Comparison of genome-scale DNA methylation profiles in hepatocellular carcinoma by viral status

Figures & data

Table 1. Characteristics of study participants.

				P-value^a
Characteristics	HBV-HCC	HCV-HCC	NIV-HCC	HBV- vs. HCV-HCC	HBV- vs. NIV-HCC	HCV- vs. NIV-HCC
Age, years, mean±SD	62.3 ± 12.2	64.1 ± 8.5	72.0 ± 11.6	0.852	0.105	0.107
BMI (kg/m2), mean±SD	24.6 ± 5.1	24.8 ± 5.5	26.5 ± 6.3	0.951	0.631	0.369
Gender, n (%)				0.400	0.400	1.000
Male	7 (70)	11 (85)	7 (30)
Female	3 (30)	2 (15)	3 70)
Cirrhosis, n (%)				0.163	0.250	0.020
Yes	1 (12.5)	5 (42)	0 (0)
No	7 (87.5)	7 (58)	10 (100)
Missing	2	1	0
Alcohol consumption^b, n (%)				0.231	0.305	0.054
Yes	1 (10)	4 (31)	0 (0)
No	9 (90)	9 (69)	10 (100)
Smoking status, n (%)				0.382	0.371	0.072
Ever smoker	6 (60)	10 (77)	4 (40)
Never smoker	4 (40)	3 (23)	6 (60)
Hyperlipidemia, n (%)				0.277	0.708	0.162
Yes	3 (23)	1 (8)	3 (30)
No	10 (77)	12 (92)	7 (70)
AJCC stage, n (%)				0.282	0.214	0.292
1	5 (50)	8 (62)	9 (90)
2	1 (10)	2 (15)	0 (0)
3a	2 (20)	1 (8)	1 (10)
3b	2 (20)	0 (0)	0 (0)
4	0 (0)	2 (15)	0 (0)
Diabetes, n (%)				0.183	0.371	0.026
Yes	4 (40)	2 (15)	6 (60)
No	6 (60)	11 (85)	4 (40)
Race				0.196	0.196	0.270
Asian	9 (90)	9 (69)	7 (70)
Black	0 (0)	1 (8)	0 (0)
Pacificist	1 (10)	0 (0)	2 (20)
White	0 (0)	3 (23)	1(10)

^aMann–Whitney rank test for continuous variables, Chi-squre for categorical variables.

^bif they drank more than 2 drinks/day for 10 years total.

Figure 1. Differentially methylated CpGs between HBV-HCC vs. HCV-HCC. (A) Principal component analysis of the methylation data was plotted using the first 2 principal components (PC1 = 67.2% and PC2 = 9.04%). Each dot represents a sample (red for HBV-HCC and blue for HCV-HCC). (B) Unsupervised hierarchical clustering of β-values for differentially methylated loci. Red and blue blocks on the top of the maps represent HBV-HCC (n = 10) and HCV-HCC (n = 13), respectively. (C) Mean methylation level for all 7 differentially-methylated loci among HBV-HCC (left) and HCV-HCC (right). (D) The top IPA network involving differentially methylated genes. Red and blue genes indicate differentially methylated genes and the connected cancer related genes, respectively. (E) The top associated cellular functions with numbers of differentially-methylated genes.

Figure 2. Differentially methylated CpGs between HBV-HCC vs. NIV-HCC. (A) Principal component analysis of the methylation data was plotted using the first 2 principal components (PC1 = 68.4% and PC2 = 6.55%). Each dot represents a sample (red for HBV-HCC and green for NIV-HCC). (B) Unsupervised hierarchical clustering of β-values for differentially methylated loci. Red and green blocks on the top of the maps represent HBV-HCC (n = 10) and NIV-HCC (n = 10), respectively. (C) Mean methylation level for all 7 differentially-methylated loci among HBV-HCC (left) and NIV-HCC (right). (D) The top IPA network involving differentially methylated genes. Red and blue genes indicate differentially methylated genes and the connected cancer related genes, respectively. (E) The top associated cellular functions with numbers of differentially-methylated genes.

Figure 3. Differentially methylated CpGs between HCV-HCC vs. NIV-HCC. (A) Principal component analysis of the methylation data was plotted using the first 2 principal components (PC1 = 64% and PC2 = 6.65%). Each dot represents a sample (blue for HCV-HCC and green for NIV-HCC). (B) Unsupervised hierarchical clustering of β-values for differentially methylated loci. Blue and green blocks on the top of the maps represent HCV-HCC (n = 13) and NIV-HCC (n = 10), respectively. (C) Mean methylation level for all 7 differentially-methylated loci among HCV-HCC (left) and NIV-HCC (right). (D) The top IPA network involving differentially methylated genes. Red and blue genes indicate differentially methylated genes and connected cancer related genes, respectively. (E) The top associated cellular functions with numbers of differentially-methylated genes.

Table 2. Correlations of DM loci and gene expression.

Comparison	Gene name (DASL)	TargetID (HM450)	Relation to UCSC CpG Island	Functional Location	Enhancer	Spearman, r	Spearman, P-value
HBV vs HCV	MFSD7	cg05827190	Island	Body	Enhancer	-0.41	0.0315
HBV vs HCV	FLNC	cg02661879	Island	5'UTR;1stExon	Non-enhancer	-0.43	0.0221
HBV vs NIV	ATF5	cg04315771	S_Shore	TSS1500;5'UTR	Non-enhancer	-0.64	0.0003
HBV vs NIV	KIAA1199	cg02585724	Opensea	5'UTR	Enhancer	0.40	0.0357
HCV vs NIV	AXIN2	cg09231741	Island	Body	Non-enhancer	0.45	0.0156
HCV vs NIV	CES3	cg26538442	Opensea	TSS200	Enhancer	-0.48	0.0099
HCV vs NIV	DHRS3	cg26029221	N_Shelf	Body	Enhancer	-0.51	0.0054
HCV vs NIV	HOXA6	cg23129930	Island	1stExon	Non-enhancer	0.45	0.0161
HCV vs NIV	IGFBP4	cg02627216	N_Shore	TSS1500	Non-enhancer	-0.44	0.0199
HCV vs NIV	LRRC17	cg20066782	Opensea	Body	Enhancer	-0.45	0.0169
HCV vs NIV	RAB11FIP4	cg07146073	Opensea	Body	Enhancer	0.51	0.0055
HCV vs NIV	ROR1	cg03937361	Opensea	Body	Enhancer	0.67	0.0001
HCV vs NIV	ROR1	cg13942627	Opensea	Body	Enhancer	0.44	0.0193
HCV vs NIV	SGIP1	cg09665332	Opensea	Body	Enhancer	-0.52	0.0046
HCV vs NIV	STEAP2	cg06112923	Opensea	Body;3'UTR	Non-enhancer	0.51	0.0057

Figure 4. Association between differentially methylated loci by virus infection and other HCC risk factors. Mann-Whitney rank test was used for alcohol consumption (ever vs. never), cirrhosis (yes vs. no), diabetes (yes vs. no), and smoking (yes vs. no) and Spearman correlation was used for BMI (continuous variable).

Figure 5. Unsupervised hierarchical clustering of β-values for all differentially methylated loci identified by different viral infection. Unsupervised hierarchical clustering of β-values for DM loci (rows) in 33 HCC samples (columns). Red, blue, green blocks on the top of the maps represent HBV-HCC (n = 10), HCV-HCC (n = 13) and NIV-HCC (n = 10), respectively. The clinical characteristics of the samples including alcohol use, BMI, cirrhosis, diabetes, and smoking were represented as other blocks below sample group.