Abstract
Background
The incidence of metabolic dysfunction-associated steatohepatitis related hepatocellular carcinoma is increasing. However, its response to immunotherapy remains unclear. This study aimed to compare the prognosis of patients with MASH-HCC and hepatitis B virus-related HCC (HBV-HCC) under different treatment strategies.
Methods
This single-center retrospective study included 218 patients with HCC from 2022 to 2025. The primary endpoint was progression-free survival (PFS), and the secondary endpoint was 1-year overall survival (OS). Kaplan–Meier analysis, Cox regression models, and restricted cubic spline (RCS) analysis were used to evaluate the association between immune cells and prognosis. Stratified and subgroup analyses were also performed.
Results
A total of 136 HBV-HCC and 82 MASH-HCC patients were enrolled in this study. PFS was significantly longer in HBV-HCC patients than in MASH-HCC patients (70.59% vs 41.46%, P<0.001, HR=3.426, 95% CI: 1.668–7.038), while no significant difference in OS was observed (P=0.302). Cox regression indicated that multiple therapeutic modalities were associated with PFS in both subgroups. After stratification by treatment, PFS differences remained prominent in selected interventions. RCS analysis revealed a significant association between CD4⁺ T cell count and PFS (P overall=0.036). Stratification by CD4⁺ T-cell cutoff (354 cells/μL) showed that the prognostic gap between etiologies was diminished in patients with higher CD4⁺ T cell levels, while lower CD4⁺ T cell counts were associated with poorer PFS in MASH-HCC.
Conclusion
MASH-HCC patients showed a trend toward poorer response to ICI therapy compared with HBV-HCC patients, particularly in those with CD4⁺ T cell counts <354 cells/μL. These results suggest that both disease etiology and immune status may contribute to variability in immunotherapy outcomes and warrant consideration in treatment decision making.
Keywords: etiology, immunotherapy, metabolic-associated steatohepatitis related hepatocellular carcinoma
Introduction
Hepatocellular carcinoma (HCC) is the sixth most common cancer worldwide and the third leading cause of cancer-related death.1 It is estimated that by 2050, the number of new HCC cases and deaths will reach 1.52 million and 1.37 million, respectively, indicating a growing global burden.2 Hepatitis B virus (HBV) infection remains one of the main causes of HCC, accounting for about one-third of cases worldwide. However, with the widespread use of vaccination and antiviral therapy, the incidence and mortality of HBV-related HCC have declined.3 Conversely, metabolic dysfunction-associated steatohepatitis (MASH) is becoming an increasingly important cause of HCC. This shift suggests a major change in the etiology of HCC and highlights the growing burden of MASH-HCC.4
Epidemiological studies have shown that HCC caused by MASH alone accounts for about 12.4% of cases. In addition, the prevalence of MASH among patients with HCC caused by HBV, hepatitis C, and alcohol-related liver disease is as high as 40.0%, 54.1%, and 64.3%, respectively.5 Collectively, these findings indicate that MASH is highly prevalent in HCC patients and may act both as an independent cause and in combination with other liver diseases. Previous studies suggest that MASH-HCC may have poorer overall survival (OS) and is often associated with larger tumor size and a higher rate of extrahepatic metastasis.6 However, the prognosis of MASH-HCC remains complex and controversial, and the underlying causes of these differences need further investigation.
Currently, treatment options for HCC include surgical resection, local therapy, and systemic therapy. However, increasing evidence suggests that due to the metabolic background and unique tumor biology of MASH-HCC, the response to treatment may differ from that of HCC caused by other etiologies.7 For example, patients with MASH-HCC who undergo surgical resection in the early stage may have poorer postoperative outcomes, partly due to obesity and other metabolic conditions.8 In contrast, no significant difference in OS has been observed between MASH- and non-MASH-HCC patients receiving local therapies.9,10 Notably, recent studies have reported that patients with MASH-HCC may have a reduced response or even potential adverse outcomes when treated with immune checkpoint inhibitors (ICIs).10,11 Emerging evidence suggests that treatment responses differ between MASH-HCC and HBV-HCC patients, particularly in the context of immunotherapy. Studies have shown that the immune microenvironment differs significantly between MASH-HCC and HBV-HCC patients, characterized by increased expression of exhaustion markers on CD8⁺ T cells leading to CD8⁺ T-cell dysfunction, which is one of the main reasons for resistance to ICI therapy in MASH-HCC patients.12,13
Therefore, based on these findings, this study aimed to compare the treatment response and prognosis of patients with MASH-HCC and HBV-HCC under different treatment strategies.
Methods
Patients
This retrospective study included patients with HCC who were hospitalized at Beijing Ditan Hospital, Capital Medical University, between January 2022 and December 2025. The inclusion criteria were as follows: (1) age between 18 and 75 years; (2) a confirmed diagnosis of either HBV-HCC or MASH-HCC. The exclusion criteria were: (1) age <18 or >75 years; (2) HCC caused by other etiologies, including hepatitis A, C, D, or E virus infection, alcohol-related liver disease, or autoimmune liver disease; (3) presence of other malignancies; (4) loss to follow-up. The Ethics Committee of Beijing Ditan Hospital approved this study and it was conducted in accordance with the Declaration of Helsinki.
Diagnosis
The diagnosis of HCC was based on the following criteria:14 (1) histopathological confirmation of HCC; or (2) typical imaging features on abdominal ultrasound, computed tomography (CT), or magnetic resonance imaging (MRI). MASH-HCC was diagnosed according to established HCC criteria, together with either histological evidence of MASH or the presence of metabolic risk factors, including overweight/obesity (BMI ≥23 kg/m2), central obesity (waist circumference ≥94 cm in men or ≥80 cm in women), dyslipidemia (triglycerides (TG) ≥150 mg/dL or reduced HDL cholesterol < 40 mg/dL in men and <50 mg/dL in women).15 Viral control was defined as undetectable HBV DNA levels during nucleos(t)ide analogue therapy or sustained virological response (SVR).
Follow-up and Outcomes
All patients were followed for at least one year. The primary endpoint was progression-free survival (PFS), and the secondary endpoint was 1-year OS. During follow-up, CT or MRI examinations were performed every 3–6 months to assess tumor progression and treatment response. PFS was defined as the time from treatment initiation to the first occurrence of radiological disease progression or death from any cause, whichever occurred first.16
Statistical Analysis
Statistical analysis was performed using R software (version 4.4.3). Continuous variables were expressed as mean ± standard deviation and compared using the independent Student’s t-test or Mann–Whitney U-test, depending on data distribution. Categorical variables were expressed as counts and compared using the chi-square test or Fisher’s exact test. PFS and OS were estimated using the Kaplan–Meier (KM) method, and differences between groups were compared using the Log rank test. Cox proportional hazards regression models were used to identify factors associated with PFS. Restricted cubic spline (RCS) analysis was performed to evaluate the relationship between immune cell levels and PFS. The number of variables included in the multivariable Cox regression analysis was determined according to the events-per-variable (EPV) principle, with candidate predictors selected accordingly.17 The proportional hazards assumption was evaluated using Schoenfeld residuals before model fitting. No significant violations of the proportional hazards assumption were observed for the variables included in the final multivariable models. All tests were two-sided, and P<0.05 was considered statistically significant.
Results
Baseline Characteristics
A total of 586 patients with HCC admitted to Beijing Ditan Hospital, Capital Medical University, between January 2022 and December 2025 were enrolled. After applying the inclusion and exclusion criteria, 218 patients were included, including 136 patients with HBV-HCC and 82 patients with MASH-HCC (Figure 1).
Figure 1.
Study flowchart.
Compared with the HBV-HCC group, patients with MASH-HCC were significantly older (65.28 ± 11.27 vs 59.48 ± 9.52, P<0.001) and had a higher proportion of females (43.90% vs 16.18%, P<0.001). In terms of clinical characteristics, ascites (68.38% vs 37.80%, P<0.001), infection rate (37.50% vs 21.95%, P=0.017), and hypersplenism (46.32% vs 4.88%, P<0.001) were more common in the HBV-HCC group. No significant differences were observed in hypertension, diabetes, hepatic encephalopathy, or gastrointestinal bleeding between the two groups (all P>0.05). For laboratory parameters, white blood cell (WBC) count (6.54 ± 6.94 vs 4.44 ± 1.93, P=0.009), platelet count (170.66 ± 115.24 vs 113.38 ± 66.45, P<0.001), and LDL-C levels (2.49 ± 0.97 vs 2.11 ± 0.79, P=0.002) were significantly higher in the MASH-HCC group. Other liver function and biochemical markers showed no significant differences. In terms of immune parameters, CD8⁺ T cell counts were higher in the MASH-HCC group (328.49 ± 205.36 vs 268.65 ± 206.82, P=0.039), whereas CD4⁺ T cell levels were significantly lower compared with the HBV-HCC group (415.15 ± 297.76 vs 573.48 ± 289.45, P<0.001).
Regarding tumor characteristics, portal vein tumor thrombosis was more frequent in the HBV-HCC group (45.59% vs 26.83%, P=0.006), while distant metastasis showed no significant difference. More patients in the HBV-HCC group were classified as BCLC stage C (53.68% vs 31.71%, P=0.001). Treatment distribution also differed significantly between groups (P=0.002), with a higher proportion of surgical resection in the MASH-HCC group (18.29% vs 3.68%). Survival analysis showed that PFS was significantly longer in the HBV-HCC group than in the MASH-HCC group (70.59% vs 41.46%, P<0.001), while no significant difference was observed in OS (P=0.302) (Table 1 and Figure 2). Overall, these findings suggest potential differences in treatment response between the two groups.
Table 1.
Baseline Characteristics of HBV-HCC and MASH-HCC Patients
| Variables | HBV-HCC (n = 136) | MASH-HCC (n = 82) | P |
|---|---|---|---|
| Age | 59.48 ± 9.52 | 65.28 ± 11.27 | <0.001 |
| Gender | <0.001 | ||
| Female | 22 (16.18) | 36 (43.90) | |
| Male | 114 (83.82) | 46 (56.10) | |
| Hypertension | 64 (47.06) | 44 (53.66) | 0.345 |
| DM | 47 (34.56) | 34 (41.46) | 0.307 |
| Ascites | 93 (68.38) | 31 (37.80) | <0.001 |
| HE | 21 (15.44) | 9 (10.98) | 0.354 |
| Gastrointestinal bleeding | 11 (8.09) | 5 (6.10) | 0.585 |
| Infection | 51 (37.50) | 18 (21.95) | 0.017 |
| Splenomegaly | 73 (53.68) | 37 (45.12) | 0.221 |
| Hypersplenism | 63 (46.32) | 4 (4.88) | <0.001 |
| WBC | 4.44 ± 1.93 | 6.54 ± 6.94 | 0.009 |
| Neutrophils | 65.53 ± 11.50 | 66.14 ± 12.10 | 0.708 |
| Lymphocytes | 22.55 ± 9.85 | 23.59 ± 10.36 | 0.457 |
| Hemoglobin | 122.06 ± 29.72 | 121.20 ± 23.31 | 0.811 |
| Platelet | 113.38 ± 66.45 | 170.66 ± 115.24 | <0.001 |
| ALT | 34.44 ± 33.19 | 45.93 ± 60.76 | 0.073 |
| AST | 53.74 ± 70.25 | 54.88 ± 71.37 | 0.909 |
| TBIL | 26.65 ± 26.23 | 23.68 ± 36.41 | 0.486 |
| ALB | 35.98 ± 5.41 | 36.60 ± 5.91 | 0.433 |
| LDH | 224.81 ± 86.48 | 220.92 ± 95.64 | 0.758 |
| GGT | 100.34 ± 159.80 | 104.17 ± 117.09 | 0.851 |
| ALP | 125.89 ± 97.58 | 138.56 ± 182.87 | 0.506 |
| TC | 3.87 ± 4.30 | 3.89 ± 1.11 | 0.965 |
| TG | 1.07 ± 1.70 | 2.50 ± 11.75 | 0.163 |
| HDL-C | 0.90 ± 0.31 | 0.95 ± 0.30 | 0.248 |
| LDL-C | 2.11 ± 0.79 | 2.49 ± 0.97 | 0.002 |
| K | 3.85 ± 0.41 | 3.84 ± 0.49 | 0.914 |
| Sodium | 138.24 ± 11.40 | 140.43 ± 3.85 | 0.094 |
| Creatinine | 7.96 ± 15.00 | 6.26 ± 4.37 | 0.319 |
| Blood urea nitrogen | 75.46 ± 41.96 | 70.90 ± 33.94 | 0.405 |
| Glucose | 7.18 ± 3.15 | 7.05 ± 3.02 | 0.765 |
| PT | 13.56 ± 2.01 | 13.74 ± 5.31 | 0.719 |
| INR | 1.25 ± 0.19 | 1.26 ± 0.50 | 0.883 |
| AFP | 315.81 ± 865.05 | 205.29 ± 565.42 | 0.304 |
| CD8+T cell | 268.65 ± 206.82 | 328.49 ± 205.36 | 0.039 |
| CD4+T cell | 573.48 ± 289.45 | 415.15 ± 297.76 | <0.001 |
| NK cell | 159.10±130.07 | 156.51±117.26 | 0.883 |
| Portal vein tumor thrombus | 62 (45.59) | 22 (26.83) | 0.006 |
| Distant metastasis | 39 (28.68) | 17 (20.73) | 0.193 |
| Tumor Size | 0.169 | ||
| < 5cm | 47 (34.56) | 36 (43.90) | |
| ≥ 5cm | 89 (65.44) | 46 (56.10) | |
| Treatment | 0.002 | ||
| Conservative treatment | 37 (27.21) | 14 (17.07) | |
| Locoregional therapy | 29 (21.32) | 15 (18.29) | |
| ICI treatment | 65 (47.49) | 38 (46.34) | |
| Surgical resection | 5 (3.68) | 15 (18.29) | |
| CTP | 0.509 | ||
| A | 84 (61.76) | 57 (69.51) | |
| B | 42 (30.88) | 20 (24.39) | |
| C | 10 (7.35) | 5 (6.10) | |
| BCLC | 0.001 | ||
| 0 | 9 (6.62) | 1 (1.22) | |
| A | 20 (14.71) | 22 (26.83) | |
| B | 24 (17.65) | 27 (32.93) | |
| C | 73 (53.68) | 26 (31.71) | |
| D | 10 (7.35) | 6 (7.32) | |
| PFS | 96 (70.59) | 34 (41.46) | <0.001 |
| OS | 15 (11.03) | 13 (15.85) | 0.302 |
Abbreviations: HBV, hepatitis B virus; HCC, hepatocellular carcinoma; MASH, metabolic dysfunction-associated steatohepatitis; DM, diabetes mellitus; HE, hepatic encephalopathy; WBC, white blood cell; ALT, alanine aminotransferase; AST, aspartate aminotransferase; ALP, alkaline phosphatase; GGT, gamma-glutamyl transferase; TBIL, total bilirubin; ALB, albumin; LDH, lactate dehydrogenase; TC, total cholesterol; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; PT, prothrombin time; INR, international normalized ratio; AFP, alpha-fetoprotein; BCLC, Barcelona Clinic Liver Cancer; CTP, Child–Pugh classification; PFS, progression-free survival; OS, overall survival.
Figure 2.
Kaplan–Meier (KM) curves of PFS and OS. (A) KM curves showing PFS comparison in HBV-HCC and MASH-HCC patients; (B) KM curves showing OS comparison in HBV-HCC and MASH-HCC patients.
ICI Therapy Showed Greater Benefit in HBV-HCC Than in MASH-HCC
To further investigate whether disease etiology influenced treatment outcomes, Cox regression analyses were performed separately in the HBV-HCC and MASH-HCC groups using PFS as the study endpoint. In the HBV-HCC group, univariate analysis identified infection, WBC count, total bilirubin, gamma-glutamyl transferase, alkaline phosphatase (ALP), serum sodium (Na), creatinine, blood urea nitrogen, international normalized ratio, and distant metastasis as significant factors associated with PFS (all P<0.05). Because ascites, hypersplenism, and portal vein tumor thrombosis (PVTT) differed between the two groups at baseline and are important factors influencing tumor treatment, these variables were included as confounders in the multivariate regression analysis. Multivariate analysis showed that ALP (P=0.042, HR: 1.01, 95% CI: 1.01–1.01) and creatinine (P=0.030, HR: 1.18, 95% CI: 1.02–1.36) were independent risk factors, whereas ascites (P=0.277, HR: 1.65, 95% CI: 0.67–4.03), hypersplenism (P=0.640, HR: 0.83, 95% CI: 0.36–1.79), and PVTT (P=0.836, HR: 1.08, 95% CI: 0.53–2.19) were not statistically significant. Furthermore, treatment modality had a significant impact on prognosis. Local therapy (P=0.022, HR: 0.23, 95% CI: 0.07–0.81), ICI therapy (P=0.005, HR: 0.19, 95% CI: 0.06–0.61), and surgical resection (P=0.001, HR: 0.09, 95% CI: 0.02–0.38) were independent protective factors (Table 2).
Table 2.
Cox Regression Analysis of PFS in HBV-HCC Patients
| Variables | P | HR (95% CI) | P | HR (95% CI) |
|---|---|---|---|---|
| Age | 0.092 | 1.02 (1.00 ~ 1.05) | ||
| Gender | ||||
| Female | 1.00 (Reference) | |||
| Male | 0.385 | 0.78 (0.44 ~ 1.37) | ||
| Hypertension | 0.114 | 1.60 (0.89 ~ 2.85) | ||
| DM | 0.321 | 0.74 (0.41 ~ 1.33) | ||
| Ascites | 0.177 | 1.49 (0.84 ~ 2.64) | 0.277 | 1.65 (0.67–4.03) |
| HE | 0.067 | 2.12 (0.95 ~ 4.74) | ||
| Gastrointestinal bleeding | 0.103 | 2.37 (0.84 ~ 6.66) | ||
| Infection | <0.001 | 3.03 (1.65 ~ 5.56) | ||
| Splenomegaly | 0.859 | 1.05 (0.60 ~ 1.86) | ||
| Hypersplenism | 0.819 | 1.13 (0.40 ~ 3.14) | 0.640 | 0.83 (0.36–1.79) |
| WBC | <0.001 | 1.09 (1.04 ~ 1.14) | ||
| Neutrophils | 0.364 | 1.01 (0.99 ~ 1.04) | ||
| Lymphocytes | 0.456 | 0.99 (0.96 ~ 1.02) | ||
| Hemoglobin | 0.430 | 1.00 (0.98 ~ 1.01) | ||
| PLT | 0.527 | 1.00 (1.00 ~ 1.00) | ||
| ALT | 0.954 | 1.00 (1.00 ~ 1.00) | ||
| AST | 0.412 | 1.00 (1.00 ~ 1.00) | ||
| TBIL | <0.001 | 1.02 (1.01 ~ 1.03) | ||
| ALB | 0.043 | 0.95 (0.90 ~ 0.99) | ||
| LDH | 0.057 | 1.00 (1.00 ~ 1.01) | ||
| GGT | 0.001 | 1.01 (1.01 ~ 1.01) | ||
| ALP | <0.001 | 1.01 (1.01 ~ 1.01) | 0.042 | 1.01 (1.01 ~ 1.01) |
| TC | 0.419 | 1.11 (0.86 ~ 1.43) | ||
| TG | 0.540 | 0.96 (0.85 ~ 1.09) | ||
| HDL-C | 0.572 | 0.74 (0.26 ~ 2.10) | ||
| LDL-C | 0.163 | 1.29 (0.90 ~ 1.85) | ||
| K | 0.101 | 1.83 (0.89 ~ 3.78) | ||
| Sodium | <0.001 | 0.86 (0.79 ~ 0.94) | ||
| Creatinine | <0.001 | 1.17 (1.09 ~ 1.26) | 0.030 | 1.18 (1.02 ~ 1.36) |
| Blood urea nitrogen | <0.001 | 1.02 (1.01 ~ 1.03) | ||
| Glucose | 0.197 | 0.93 (0.84 ~ 1.04) | ||
| INR | 0.006 | 2.48 (1.30 ~ 4.74) | ||
| AFP | 0.961 | 1.00 (1.00 ~ 1.00) | ||
| CD8+T cell | 0.809 | 1.00 (1.00 ~ 1.00) | ||
| CD4+T cell | 0.808 | 1.00 (1.00 ~ 1.00) | ||
| Portal vein tumor thrombus | 0.059 | 1.79 (0.98 ~ 3.27) | 0.836 | 1.08 (0.53–2.19) |
| Distant metastasis | 0.001 | 2.85 (1.52 ~ 5.32) | ||
| Treatment | ||||
| Conservative treatment | 1.00 (Reference) | 1.00 (Reference) | ||
| Locoregional therapy | <0.001 | 0.22 (0.11 ~ 0.47) | 0.022 | 0.23 (0.07 ~ 0.81) |
| ICI treatment | 0.001 | 0.27 (0.12 ~ 0.60) | 0.005 | 0.19 (0.06 ~ 0.61) |
| Surgical resection | <0.001 | 0.11 (0.04 ~ 0.32) | 0.001 | 0.09 (0.02 ~ 0.38) |
| BCLC | ||||
| 0 | 1.00 (Reference) | |||
| A | 0.547 | 0.71 (0.23 ~ 2.17) | ||
| B | 0.640 | 0.77 (0.26 ~ 2.28) | ||
| C | 0.809 | 1.13 (0.41 ~ 3.16) | ||
| D | 0.311 | 1.84 (0.57 ~ 5.97) |
Abbreviations: HBV, hepatitis B virus; HCC, hepatocellular carcinoma; MASH, metabolic dysfunction-associated steatohepatitis; DM, diabetes mellitus; HE, hepatic encephalopathy; WBC, white blood cell; ALT, alanine aminotransferase; AST, aspartate aminotransferase; ALP, alkaline phosphatase; GGT, gamma-glutamyl transferase; TBIL, total bilirubin; ALB, albumin; LDH, lactate dehydrogenase; TC, total cholesterol; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; PT, prothrombin time; INR, international normalized ratio; AFP, alpha-fetoprotein; BCLC, Barcelona Clinic Liver Cancer.
In the MASH-HCC group, univariate analysis showed that sex, neutrophil count, lymphocyte count, total cholesterol, TG, Sodium, and distant metastasis were associated with PFS (P<0.05). Likewise, ascites, hypersplenism, and PVTT were included in the multivariate regression analysis. Multivariate analysis identified Sodium as an independent protective factor (P=0.002, HR=0.97, 95% CI: 0.95–0.99), whereas ascites (P=0.277, HR: 1.65, 95% CI: 0.67–4.03), hypersplenism (P=0.640, HR: 0.83, 95% CI: 0.36–1.79), and PVTT (P=0.836, HR: 1.08, 95% CI: 0.53–2.19) were not statistically significant. ICI therapy was also associated with improved PFS (P=0.029, HR=0.18, 95% CI: 0.04–0.84) (Table 3).
Table 3.
Cox Regression Analysis of PFS in MASH-HCC Patients
| Variables | P | HR (95% CI) | P | HR (95% CI) |
|---|---|---|---|---|
| Age | 0.467 | 0.99 (0.96 ~ 1.02) | ||
| Gender | ||||
| Female | 1.00 (Reference) | |||
| Male | 0.027 | 9.95 (1.30 ~ 76.08) | ||
| Hypertension | 0.334 | 1.35 (0.73 ~ 2.50) | ||
| DM | 0.372 | 0.74 (0.38 ~ 1.44) | ||
| Ascites | 0.120 | 1.80 (0.86 ~ 3.77) | 0.443 | 1.37 (0.62–3.03) |
| HE | 0.823 | 0.91 (0.38 ~ 2.15) | ||
| Gastrointestinal bleeding | 0.955 | 0.97 (0.30 ~ 3.13) | ||
| Infection | 0.487 | 0.79 (0.41 ~ 1.53) | ||
| Splenomegaly | 0.466 | 0.80 (0.43 ~ 1.47) | ||
| Hypersplenism | 0.722 | 0.89 (0.48 ~ 1.66) | 0.611 | 1.41 (0.38–5.31) |
| WBC | 0.916 | 0.99 (0.84 ~ 1.16) | ||
| Neutrophils | 0.022 | 0.97 (0.94 ~ 0.99) | ||
| Lymphocytes | 0.021 | 1.04 (1.01 ~ 1.07) | ||
| Hemoglobin | 0.578 | 1.00 (0.99 ~ 1.01) | ||
| PLT | 0.865 | 1.00 (1.00 ~ 1.00) | ||
| ALT | 0.784 | 1.00 (0.99 ~ 1.01) | ||
| AST | 0.570 | 1.00 (1.00 ~ 1.00) | ||
| TBIL | 0.418 | 1.00 (0.99 ~ 1.01) | ||
| ALB | 0.773 | 0.99 (0.94 ~ 1.05) | ||
| LDH | 0.877 | 1.00 (1.00 ~ 1.00) | ||
| GGT | 0.984 | 1.00 (1.00 ~ 1.00) | ||
| ALP | 0.824 | 1.00 (1.00 ~ 1.00) | ||
| TC | 0.008 | 1.06 (1.02 ~ 1.10) | ||
| TG | 0.010 | 1.16 (1.04 ~ 1.29) | ||
| HDL-C | 0.977 | 0.99 (0.37 ~ 2.62) | ||
| LDL-C | 0.728 | 0.93 (0.63 ~ 1.38) | ||
| K | 0.085 | 1.99 (0.91 ~ 4.36) | ||
| Sodium | 0.032 | 0.98 (0.96 ~ 0.99) | 0.002 | 0.97 (0.95 ~ 0.99) |
| Creatinine | 0.226 | 0.94 (0.84 ~ 1.04) | ||
| Blood urea nitrogen | 0.404 | 1.00 (0.99 ~ 1.01) | ||
| Glucose | 0.737 | 1.02 (0.93 ~ 1.12) | ||
| INR | 0.293 | 2.16 (0.52 ~ 9.05) | ||
| AFP | 0.273 | 1.00 (1.00 ~ 1.00) | ||
| CD8+T cell | 0.160 | 1.00 (1.00 ~ 1.00) | ||
| CD4+T cell | 0.419 | 1.00 (1.00 ~ 1.00) | ||
| Portal vein tumor thrombus | 0.432 | 1.28 (0.69 ~ 2.36) | 0.307 | 1.12 (0.37–1.37) |
| Distant metastasis | 0.027 | 2.02 (1.08 ~ 3.76) | ||
| Treatment | ||||
| Conservative treatment | 1.00 (Reference) | 1.00 (Reference) | ||
| Locoregional therapy | 0.542 | 2.28 (1.02 ~ 5.08) | 0.220 | 0.46 (0.13 ~ 1.59) |
| ICI treatment | 0.044 | 0.77 (0.33 ~ 1.78) | 0.029 | 0.18 (0.04 ~ 0.84) |
| Surgical resection | 0.694 | 1.36 (0.30 ~ 6.19) | 0.964 | 0.96 (0.17 ~ 5.34) |
| BCLC | ||||
| 0 | 1.00 (Reference) | 1.00 (Reference) | ||
| A | 0.244 | 0.46 (0.12 ~ 1.71) | 0.468 | 0.55 (0.11 ~ 2.79) |
| B | 0.041 | 0.21 (0.05 ~ 0.94) | 0.555 | 0.73 (0.26 ~ 2.06) |
| C | 0.524 | 0.71 (0.25 ~ 2.04) | 0.103 | 2.25 (0.85 ~ 5.98) |
| D | 0.471 | 0.58 (0.13 ~ 2.58) | 0.820 | 1.21 (0.23 ~ 6.31) |
Abbreviations: HBV, hepatitis B virus; HCC, hepatocellular carcinoma; MASH, metabolic dysfunction-associated steatohepatitis; DM, diabetes mellitus; HE, hepatic encephalopathy; WBC, white blood cell; ALT, alanine aminotransferase; AST, aspartate aminotransferase; ALP, alkaline phosphatase; GGT, gamma-glutamyl transferase; TBIL, total bilirubin; ALB, albumin; LDH, lactate dehydrogenase; TC, total cholesterol; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; PT, prothrombin time; INR, international normalized ratio; AFP, alpha-fetoprotein; BCLC, Barcelona Clinic Liver Cancer.
Taken together, although the prognostic factors differed between the two groups, ICI therapy consistently showed a protective effect, suggesting that it may play a key role in the differences in PFS between etiologies.
Treatment-Specific Survival Differed by HCC Etiology
To further evaluate the impact of treatment modality on prognosis, Kaplan–Meier analyses were performed after stratification by treatment strategy. In the conservative treatment group, HBV-HCC patients had significantly longer PFS than MASH-HCC patients (P<0.0001, HR: 6.649, 95% CI: 1.952–22.65) (Figure 3A). No significant difference was observed in the local therapy group (P=0.7, HR: 1.174, 95% CI: 0.504–2.735) (Figure 3B). In the ICI treatment group, HBV-HCC patients again showed significantly better PFS (P=0.00013, HR: 3.426, 95% CI: 1.668–7.038, HR: 0.928, 95% CI: 0.178–4.948) (Figure 3C). In contrast, no significant difference was found in the surgical group (P=0.94) (Figure 3D).
Figure 3.
Kaplan–Meier (KM) curves of PFS stratified by treatment type. (A) KM curves in Conservative treatment group; (B) KM curves in Local therapy group; (C) KM curves in Immune checkpoint inhibitor (ICI) therapy group; (D) KM curves in Surgical resection group.
For OS, MASH-HCC patients had worse survival than HBV-HCC patients in the conservative treatment group (Figure 4A), while no significant differences were observed in other treatment groups (Figure 4B–D).
Figure 4.
Kaplan–Meier curves of OS stratified by treatment type. (A) KM curves in Conservative treatment group; (B) KM curves in Local therapy group; (C) KM curves in Immune checkpoint inhibitor (ICI) therapy group; (D) KM curves in Surgical resection group.
Collectively, these results indicate that differences in PFS are mainly observed in the conservative and ICI treatment groups, with the most pronounced difference in the ICI group, suggesting a poorer response to immunotherapy in MASH-HCC patients.
Low CD4⁺ T Cells Limited ICI Benefit in MASH-HCC
Given the observed differences in immunotherapy outcomes between the two etiological groups, we next investigated whether baseline immune cell subsets were associated with PFS. The CD4⁺ T cell count showed a significant overall association with PFS (P overall=0.036), although no significant nonlinear relationship was observed (P non-linear=0.51) (Figure 5A). In contrast, no significant association was found between CD8+T cell count and NK cell count and PFS (Figure 5B and C).
Figure 5.
Restricted cubic spline (RCS) and KM curves of immune cell counts and PFS. (A) RCS analysis of CD4⁺ T cells and PFS; (B) RCS analysis of CD8⁺ T cells and PFS; (C) RCS analysis of NK cells and PFS; (D) KM curves for PFS stratified by CD4⁺ T-cell count (cut-off: 354 cells/μL) in patients with CD4⁺ T cells <354 cells/μL; (E) KM curves for PFS stratified by CD4⁺ T-cell count (cut-off: 354 cells/μL) in patients with CD4⁺ T cells ≥354 cells/μL.
Based on the optimal cutoff identified by the RCS analysis, patients were stratified using a CD4⁺ T-cell cut-off value of 354 cells/μL. In the high CD4 group (≥354 cells/μL), the difference in PFS between MASH-HCC and HBV-HCC patients was attenuated and did not reach statistical significance (P=0.09, HR: 1.974, 95% CI: 0.734–5.310) (Figure 5D). In contrast, in the low CD4 group (<354 cells/μL), KM analysis demonstrated that MASH-HCC patients had significantly worse PFS compared with HBV-HCC patients (P = 0.00042, HR: 2.385, 95% CI: 1.432–3.971), suggesting that preserved CD4⁺ T-cell levels may explain the prognostic disparity between etiologies. These findings suggest that CD4⁺ T cells may play a specific role in the prognosis of MASH-HCC.
To further investigate the relationship between metabolic status and systemic immunity, we analyzed the correlations between baseline circulating CD4⁺ T-cell counts and serum lipid parameters in patients stratified by the optimal CD4⁺ T-cell cutoff (354 cells/μL). Among patients with baseline CD4⁺ T-cell counts <354 cells/μL, CD4⁺ T-cell counts were significantly negatively correlated with both total cholesterol (TC) (R = −0.36, P = 6.6 × 10−8) and triglyceride (TG) levels (R = −0.45, P = 3.8 × 10−12) (Figure 6A). In contrast, no significant correlations were observed between CD4⁺ T-cell counts and either TC (R = 0.19, P = 0.065) or TG (R = 0.18, P = 0.08) among patients with baseline CD4⁺ T-cell counts ≥354 cells/μL (Figure 6B).
Figure 6.
Correlation between serum lipid parameters and baseline circulating CD4⁺ T-cell counts. (A) Patients with baseline CD4⁺ T-cell counts <354 cells/μL. The correlation between baseline CD4⁺ T-cell counts and total cholesterol (left) or triglyceride levels (right); (B) Patients with baseline CD4⁺ T-cell counts ≥354 cells/μL. The correlation between baseline CD4⁺ T-cell counts and total cholesterol (left) or triglyceride levels (right).
Limited ICI Benefit Was Observed in MASH-HCC
Finally, subgroup analyses were performed to further evaluate whether the effect of ICI therapy differed across clinically relevant subgroups. In the MASH-HCC group, no significant benefit of ICI therapy was observed in the overall population or across most subgroups, and no significant interaction was detected. A potential benefit was observed only in the Child–Pugh (CTP) subgroup (Figure 7A).
Figure 7.
Subgroup analysis comparing PFS between ICI therapy and other treatment across clinical subgroups. (A) Subgroup analysis in MASH-HCC group; (B) Subgroup analysis in HBV-HCC group.
In contrast, in the HBV-HCC group, ICI therapy showed a consistent benefit in the overall population and most subgroups, with a significant interaction observed only in the BCLC stage subgroup (P for interaction=0.008) (Figure 7B). Overall, these findings further support that MASH-HCC patients derive limited benefit from ICI therapy compared with HBV-HCC patients.
Discussion
This study systematically compared treatment outcomes between patients with MASH-HCC and HBV-HCC under different therapeutic strategies and demonstrated that disease etiology was associated with treatment efficacy. Overall, patients with MASH-HCC showed less favorable treatment responses, particularly to ICI therapy. Furthermore, RCS analysis demonstrated that baseline circulating CD4⁺ T-cell count was significantly associated with PFS, and subgroup analyses further indicated that patients with MASH-HCC derived limited benefit from ICI therapy compared with those with HBV-HCC. Collectively, these findings suggest that both disease etiology and baseline immune status may influence immunotherapy outcomes in HCC.
The reduced response of MASH-HCC to ICI therapy has attracted increasing attention. Preclinical studies have shown that PD-1 blockade does not induce tumor regression in MASH-HCC mouse models and may even promote tumor development when used preventively.18 Consistent with these experimental findings, meta-analyses have reported that patients with viral-related HCC achieve greater survival benefit from immunotherapy than those with non-viral HCC,19 which is consistent with our findings. However, some studies have suggested that the response to ICI therapy in advanced HCC is not affected by etiology.20 One possible explanation for this discrepancy is that these studies generally combined all non-viral HCC into a single category without distinguishing MASH-HCC from other non-viral etiologies, thereby potentially obscuring important biological and clinical heterogeneity. Therefore, further studies with larger sample sizes and clear etiological classification are needed to better define the response of MASH-HCC to ICI therapy.
In terms of survival outcomes, no significant difference in one-year OS was observed between MASH-HCC and HBV-HCC patients in this study. This finding should be interpreted cautiously, as the relatively short follow-up period may have limited our ability to detect differences in long-term survival. Previous meta-analyses have shown that although OS may not differ significantly, patients with MASH-HCC may have a higher overall risk of death.21 In addition, MASH-HCC is often diagnosed at a later stage, which may lead to worse long-term outcomes.22 Similarly, a study with a median follow-up of 6.9 years also reported poorer OS and a higher risk of liver decompensation in MASH-HCC patients treated with radiofrequency ablation.23 Therefore, the lack of OS difference in this study should be interpreted with caution and needs further validation.
An important finding of this study is the association between CD4⁺ T-cell count and PFS in MASH-HCC patients, particularly when stratified by a cut-off value of 354 cells/μL. Previous studies have shown that lipid metabolic imbalance in MASH can lead to selective loss of intrahepatic CD4⁺ T cells, which promotes HCC development,24,25 consistent with our findings. CD4⁺ T cells are functionally diverse and can differentiate into multiple subsets, including Th1, Th2, Th17, Th22, and regulatory T cells (Treg). Among these, Treg cells suppress immune responses and promote tumor immune escape, playing a key role in MASH-HCC progression.26–28 Although this study identified a link between CD4⁺ T cells and prognosis, the role of specific CD4⁺ T-cell subsets, especially Treg cells, in ICI response requires further investigation, particularly in relation to the identified threshold of 354 cells/μL.
Despite these findings, this study has several limitations. First, this was a single-center retrospective study with a relatively small sample size, which may limit the generalizability of the results. Second, the follow-up period was relatively short, which may underestimate the impact of etiology on long-term survival. In addition, although stratified analyses according to treatment modality yielded findings consistent with the primary results, the heterogeneity of treatment strategies in this retrospective cohort may still have introduced residual confounding. Third, prospective and multicenter studies are still needed to confirm these findings. A longer follow-up period is required to further validate the robustness of our findings. Therefore, Future studies should focus on larger cohorts and mechanistic exploration.
Conclusions
In conclusion, this study demonstrates that treatment response differs significantly between patients with MASH-HCC and HBV-HCC. ICI therapy provides clear benefit in HBV-HCC patients but shows limited efficacy in MASH-HCC patients, suggesting that disease etiology may influence immunotherapy outcomes. In addition, a CD4⁺ T cell count <354 cells/μL is associated with PFS in MASH-HCC patients, suggesting a potential role of the baseline circulating CD4⁺ T cell count in disease progression. These findings highlight the importance of considering disease etiology in the clinical management of HCC. Patients with MASH-HCC may require more individualized treatment strategies, especially when considering immunotherapy. Further large-scale, prospective, and multicenter studies are needed to validate these results and explore the underlying mechanisms.
Acknowledgments
We thank all patients who participated in the study.
Funding Statement
This study is supported by the National Key Research and Development Program of China (2023YFC2308802); Beijing Municipal Administration of Hospitals Incubating Program (PZ2024034); High-Level Public Health Talent Development Program(Discipline Backbone-03-07); Laboratory for Clinical Medicine, Capital Medical University.
Data Sharing Statement
Data could be obtained for non-commercial purposes upon request via email: xuexin162@163.com.
Ethical Approval
This study received approval from the Ethics Committee of Beijing Ditan Hospital, Capital Medical University (DTEC-KY2024-297-01), and written informed consent was obtained from all participants. All study procedures complied with the ethical principles of the institutional and national committees responsible for human research and were conducted in accordance with the 1975 Declaration of Helsinki and its 2008 revision.
Author Contributions
Yixin Hou conceived, designed, and supervised the study. Fengna Yan co-designed the study and served as a co-principal investigator. Yixin Hou and Fengna Yan were the principal investigators of the study. Xuejie Zhang, Dandan Chen, Yang Zhou, and Peipei Meng were responsible for clinical data collection. Feixiang Xiong, performed the statistical analyses, interpreted the data, and drafted the manuscript. All authors made substantial contributions to the conception and design of the study, data acquisition, analysis, or interpretation; participated in drafting or critically revising the manuscript; approved the final version for publication; agreed to submit the manuscript to this journal; and agree to be accountable for all aspects of the work.
Disclosure
The authors declare no conflicts of interest.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data could be obtained for non-commercial purposes upon request via email: xuexin162@163.com.