GRAPHICAL ABSTRACT

INTRODUCTION

Metabolic dysfunction-associated steatotic liver disease (MASLD), previously known as non-alcoholic fatty liver disease, has become a major global concern as its prevalence continues to rise^1,2. With this growth, MASLD is now one of the leading causes of liver-related outcomes, including hepatocellular carcinoma (HCC)^3,4. MASLD is defined by hepatic steatosis in the setting of metabolic dysfunction, such as obesity, diabetes, hypertension, and dyslipidemia⁵. Its presence and severity reflect multiple, interacting risk factors, including insulin resistance, obstructive sleep apnea, sarcopenia, disturbances of the gut-liver axis, and genetic variants^6,7.

The I148M variant in patatin-like phospholipase domain-containing protein 3 (PNPLA3) is the best-established genetic determinant of MASLD development and progression^8,9. Mechanistically, the I148M substitution reduces PNPLA3’s catalytic activity at the lipid-droplet surface, limiting triglyceride hydrolysis/mobilization and promoting lipid-droplet accumulation, changes that collectively accelerate disease progression^10-12. Beyond steatosis, carriers of the GG genotype have a higher risk of fibrosis progression and liver-related events in longitudinal cohorts^13-15. Taken together, these mechanistic and clinical data highlight PNPLA3 I148M as a major contributor to inter-individual variability in MASLD trajectory and prognosis, raising the question of whether its influence also extends to hepatocarcinogenesis.

Evidence on HCC risk in relation to PNPLA3 is heterogeneous across ethnicities and liver disease etiologies. A 2015 meta- analysis reported a higher HCC risk among carriers of the G allele in alcohol-related cirrhosis¹⁶. More recently, a longitudinal cohort showed that the GG genotype was associated with a higher cumulative incidence of HCC in compensated cirrhosis, suggesting a role for PNPLA3 in hepatocarcinogenesis in that setting¹⁷. By contrast, an Asian study of patients with chronic hepatitis B virus (HBV) infection found no significant association between PNPLA3 genotype and HCC occurrence^18, and a recent study from South America likewise reported null results^19, underscoring the context- and population-dependence of this relationship.

Based on these considerations and the limited East Asian data, we aimed to test whether the PNPLA3 I148M genotype is associated with HCC among Korean patients with MASLD, and whether any association differs by age, sex, obesity, or fibrosis burden. We also sought to evaluate the relationship between PNPLA3 genotype and tumor differentiation as reflected by Edmondson-Steiner grade.

MATERIALS AND METHODS

Patients

We prospectively enrolled adults with MASLD from two university-affiliated hospitals in Korea between June 2024 and June 2025. This analysis represents a cross-sectional comparison between patients with prevalent HCC and those without HCC at the time of enrollment within this prospective registry. All participants underwent PNPLA3 genotyping. Inclusion criteria were 1) age ≥18 years, and 2) availability of imaging sufficient to determine HCC status. For the present analysis, we conducted a cross-sectional comparison between patients with prevalent HCC and those without HCC at the time of enrollment within this prospective registry. The study protocol was approved by the Institutional Review Board of the Catholic University of Korea (XC24TIDI0025) and conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants before enrollment.

Genomic data collection and single nucleotide polymorphism genotyping

Genomic DNA was isolated from buccal swab, whole-blood, or tissue specimens using the XENOPURETM gDNA Purification Kit (Xenohelix, Incheon, Korea). Single-nucleotide polymorphism (SNP) genotyping was conducted with the XENOSNP assay (Xenohelix) according to the supplier’s instructions. For each specimen, to 10 ng of DNA was used as template in a 20-μL reaction set up in 96-well format, and polymerase chain reaction (PCR) amplification with allele discrimination was performed on a CFX Connect Real-time PCR System (Bio-Rad, Hercules, CA, USA).

Assessment of liver fibrosis

Liver fibrosis was assessed using vibration-controlled transient elastography (VCTE) and the fibrosis-4 (FIB-4) index. For VCTE, advanced fibrosis was defined as a liver stiffness measurement (LSM) ≥15 kPa, consistent with the Baveno VII threshold for compensated advanced chronic liver disease²⁰. For FIB-4, a score ≥2.67 was used to indicate advanced fibrosis, in line with major clinical guidelines on noninvasive fibrosis assessment^21,22.

Clinical and histological data collection

Collected variables included demographics (age, sex, body mass index [BMI]), laboratory results (lipid profile and liver-related markers), and liver stiffness by VCTE. Obesity was defined as BMI ≥25 kg/m². For patients with HCC who underwent percutaneous core-needle liver biopsy in routine practice, histology was reviewed. Tumor differentiation was assessed by an experienced hepatobiliary pathologist using the Edmondson-Steiner grading system²³. Edmonson-Steiner grade of 3 or 4 were classified as high grade and grades 1-2 as low grade.

Statistical analysis

Continuous variables were summarized as mean±standard deviation (or median with interquartile range, as appropriate) and compared using unpaired Student’s t-tests or Mann-Whitney U tests. Categorical variables were reported as counts (percentages) and compared using chi-square tests or Fisher’s exact tests. The association between PNPLA3 I148M genotype and HCC presence was evaluated with logistic regression, with results expressed as odds ratios (ORs) and 95% confidence intervals (CIs); multivariable models adjusted for prespecified clinical covariates. To minimize confounding between the HCC and non-HCC groups, inverse probability of treatment weighting (IPTW) based on propensity scores was applied using a logistic regression model including age and sex as covariates. Two-sided P-values <0.05 were considered statistically significant. All analyses were performed in R statistical software (version 4.4.3; R Foundation, Vienna, Austria; http://cran.r-project.org).

RESULTS

Baseline characteristics of the study population

A total of 562 MASLD patients were included for the study analysis (Supplementary Fig. 1). Among the study population, 88 patients had HCC at the time of enrollment while 474 patients had not. The HCC group included more men (80.7% vs. 54.4%) and was older (mean age, 70.1 vs. 52.8 years; P<0.001) (Table 1). Noninvasive fibrosis indices were higher in the HCC group, including aspartate aminotransferase to platelet ratio index (APRI), FIB-4, and LSM. Overall, 25.3% had cirrhosis, with a greater proportion in the HCC group than in the non-HCC group (58.0% vs. 19.2%).

Distribution of PNPLA3 genotype according to HCC status

PNPLA3 genotypes were compared between the non-HCC (n=474) and HCC (n=88) groups (Fig. 1). In the non-HCC group, the GG genotype was most common (39.9%), followed by GC (37.1%) and CC (23.0%). In the HCC group, GG likewise predominated (45.5%), followed by GC (36.4%) and CC (18.2%). Overall, the genotype distribution did not differ significantly between groups (P=0.509).

Distribution of PNPLA3 genotypes by the presence of liver cirrhosis

Further analyses were performed according to the presence or absence of cirrhosis (Fig. 2). Among patients without cirrhosis (n=420), the proportions of GG, GC, and CC genotypes in the non-HCC group (n=383) were 37.1%, 39.7%, and 23.2%, respectively, whereas the corresponding proportions in the HCC group (n=37) were 37.8%, 40.5%, and 21.6%, with no significant difference in the overall distribution between groups (P=0.976) (Fig. 2A). Among patients with cirrhosis (n=142), genotype proportions in the non-HCC group (n=91) were 51.6% (GG), 26.4% (GC), and 22.0% (CC), compared with 51.0%, 33.3%, and 15.7% in the HCC group (n=51), again showing no significant difference (Fig. 2B). Taken together, stratification by cirrhosis status did not reveal differences in PNPLA3 genotype distribution according to HCC.

PNPLA3 genotype distribution by HCC status stratified by fibrotic burden

The distribution of PNPLA3 genotypes between the HCC and non-HCC groups was evaluated across strata of fibrotic burden (Fig. 3). First, fibrosis was classified by VCTE using an LSM threshold of 15 kPa (Fig. 3A, B). Among patients with LSM <15 kPa, the proportions of the GG genotype were 37.6% in non-HCC and 40.0% in HCC, with no significant difference between groups (P=0.618). Among those with LSM ≥15 kPa, the GG proportion was higher in the HCC group than in the non-HCC group (66.7% vs. 47.0%), but this difference was not statistically significant (P=0.296).

Next, fibrosis was assessed with the FIB-4 index using a cutoff of 2.67 (Fig. 3C, D). In patients with FIB-4 <2.67, the GG proportions were 35.3% in non-HCC and 29.0% in HCC (P=0.590). In patients with FIB-4 ≥2.67, the GG proportions were 52.3% in non-HCC and 54.4% in HCC (P=0.242). Taken together, stratification by fibrotic burden using either VCTE or FIB-4 did not reveal significant differences in PNPLA3 genotype distribution according to HCC status.

Association between PNPLA3 genotype and hepatocellular carcinoma occurrence

To evaluate the association between PNPLA3 genotype and HCC development, we performed logistic regression analyses (Table 2). In the unadjusted model, neither the GG genotype (OR, 1.05; P=0.249) nor the GC genotype (OR, 1.03; P=0.531), each compared with the CC reference, showed a significant association with HCC. Three multivariable models were subsequently tested: model 1 adjusted for age, sex, BMI, alanine aminotransferase, and diabetes, model 2 additionally adjusted for cirrhosis, and model 3 incorporated the FIB-4 score along with the covariates in model 1. In model 1, the GG (OR, 1.03; P=0.324) and GC (OR, 1.03; P=0.324) genotypes remained non-significant. The results were consistent in model 2 (OR, 1.03; P=0.396 for GG vs. OR, 1.02; P=0.556 for GC) and model 3 (OR, 1.03; P=0.459 for GG vs. OR, 1.02; P=0.607 for GC). Collapsing genotypes to compare GG versus GC/CC yielded similar findings in the unadjusted analysis (OR, 1.03; P=0.329) and after adjustment (OR, 1.02; P=0.384 for model 1 vs. OR, 1.02; P=0.537 for model 2 vs. OR, 1.01; P=0.592 for model 3).

Furthermore, subgroup analysis with age, sex, obesity, and cirrhosis as categorization variables was carried out to assess the association between the GG genotype and HCC occurrence across diverse subgroups (Fig. 4). The GG genotype, in comparison with the GC/CC genotypes, did not show a significant association irrespective of subgroup (age ≥65, age <65, male, female, obese, non-obese, cirrhosis, non-cirrhosis). In the same context, no interactions between these variables and HCC occurrence were detected, with P-values for interaction >0.05 for all variables.

PNPLA3 genotype distribution after IPTW adjustment

To minimize baseline differences in age and sex distribution between HCC and non-HCC groups, IPTW was applied. After weighting, age and sex distributions were well balanced between the two groups (Supplementary Table 1). Using the IPTW-adjusted cohort, the distribution of PNPLA3 genotypes according to HCC presence was compared (Supplementary Fig. 2). The distribution of PNPLA3 genotypes was comparable between the two groups in the IPTW cohort, with no statistically significant difference observed (P=0.596) (Supplementary Fig. 2A). Similarly, grouping genotypes as GG versus GC+CC yielded no significant difference between HCC and non-HCC groups (GG, 39.4% and GC+CC, 60.6% for non-HCC vs. GG, 49.7% and GC+CC 50.3% for HCC; P=0.488) (Supplementary Fig. 2B).

Association between PNPLA3 genotype and histologic tumor differentiation

For patients with HCC who had available histologic data (n=47), the association between PNPLA3 genotype and tumor differentiation was assessed (Fig. 5). Among patients with the GG genotype, high-grade differentiation (Edmondson-Steiner grade 3-4) predominated, comprising 57.1% of cases. In contrast, patients with GC/CC genotypes had a higher proportion of low-grade tumors (Edmondson-Steiner grade 1-2) than high-grade tumors (81.8% vs. 18.2%), and the distribution of tumor differentiation differed significantly compared with the GG group (P=0.008). In a multivariable logistic regression model adjusting for age, sex, and cirrhosis, the PNPLA3 GG genotype remained independently associated with high-grade differentiation (adjusted OR, 1.36; 95% CI, 1.03-1.80).

DISCUSSION

In this study, the HCC occurrence according to the PNPLA3 I148M GG genotype was assessed in patients with MASLD. As a result, GG carriage was found to be not associated with HCC occurrence, regardless of the presence or absence of liver cirrhosis. Furthermore, this lack of association was consistently shown regardless of age group, sex, BMI status, and fibrotic burden. The insignificance of the association stayed consistent after adjusting for age, sex, and liver cirrhosis, consolidating the results derived from the study. To our knowledge, this study is the first to evaluate the association between PNPLA3 genotype and HCC occurrence in a Korean population with MASLD.

Prior studies examining PNPLA3 and hepatocarcinogenesis have produced heterogeneous results across etiologies, ancestries, and study designs. Associations between the G allele and increased HCC occurrence have been most consistently reported in alcohol-related cirrhosis and, to a lesser extent, in cohorts enriched for advanced metabolic liver disease, particularly when fibrosis is substantial and surveillance is standardized^24,25. By contrast, studies in chronic HBV, especially in East Asian populations, often show null or attenuated associations, suggesting that virally driven pathways to HCC may dominate over lipid-droplet-centric mechanisms^18,26. Differences in case definition (incident vs. prevalent HCC), analytic approach, and confounder control further complicate comparisons. Beyond incidence, several studies suggest that PNPLA3 is associated with tumor behavior, including differentiation grade and molecular profile^27,28. This suggests that PNPLA3 may shape tumor behavior rather than the earliest steps of carcinogenesis. Mechanistically, the I148M change can impair triglyceride handling on lipid droplets, leading to fat build-up and cellular stress in hepatocytes. These changes have been proposed to heighten inflammation and activate fibrogenic cells, creating a milieu that may permit tumor progression^29,30. That said, human data are mixed and likely context-dependent, varying by liver disease setting and genetic background. These considerations motivate testing the association within well-defined populations and etiologies.

Within this landscape, our study adds population- and etiology-specific nuance. In a Korean MASLD cohort, we did not observe a significant association between the GG genotype and the presence of HCC after accounting for demographic factors, adiposity, and fibrosis burden. This aligns with reports from East Asian settings in which viral or mixed etiologies dilute genotype effects, but it contrasts with findings from alcohol-related cirrhosis or ethnically diverse MASLD cohorts where PNPLA3 has tracked more closely with HCC occurrence^18,24,31. Importantly, among patients with histologically graded tumors, the GG genotype was associated with poorer differentiation by the Edmondson-Steiner system, aligning with the previous reports on poorer differentiation for the GG genotype carriers²⁸. Together with our null incidence result, this supports a cautious model in which PNPLA3 influences progression pathways more than the initial trigger for HCC. In addition, we found no evidence that age, sex, obesity, or fibrosis modified the association between PNPLA3 and HCC, indicating similar null findings across subgroups. Differences in risk-allele frequency may partly explain divergent findings on the PNPLA3-HCC relationship. In East Asians, the PNPLA3 risk allele is common, reducing genotype contrast between cases and controls and attenuating detectable signals for HCC incidence³². In this context, co-exposures and modifier variants (e.g., TM6SF2, HSD17B13) may shift the observed association from occurrence to tumor behavior. Nevertheless, our results do not support an association with HCC occurrence in Korean MASLD, although we cannot exclude a small, context-dependent effect on incidence.

This study has limitations. The cross-sectional, single-center design may limit the generalizability and also precludes causality analysis. Although our total sample was sizable, the number of HCC cases with histologic grading was relatively small, limiting precision for subgroup analyses and increasing the risk of type II error. Residual confounding remains possible, particularly for alcohol exposure patterns, diet, physical activity, or unmeasured environmental factors. We did not assess additional germline variants (e.g., TM6SF2, MBOAT7, HSD17B13) or polygenic risk scores, which could clarify whether PNPLA3 tracks with specific molecular subclasses. Although the study was designed prospective and recruited patients consecutively from the study initiation, potential for selection bias cannot be neglected resulting in biased results. Finally, our findings may not generalize beyond Korean MASLD populations or to non-tertiary settings which warrants external validation in other institutions in Korea and other part of the world.

In summary, the present study did not demonstrate a significant association between the PNPLA3 I148M GG genotype and HCC occurrence in Korean patients with MASLD after adjustment for clinical covariates. In contrast, its association with poorer tumor differentiation supports a model in which PNPLA3 relates more to tumor characteristics than to initiation in this population. Our results suggest potential heterogeneity in PNPLA3 effects by ethnicity, underscoring the need for caution when interpreting genotype studies from other populations. Future multicenter cohorts in East Asia that include germline, tumor, metabolic, and microenvironment measures are required to link MASLD with HCC and to determine whether PNPLA3 can meaningfully guide risk stratification.

Article information

Conflicts of Interests

Pil Soo Sung is an editorial board member of Journal of Liver Cancer and was not involved in the review process of this article. Otherwise, the authors have no conflicts of interests to declare.

Ethics Statement

This study was approved by the Institutional Review Board of the Catholic University of Korea (approval No. XC24TIDI-0025). Informed consent was obtained from all participants prior to the study enrolment.

Funding Statement

This work was supported by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) (RS-2024-00438542 to SHB). This work was also supported by the Research Supporting Program of the Korean Association for the Study of the Liver (RS-2024-00337298 to PSS). Additional support was provided in part by the NRF grant funded by the Korean government (MSIT) (RS-2023-00208767 to SHB) and (RS-2024-00337298 to PSS). This study was also supported by the Internal Research Fund of the Korean Liver Cancer Association (2025). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data Availability

All data supporting the findings of this study are included in the article. Further inquiries can be directed at the corresponding author.

Author Contributions

Conceptualizatio: JL, JHC, HY, SKC, SWY, SHB, PSS

Data curation: JL, DYL, JHC, HSC, KY, HY, MYB, SKC, SWY, SHB, PSS

Formal analysis: JL, DYL, JHC, MYB, SWY

Funding acquisition: SHB, PSS

Investigation: MYB, SKC, SWY, PSS

Methodology: JHC, PSS

Resources: HY, SWY, SHB, PSS

Supervision: SWY, SHB, PSS

Validation: JL, DYL, HY, MYB, SKC, SWY, SHB, PSS

Visualization: JL, JHC, HSC,

Writing - original draft: JL

Writing - review & editing: PSS

Supplementary Materials

Figure 1.

Distribution of PNPLA3 I148M genotypes according to HCC status. Numbers within each bar indicate the percentage of patients in each genotype category. ns, not significant; HCC, hepatocellular carcinoma.

Figure 2.

Distribution of PNPLA3 I148M genotypes by HCC status, stratified by presence of cirrhosis. (A) Patients without cirrhosis. (B) Patients with cirrhosis. Numbers within each bar indicate the percentage of patients in each genotype category. ns, not significant; HCC, hepatocellular carcinoma.

Figure 3.

Distribution of PNPLA3 I148M genotypes by HCC status, stratified by fibrosis status. (A, B) Liver fibrosis assessed by vibrationcontrolled transient elastography. (A) Patients with LSM <15 kPa. (B) Patients with LSM ≥15 kPa. (C, D) Liver fibrosis assessed by FIB-4 index. (C) Patients with FIB-4 <2.67. (D) Patients with FIB-4 >2.67. Numbers within each bar indicate the percentage of patients in each genotype category. LSM, liver stiffness measurement; ns, not significant; HCC, hepatocellular carcinoma; FIB-4, fibrosis-4.

Figure 4.

Subgroup analysis of hepatocellular carcinoma (HCC) risk associated with the PNPLA3 GG genotype. Forest plot shows hazard ratios (HRs) and 95% confidence intervals (CIs) for HCC development across predefined subgroups. P-values for interaction indicate the absence of significant effect modification across all subgroups.

Figure 5.

Tumor differentiation grade according to PNPLA3 I148M genotype. Tumor differentiation was assessed using the Edmonson-Steiner (ES) grading system. Numbers within each bar indicate the percentage of patients in each genotype category. **P<0.01.

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Figure & Data

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