







































Determinants of Hepatocellular 
Carcinoma in the United States: 
Differences in Risk Factor and 
Genetic Susceptibility by Race/

Ethnicity 
Mehwish Rafique, Dana Kristjansson, PhD. 

Volume One 
Edition One 
February 2021 

 
GEORGETOWN SCIENTIFIC
RESEARCH JOURNAL

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https://doi.org/10.48091/LKML8578 

Determinants of Hepatocellular Carcinoma in 

the United States: Differences in Risk Factor 

and Genetic Susceptibility by Race/Ethnicity 

Mehwish Rafique1 and Dana Kristjansson2 
1 Department of Systems Medicine, Georgetown University School of 
Medicine, Washington DC 
2 Department of Genetics and Bioinformatics, Norwegian Institute of Public 
Health, Oslo, Norway 

E-mail: Mr1502@georgetown.edu
Abstract 
Background: Hepatocellular carcinoma (HCC) is one of the few cancers with an increasing incidence and 
mortality worldwide. This study aims to determine the contribution of known risk factors for HCC by 
race and ethnicity. 

Methods:  Data on race, ethnicity, age, and gender were obtained from National Health and Nutrition 
Examination Survey (NHANES). Population attributable fractions (PAFs) of risk factors were estimated 
using non-invasive scoring measures of Hepatitis B and C virus infection, excessive alcohol use, smoking, 
diabetes and emerging metabolic risk factors [non-alcoholic steatohepatitis advanced cirrhosis (NASH) 
and non-alcoholic fatty liver disease-advanced fibrosis (NAFLD-fib)] over a 10-year period, 1999-2002 
and 2009-2012.  Genetic analysis was performed using DisGenet platform by attaining the top enriched 
genes strongly related to HCC. Furthermore, cytoscape network was used to form a gene-disease network 
association. 

Results:   NASH-cirrhosis increased in the overall population and among all race and ethnic groups. Both 
liver fat accumulation and ALT levels vary among different populations; however, Hispanics have the 
highest prevalence of NAFLD and elevated ALT levels.  Non-Hispanic (NH) blacks and Hispanics had 
a 3 to 4 times higher PAF for HCC than whites attributed due to chronic liver diseases, including NASH-
cirrhosis and NAFLD-fib. Our genetic analysis demonstrated that PNPLA3 polymorphism is strongly 
associated with NAFLD-fib, which appears to represent susceptibility to liver disease among the Hispanic 
community. 

Conclusion: Hispanics and NH blacks are at a disproportionately higher risk for HCC in part due to the 
higher prevalence of liver disease comorbidities, including NASH-cirrhosis and NAFLD-fib. Compared 

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to NH whites, Hispanics and NH blacks have a higher baseline risk for liver cancer due to non-metabolic 
factors, which may include a genetic susceptibility. Metabolic risk factors have increased and are now 
contributing to nearly half of HCC cases in the US.

Keywords: non-alcoholic fatty liver disease advanced fibrosis; hepatocellular carcinoma; non-alcoholic 
steatohepatitis; metabolic risk factors; non-metabolic risk factors; population attributable fraction

1. Introduction
Hepatocellular carcinoma (HCC) is the third

most common cause of cancer related deaths 
worldwide.1 In the US, HCC incidence and 
mortality rates are increasing at a rate of 3% per 
year and are distributed disproportionally among 
certain racial/ethnic groups. 2   
 HCC most often occurs among individuals 
who have chronic liver diseases. Nonalcoholic fatty 
liver disease (NAFLD) and non-alcoholic 
steatohepatitis (NASH) are growing and 
becoming the leading risk factors for HCC. 
NAFLD-fib and its subtype NASH-cirrhosis 
affect approximately 30% and 5%, respectively, of 
the US population.3 The major risk factors are 
hepatitis C virus (HCV) infection, hepatitis B 
virus (HBV) infection, cigarette smoking, 
excessive alcohol consumption, hereditary genetic 
diseases, and metabolic disorders (diabetes, 
obesity, impaired glucose tolerance, metabolic 
syndrome, and non-alcoholic fatty liver disease).4 
Some risk factors of metabolic diseases have been 
shown to be more prominent in certain ethnic 
groups. Data from the United States National 
Center for Health Statistics (2000-2006) 
identified chronic liver diseases as the sixth most 
common cause of death in the Hispanic 
population.5 Obesity and diabetes are highly 
prevalent among both Hispanic and non-Hispanic 
(NH) Blacks due to lifestyle choices, diet, or 
genetic polymorphism, which causes all race-
related genetic differences between different 
groups. The proportion of incident cases of heavy 
drinkers in the United States between 1984 and 

1992 was highest among NH blacks (51%), 
followed by Hispanics (43%) and whites (32%). 
There are several lines of evidence suggesting that 
NH blacks who consume alcohol have greater liver 
enzyme elevation than whites, which further leads 
to liver disease .6,7 
 The prevalence of NAFLD-fib and risk of 
progression is higher among Hispanics than other 
racial and ethnic groups.8 The higher incidence of 
HCC among Hispanics is driven by higher levels 
of sugar, carbohydrates and intake of saturated fat 
as compared to whites. Obesity and insulin 
resistance, two important risk factors for the 
metabolic syndrome, have been found to have a 
positive correlation with NASH-cirrhosis in 
Hispanic persons only.5 Hispanics and NH blacks 
have also been shown to have higher HCC rates 
than whites. Cirrhosis rates are higher for NH 
blacks than for whites, and the highest cirrhosis 
mortality rates are observed among Hispanics.7 
Mortality from chronic liver disease in Hispanic 
people in the United States is nearly 50% higher 
than in NH white persons (13.7 per 100,000 in 
Hispanic persons vs 9.2 in NH whites and 7.5 in 
African American persons).5 
 In addition to known HCC risk factors, it is 
likely that access to preventive health education 
and early treatment may be a barrier to some racial 
and ethnic groups. The incidence of HCC varies 
by race and ethnicity primarily as a result of 
differences in the prevalence of major risk factors 
and also disparities in access to high-quality 
healthcare.9    Socioeconomic disadvantage, lack of 
health insurance, and language barriers limit access 
to cancer screening and treatment among NH 
blacks and Hispanics.10 It has also been found that 

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Hispanics and NH blacks were less likely to be 
diagnosed with early-stage HCC compared with 
whites.10   
 There is a need to understand how HCC risk 
factors contribute to HCC prevalence rates within 
racial and ethnic groups, to reduce health-care 
disparities. The aim of this study was to determine 
contribution of specific known risk factors for 
HCC by race and ethnicity, using a nationally 
representative US population. A further network 
study of gene specificity and HCC was conducted. 
1. Methods
1.1 Study Population 
 The National Health and Nutrition 
Examination Survey (NHANES) is a biennial 
cross-sectional survey representative of the US 
civilian, non-institutionalized population. Details 
of the NHANES methods and sampling strategy 
have been described by the National Center for 
Health Statistics (NCHS) of the Centers for 
Disease Control and Prevention (CDC).11 Briefly, 
subjects were recruited though a multistage 
probability sampling design, which was used to 
select participants representative of the civilian, 
non-institutionalized US population, with a 
sample weight assigned to each person.12 Next, 
each subject was interviewed and underwent a 
physical examination, including a blood draw. 
General demographic characteristics, including 
age, sex, race/ethnicity (non-Hispanic white, non-
Hispanic Black, Mexican American, other 
Hispanic, other race including Asian descent and 
multiracial (other/mixed)), and smoking behavior 
were collected during the Mobile Examination 
Center interview stage. During the examination, 
body measurements, including height, weight, and 
waist circumference (cm) were also collected. 
Serum samples were obtained and analyzed 
for albumin (g/dL), alanine aminotransferase 
(ALT, U/L), aspartate (aminotransferase (AST, 

U/L), alkaline phosphatase (U/L), fasting glucose 
(mg/dL), fasting insulin (uU/mL),  gamma-
glutamyl transpeptidase (GGT, U/L), platelet 
count (1000 cells/µL), total bilirubin (mg/dL), 
hemoglobin A1C (%), total cholesterol (mg/dL), 
high density lipoprotein (HDL), low density 
lipoprotein (LDL) cholesterol (mg/dL), and 
triglycerides (mg/dL).  All participants 
provided informed consent. NHANES is 
approved by the Institutional Review Board of the 
CDC. Subjects who were less than 18 years old or
pregnant were excluded from this analysis (Figure
1).

Figure 1. Exclusion Criteria 

The prevalence of HCC risk factors was 
determined using interview, physical exam, and/or 
laboratory NHANES data. Hepatitis C virus was 
defined as having a positive hepatitis C virus 
antibody (anti-HCV) in laboratory testing.  
Hepatitis B viral infection was defined as having a 
positive surface antigen (HBsAg) on laboratory 
testing. Persons were identified as smokers when 
they reported current smoking on the NHANES 
questionnaire. Men who reported consuming more 
than 14 drinks per week and women who reported 
more than 7 drinks per week were defined as 
excessive drinkers.13   
 Of the metabolic risk factors, obesity was 
defined by BMI greater than or equal to 30 from 
body measurements taken on physical exam. 
Metabolic syndrome was defined using the 

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International Diabetes Federation definition.14 
Persons with any three of the following five criteria 
were defined as having metabolic syndrome: 1) 
Elevated waist circumference (men >102cm, 
women >88cm) which was measured during 
physical exam; 2) Elevated triglycerides (>= 150 
mg/dL  or currently taking prescription to lower 
lipids); 3) Reduced high-density lipoprotein 
(<40mg/dL for males or <50mg/dl for females); 4) 
Hypertension (blood pressure measurements 
greater than 140mg/dL for systolic blood pressure 
or greater than 90 for diastolic); or 5) Elevated 
fasting glucose (!100mg/dL). Diabetes was 
defined as having answered yes to the 
questionnaire question of “Have you ever been 
diagnosed by a physician as having diabetes?” or 
“Are you currently taking a blood glucose lowering 
medication?”, and/or having a fasting glucose level 
greater than 126 mg/dL, or having a hemoglobin 
A1C level greater than 6.5%.   
 Cirrhosis was defined as having an AST-to-
platelet ratio index (APRI) >2 and any one of the 
following abnormal liver function tests: 1) elevated 

ALT levels (>40 U/L for men or >30 U/L for 
women); 2) Elevated alkaline phosphatase (>113 
U/L); or 3) elevated total bilirubin (>1.3 mg/dL). 
NAFLD-advanced fibrosis (NAFLD-fib) was 
defined using three different noninvasive formulas: 
hepatic steatosis index (HSI), the FIB-4 index 
(FIB4), and the NAFLD fibrosis score (NFS).15,16 
Persons who had fatty liver based on the HSI and 
had fibrosis based on the FIB4 and/or the NFS 
were defined as having NAFLD-advanced 
fibrosis.   

𝑯𝑯𝑯𝑯𝑯𝑯
= 8 × '𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴,
+ 𝐵𝐵𝐵𝐵𝐵𝐵		[+2	𝑖𝑖𝑖𝑖	𝐷𝐷𝑖𝑖𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷, +2	𝑖𝑖𝑖𝑖	𝐹𝐹𝐷𝐷𝐹𝐹𝐷𝐷𝐹𝐹𝐷𝐷]	

𝑭𝑭𝑯𝑯𝑭𝑭𝑭𝑭 =
𝐴𝐴𝐴𝐴𝐷𝐷	!"#$% 	× 𝐴𝐴𝐴𝐴𝐴𝐴	(𝑈𝑈𝐴𝐴)

𝑃𝑃𝐹𝐹𝐷𝐷𝐷𝐷𝐷𝐷𝐹𝐹𝐷𝐷𝐷𝐷	𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐷𝐷	 '10
&

𝐴𝐴 , ×	O𝐴𝐴𝐴𝐴𝐴𝐴	(𝑈𝑈𝐴𝐴)

𝑵𝑵𝑵𝑵𝑭𝑭𝑵𝑵𝑵𝑵	𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭	𝑯𝑯𝑺𝑺𝑭𝑭𝑭𝑭𝑺𝑺 = 
		 −1.675 + 0.037	(𝐴𝐴𝐴𝐴𝐷𝐷!"#$) + 0.094	(𝐵𝐵𝐵𝐵𝐵𝐵) + 1.13(𝐹𝐹𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖𝐶𝐶𝐴𝐴	𝐺𝐺𝐹𝐹𝐶𝐶𝐺𝐺𝐶𝐶𝐷𝐷𝐷𝐷)
(𝐷𝐷𝑖𝑖𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷	(𝑦𝑦𝐷𝐷𝐷𝐷 = 1, 𝐶𝐶𝐶𝐶 = 0)) + 0.99 f𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴g − 0.013 '𝑃𝑃𝐹𝐹𝐷𝐷𝐷𝐷𝐷𝐷𝐹𝐹𝐷𝐷𝐷𝐷	𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐷𝐷(10

&
𝐴𝐴 , − 0.66(𝐴𝐴𝐹𝐹𝐷𝐷𝐶𝐶𝐹𝐹𝑖𝑖𝐶𝐶 f𝐴𝐴𝐴𝐴g)

 NHANES does not include genetic disorders 
which are risk factors for HCC or impaired glucose 
tolerance. Thus, these risk factors could not be 
included in our analysis. 
1.2 Statistical Analysis.
 This analysis used the required weighting 
procedures to account for the survey design of 
NHANES. Descriptive analyses were done to 
compare the NHANES population in 1999-2002 
to the NHANES population in 2009-2012. 
Categorical variables were compared using χ2 tests.
Continuous variables were compared using the 
Students t-test after confirming all data were 

normally distributed. Age-adjusted prevalence 
rates for HCV infection, HBV infection, smoking, 
excessive alcohol use, obesity, diabetes, NASH-
cirrhosis, and NAFLD-fib for the two four-year 
time periods were calculated using the projected 
population of the United States for the year 2000.17  
 To determine the predicted contribution of 
each risk factor towards the development of HCC, 
the population attributable risk was calculated. A 
medical literature review was done to find the 
relative risk (RR) of each risk factor (HCV, HBV, 
etc.) towards the development of HCC. The 
literature review was done using PubMed and with 
the term for each risk factor, risk, and 
hepatocellular carcinoma. 

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 Results were sorted by year of publication with 
US populations, and recent meta or pooled 
analyses preferred. The population attributable 
fraction (PAF) for each risk factor was then 
calculated using the formula developed by Levin 
for each sex and race group.18 

𝑃𝑃𝐶𝐶 𝐶𝐶𝐹𝐹𝐷𝐷𝐷𝐷𝑖𝑖𝐶𝐶𝐶𝐶	𝐴𝐴𝐷𝐷𝐷𝐷 𝑖𝑖𝐷𝐷𝐶𝐶𝐷𝐷𝐷𝐷𝐷𝐷𝐹𝐹𝐷𝐷	𝐹𝐹 𝐷𝐷𝐺𝐺𝐷𝐷𝑖𝑖𝐶𝐶𝐶𝐶
= 	 𝑖𝑖𝐷𝐷 	𝑃𝑃 𝐷𝐷 𝐷𝐷𝐹𝐹𝐷𝐷𝐶𝐶𝐺𝐺𝐷𝐷	(	 % $" − 1)

𝑖𝑖𝐷𝐷 	𝑃𝑃 𝐷𝐷 𝐷𝐷𝐹𝐹𝐷𝐷𝐶𝐶𝐺𝐺𝐷𝐷	 % $" − 1 + 1

 In determining the PAF, the risk factors were 
analyzed independently, without accounting for 
interaction between their effects.  

 The combined effect of all risk factors and risk 
factors by type (metabolic vs. non-metabolic) were 
calculated using the formula below:  

𝐴𝐴𝐶𝐶𝐷𝐷𝐷𝐷𝐹𝐹	𝐴𝐴𝐷𝐷𝐷𝐷 𝑖𝑖𝐷𝐷𝐶𝐶𝐷𝐷𝐷𝐷𝐷𝐷𝐹𝐹𝐷𝐷	𝐹𝐹 𝐷𝐷𝐺𝐺𝐷𝐷𝑖𝑖𝐶𝐶𝐶𝐶

= 1 − (1 − 𝑃𝑃𝐴𝐴𝐹𝐹 )

 All statistical analyses were conducted using 
SAS 9.4 (Cary, NC) with p <0.05 considered 
significant. Figures were developed using 
GraphPad Prism version 8.0.0 for Windows, 
GraphPad Software, San Diego, California USA. 

 The top enriched pathogenic genes associated 
with liver diseases were analyzed using the 
DisGenet platform (Table 1).19 The DisGeNet 
database uses information of human gene-disease 
association (GDAs) and variant-disease 
association (VDAs) from expert curated 
repositories. The GDA score was calculated using 
the formula developed by DisGeNet. Scoring 
(gda) was used to rank the gene-disease according 
to their level of evidence.19 

 The DisGeNET Score (S) for GDAs is 
computed according to: 

where: 
Nsourcesi is the number of CURATED 
sources supporting a GDA 
i  CGI, CLINGEN, GENOMICS
ENGLAND, CTD, PSYGENET, 
ORPHANET, UNIPROT 

where: 
j  Rat, Mouse from RGD, MGD, and
CTD 

where: 
k  HPO, CLINVAR, GWASCAT,

GWASDB 

where: 
Npubs is the number of publications 
supporting a GDA in the sources 
LHGDN and BEFREE 

 DisGeNET uses two other metrics to facilitate 
the ranking of the genes associated with 
hepatocellular carcinoma. The Disease Specificity 
Index (DSI) was used, which is inversely 
proportional to the number of diseases associated 
to gene. A gene associated with multiple diseases 

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gets a score close to zero, and a gene associated 
with only one disease has DSI of 1.18 It is computed 
according to :  

where: 
N d Is the number of diseases associated to 
the gene/variant  
N T is the total number of diseases in 

DisGeNET 

 The Disease Pleiotropy Index (DPI) was the 
second metric used to rank the genes. It ranges 
from 0 to 1 and is proportional to the number of 
different (MeSH) disease classes a gene is 
associated with. The DPI is computed according 
to : 

where: 
N dc is the number of the different MeSH 
disease classes of the diseases associated to 
the gene/variant 
N TC is the total number of MeSH diseases 
classes in DisGeNET. 

 Furthermore, cytoscape network was used to 
form a gene-disease association to visualize 
interaction among different genes (Figure 2).  
Cytoscape is an open-source platform for 
visualizing molecular interactions.20 
1. Results

There were 10,945 individuals in the 1999-
2002 sample and 12,305 in the 2009-2012 sample 
(Figure 1).  The groups did not differ among the 
distribution of sex, age, or race/ethnicity.  The 
mean age at screening was 45.4 (1999-2002) and 
46.4 (2009-2012) and 50.7% of the participants 
were female (Table 2).  

 In the overall NHANES population, obesity, 
excessive alcohol consumption, and smoking were 
the most prevalent HCC risk factors in both 1999-
2002 and 2009-2012. The prevalence of HBV, 
HCV, or excessive alcohol consumption did not 
change over the ten-year period. All metabolic risk 

factors (obesity, diabetes, NAFLD-fib, NASH-
cirrhosis) increased over the ten years of study in 
the overall population (Table 2).  Concurrently, 
BMI, waist circumference, fasting glucose levels, 
and triglyceride levels all increased between the 
two time periods (all p < 0.001, Supplemental 
Table 1). 

24



 Both NASH-cirrhosis and NAFLD-fib 
increased in prevalence between 1999-2002 and
������������5IF�QSFWBMFODF�SBUFT�PG�/"'-%�GJC

 increased from 1999-2002 (1.53%) to 
2009-2012 (4.0%) (p<0.001), while NASH-
cirrhosis  

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increased from 0.07% to 0.20%, p=0.03). This 
represents a 164% increase for NAFLD-fib and
�����JODSFBTF�GPS�/"4)�DJSSIPTJT�PWFSBMM��5IF

 largest increase of NAFLD-fib occurred 
among the Hispanic population (a 296% 
increase, p<0.001) (Table 3). 

The risk factors to decrease over the ten-year 
period were the number of current smokers 
(prevalence 24.5% to 20.3%, p=<0.001) and access 
alcohol. There were no temporal changes in the 
prevalence of excessive drinking behaviors overall, 
which remained between 23.8% and 21.6% (p = 
0.83, Table 2). 

1.1 Population Attributable Fractions 
 Overall, 77.9% of HCC cases from 1999-2002 
and 76.2% of HCC cases from 2009-2012 could 
be attributed to the risk factors analyzed in this 
study. In the overall population, metabolic risk 
factors (diabetes, obesity, NAFLD-fib, NASH-
cirrhosis) composed 35% of HCC cases in 1999-

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2002 and increased to accounted for 42% of HCC 
cases in 2009-2012.  Synchronously, non-
metabolic risk factors (HBV, HCV, excessive 
alcohol use, current smokers) decreased from a 
PAF of 66% in 1999-2002 to 59% in 2009-2012 
(Table 4). Other/mixed category was removed 
from the study because it was not statistically 

significant enough to make conclusions about the 
PAF due to low sample size.  Among both 
metabolic and non-metabolic risk factors, HCV 
was the single highest attributable cause of HCC 
in both time periods; 54.1% of HCC cases were 
estimated to be attributable to HCV in 1999-2002 
and 48.4% in 2009-2012.   

 Differences in PAF magnitudes were observed 
among race/ethnic subgroups. The highest HCV 
PAF was observed among non-Hispanic Blacks 
(59% in 2009-2012), while the lowest was among 
other/mixed (34% in 2009-2012). These values did 
not significantly decrease over the ten-year period. 
Non-Hispanic Blacks had the highest obesity 
PAF (31% in 2009-2012 compared to 25% in the 
overall�QPQVMBUJPO

�XIJDI�EFDSFBTFE�GSPN�����JO�
�����������

NASH-cirrhosis or NAFLD-fib could be 
attributed to, at most, 8% of HCC cases; these 
liver diseases had the largest fold change of any 
risk factor over the study period. 
Specifically, NAFLD-fib had the greatest fold 
increase of any risk factor studied, increasing 
2.6-fold. NASH-cirrhosis had the second 
greatest PAF, increasing to a similar degree of 
2.4-fold. NASH-cirrhosis 

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increased from 3.8% of cases to 5.8% and 
NAFLD-fib increased from 1.4% to 3.7% 
(p=0.022 and p<0.001, respectively, Figure 3).   

Figure 3: Fold Change in Hepatocellular 
Carcinoma Risk Factor Population Attributablr 
Fractions 1999-2002 and 2009-2012 

 Stratification by race/ethnicity showed that 
obesity and NAFLD-fib increased across all 
groups. The largest increase was seen among 
Hispanics with a 208% increase in NAFLD-fib. 
NASH-cirrhosis was more common among men 
than women in 2009-2012 (relative risk = 6.2). 
The largest increase in NASH-cirrhosis was 
among non-Hispanic Blacks, increasing from PAF 
of 0% in 1999-2001 to 8% in 2009-2012. NASH-
cirrhosis increased significantly, accounting for 
6.3% of HCC cases in 2009-2012 (Table 3, Figure 
3). 

1.1.1 Genetic Analysis. 
 There are differences in HCC outcomes 
between different ethnic groups. The gene 
PNPLA3 was shown to play a major role in the 
development of liver disease such as NAFLD-fib 
and NASH-cirrhosis. PNPLA3 represents a GDA 
score of 5.00(figure 2). The specificity for HCC 
was 0.556, and the association of the PNPLA3 
with HCC specifically is 0.692.  This is based on 
an evidence score of 0.500, which was calculated 
from DisGenet . PNPLA3 gene represents a 
cytosine to guanine substitution, resulting in an 

isoleucine to methionine switch at codon 148  and 
individuals  with the G allele have a higher hepatic 
triglyceride level and elevated serum of ALT.21 
Our study found a total of 13 pathogenic genes 
from DisGeNET platfrom with a DSI , DPI and 
a GDA score based on DisGeNet ranking system. 
Our analysis represents high frequency of PNPLA3 
gene among Hispanic groups. Furthermore, gene-
diet interaction plays a vital role in the 
pathogenesis of liver cancer in Hispanics.  
 Figure 2 represents the 13 gene strongly 
associated with HCC. Based on the GDA score 
from DisGeNET, PNPLA3 (0.500) holds a strong 
association with increased risk of HCC. The GDA 
score of PNPLA3 (0.500) is the highest as 
compared to other genes in list. The lowest GDA 
score based on DisGeNET ranking is PPARD. 
Genes LDLR, FAS, PEMT, NR1H4, GNMT 
represents the same GDA score of 0.320 which 
means they all are equally associated in the 
development of HCC. 
1. Discussion

This study attributed nearly 80% of HCC cases
in 1999-2001 and 2009-2012 to eight known risk 
factors in a large nationally representative sample 
of the U.S. population. Metabolic risk factors are 
now contributing to nearly half of HCC cases in 
the US. Metabolic diseases (diabetes, obesity, 
NASH-cirrhosis, and NAFLD-advanced fibrosis) 
increased from contributing an estimated 35% of 
HCC burden in 1999-2002 to 42% in 2009-2012. 
Concordantly, non-metabolic risk factors 
decreased from 66% to 59% of total HCC burden 
over the same period. Stratification by 
race/ethnicity showed a similar shift across all 
groups. The HCC risk factor prevalence rates of 
obesity and NAFLD-fib increased for all groups 
and were particularly high among Hispanics and 
non-Hispanic Blacks. A genetic variant in 
PNPLA3 was identified as strongly associated with 
HCC. 

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 These findings highlight vulnerabilities within 
certain racial and ethnic groups within one 
country’s population of HCC, which is increasing 
in the US as well as worldwide. 25-28 As the obesity 
epidemic had increased from 1999 until 2012, 
metabolic risk factors (NAFLD-fib and NASH-
cirrhosis) became contributers to HCC 
development in the US more so than non-
metabolic risk factors (HBV, HCV), excessive 
alcohol use, and smoking. 22,23 
 It is likely that there is an interplay between 
lack of access to health care, racial disparities, and 
genetics leading the whole pathogenesis and 
playing a major role in the development of liver 
disease that eventually forms into HCC. Specific 
genetic contributions may help explain differences 
observed in PAFs for HCC between race/ethnic 
groups.29 While insulin resistance likely plays a role 
in its pathogenesis, oxidative injury and 
inflammatory reactions could be influenced by 
genetics. A study performed in the US among 
Hispanic, NH black and NH white individuals 
identified the variant, rs73809, (148M) in patatin-
like phospholipase domain-containing protein 3 
(PNPLA3) as a predictor for hepatic fat content.30 
The study confirmed that patients with NAFLD-
fib who carry an allele of the gene (rs73809) 
PNPLA3 have an increased risk of developing 
advanced diseases, including NASH-cirrhosis. 
Risk allele (rs73809) was the main common 
genetic determinant of hepatic fat content and of 
progressive NAFLD-fib, and this allele was mostly 
observed among Hispanic groups. It is not clear as 
to why this allele is increased among Hispanics. 
The variant has been reported to manifest in early 
life among Hispanic adolescents, 31 as well was 
having a prevalence of 80% in a single center study 
in Mexico.32 This is consistent with the current 
study’s findings showing higher burdens of 
NAFLD-fib among Hispanics. Overall, evolving 
knowledge in genetics along with epidemiological 
studies focused on race/ethnic backgrounds may 

help identify patients at higher risks for HCC.33 
This study demonstrates that PNPLA3 influences 
liver fat accumulation early in life in Hispanic 
children and adults. 34 This analysis also represents, 
individuals carrying the GG genotype of the 
PNPLA3 gene are susceptible to increased hepatic 
fat when dietary sugar intake is high. The role of 
PNPLA gene may have an association in the 
development of NAFLD-fib and NASH-cirrhosis 
in the Hispanics. Other studies have also 
confirmed that this gene predispose obese children 
and adolescents to exhibit hepatic damage.35 
 Among non-metabolic risk factors, HCV has 
long been recognized as a major predictor of HCC 
risk.36 This study is also consistent with previous 
studies indicating a largest risk of HCV in the 
non-Hispanic Black population. 36-37 A cross-
sectional study utilizing Medicare databases have 
shown that the proportion of HCC cases 
attributable to HCV and HBV have doubled over 
approximately the same study period.37,38 The 
differing estimations in previous studies compared 
to the current is likely due to the broader age of 
subjects surveyed presently in NHANES 
compared to SEER-Medicare databases. The 
known birth cohort effect of those born between 
1945-1965 reaching the age of peak HCC risk had 
been previously reported, 37,39 and the greater 
proportion of HCV contribution was towards 
HCC among the older US generation.  
 The current study does have certain 
limitations. While this study has assumed 
independent causation of HCC for each risk 
factor, the course of disease from obesity, diabetes, 
NAFLD-fib, and NASH-cirrhosis is not a 
mutually exclusive path toward malignancy. 25,26,40-

43 Patients can often present with multiple risk 
factors, raising the question of how to accurately 
weigh the contribution of each risk and handle 
overlapping interactions with other, possibly 
concurrent, risk factors. While this study served to 
focus on contributions of each specific risk factor, 

29



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https://doi.org/10.48091/LKML8578 

future studies are needed to disentangle these 
interactions using real-world data .44 Importantly, 
the average age of HCC onset is 65 while the 
study’s cohort had an average age of 46. While this 
study did adjust for age, the burden of each 
metabolic risk factor may have been an 
underestimation.16 This study also assumes that 
people do not change their lifestyle habits as they 
grow older, thereby potentially decreasing their 
HCC risk. 29 Thus, these results should be 
interpreted as epidemiological evidence for 
prevention strategies and public health education 
on risk factors.  
 In conclusion, these results display 
the changing contributions as well as the 
proportions of known HCC risk factors among 
specific racial and ethnic groups in a 
representative sample of the US population. The 
results of this study show that the increasing 
HCC rates are due to modifiable causes; this 
can be used to inform prevention and education 
programs with awareness as to racial and ethnic 
genetic and lifestyle differences. 
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