteolysis	11	2.12E-06	3.96E-06
p53 signaling pathway	8	3.07E-06	5.53E-06
Citrate cycle (TCA cycle)	6	3.70E-06	6.46E-06
Drug metabolism – cytochrome	8	4.25E-06	7.28E-06
P450
Base excision repair	6	6.30E-06	1.05E-05
Ribosome	9	7.37E-06	1.20E-05
3-Chloroacrylic acid degradation	4	1.79E-05	2.73E-05
Alanine and aspartate metabolism	5	3.23E-05	4.77E-05
Histidine metabolism	5	4.53E-05	6.41E-05
Non-small cell lung cancer	6	6.87E-05	9.47E-05
Adherens junction	7	7.46E-05	1.00E-04
Pathogenic Escherichia coli	6	7.63E-05	1.02E-04
infection – EHEC
Pathogenic Escherichia coli	6	7.63E-05	1.02E-04
infection – EPEC
Synthesis and degradation of	3	1.69E-04	2.10E-04
ketone bodies
Methionine metabolism	4	2.00E-04	2.39E-04
Mismatch repair	4	2.00E-04	2.39E-04
Glycosylphosphatidylinositol	4	2.80E-04	3.16E-04
(GPI) – anchor biosynthesis
Metabolism of xenobiotics by	6	2.93E-04	3.27E-04
cytochrome P450
Ascorbate and aldarate metabolism	4	3.27E-04	3.60E-04
Tyrosine metabolism	5	3.45E-04	3.77E-04
Parkinson’s disease	8	4.07E-04	4.36E-04
Chronic myeloid leukemia	6	4.26E-04	4.54E-04
Urea cycle and metabolism of	4	4.39E-04	4.66E-04
amino groups
Drug metabolism – other enzymes	5	5.06E-04	5.31E-04
Starch and sucrose metabolism	5	5.54E-04	5.74E-04
Inositol phosphate metabolism	5	7.19E-04	7.25E-04

Table 1, continued
Pathway	Count	p-value	q-value
Pantothenate and CoA biosynthesis	3	8.66E-04	8.52E-04
Glyoxylate and dicarboxylate	3	8.66E-04	8.52E-04
metabolism
Small cell lung cancer	6	8.84E-04	8.63E-04
ErbB signaling pathway	6	9.39E-04	9.09E-04
Geraniol degradation	2	9.80E-04	9.42E-04
Prostate cancer	6	9.97E-04	9.52E-04
Glycolysis/Gluconeogenesis	5	0.001432	0.001337
Glioma	5	0.001535	0.001421
Pancreatic cancer	5	0.002567	0.002257
Androgen and estrogen	4	0.002689	0.002354
metabolism
GnRH signaling pathway	6	0.002709	0.002362
Systemic lupus erythematosus	7	0.002765	0.002401
Phosphatidylinositol signaling	5	0.00306	0.002614
system
Pentose and glucuronate	3	0.003979	0.003241
interconversions
Selenoamino acid metabolism	3	0.004455	0.003581
Pentose phosphate pathway	3	0.004455	0.003581
Arachidonic acid metabolism	4	0.006315	0.005009
Phenylalanine, tyrosine and	2	0.006983	0.005508
tryptophan biosynthesis
Regulation of actin cytoskeleton	8	0.007736	0.006068
Alzheimer’s disease	7	0.008414	0.006409
Tight junction	6	0.008636	0.006462
Folate biosynthesis	3	0.008746	0.006521
Oxidative phosphorylation	6	0.009558	0.007101
Primary immunodeficiency	3	0.010295	0.007465
1- and 2-Methylnaphthalene	2	0.011797	0.008368
degradation
1, 4-Dichlorobenzene degradation	1	0.012902	0.008985
Melanoma	4	0.013498	0.009368
Bladder cancer	3	0.016901	0.011394
Glycine, serine and threonine	3	0.017998	0.012018
metabolism
Nucleotide excision repair	3	0.019134	0.012696
Type II diabetes mellitus	3	0.020309	0.01337
Notch signaling pathway	3	0.022775	0.014786
Monoterpenoid biosynthesis	1	0.025638	0.016343
Glutathione metabolism	3	0.026765	0.01701
Biosynthesis of unsaturated fatty	2	0.032374	0.020032
acids
Phenylalanine metabolism	2	0.032374	0.020032
Insulin signaling pathway	5	0.033766	0.020772
Nicotinate and nicotinamide	2	0.038035	0.023068
metabolism
Tetrachloroethene degradation	1	0.03821	0.023068
Galactose metabolism	2	0.044042	0.025671
Melanogenesis	4	0.046053	0.02655
Wnt signaling pathway	5	0.047776	0.027319

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Overexpression of YTHDF1 is associated with poor prognosis in patients with hepatocellular carcinoma

Abstract

Keywords

1. Introduction

2. Materials and methods

2.1 Patients and clinicopathological data

2.2 GO and KEGG pathway analysis

2.3 Co-expression network construction and analysis

3.1 m6A related proteins were dysregulated in hepatocellular carcinoma

3.2 YTHDF1 was positive correlated with pathology stage in hepatocellular carcinoma

3.3 Overexpression of YTHDF1 is associated with poor prognosis in patients with hepatocellular carcinoma

Table 1 Multivariate analysis of BCR-free survival for all HCC patients

4. Discussion

5. Conclusion

Footnotes

Conflict of interest

Supplementary data

References

Table 1
Multivariate analysis of BCR-free survival for all HCC patients