Dear Editor,
Tumor heterogeneity in hepatocellular carcinoma (HCC) is a major factor contributing to its high lethality, significantly impacting therapeutic outcomes, complicating patient stratification, and hindering response prediction [
1]. Molecular classification of HCC has greatly advanced our understanding of the mechanisms driving tumor heterogeneity and disease progression [
2,
3]. It has also shown promise in optimizing patient selection and guiding therapeutic decision-making, ultimately enhancing treatment efficacy [
4]. Recently, Aoki et al. [
5] demonstrated that pathological dedifferentiation in HCC is linked to the loss of liver-specific metabolism and a shift toward a glycolysis-dominant metabolic environment. Based on metabolic functions, they reclassified HCC into two major metabolism-based subclasses and further stratified it into five subtypes according to key oncogenic signaling pathways [
5]. The “glycolysis” subclass, characterized by histopathological dedifferentiation and suppressed liver-specific metabolism, includes two subgroups: PI3K/mTOR-High and NOTCH/TGF-β-High, both associated with poor prognosis. In contrast, the “rich metabolism” subclass, which exhibits well-to-moderate differentiation and corresponds to Hoshida S3 with a favorable prognosis, can be further divided into three subtypes: IL6-JAK-STAT3-High, Wnt/β-catenin-High, and the intermediate K-RAS-High group [
5].
Previously, we identified that liver-enriched genes are progressively downregulated with advancing pathological stages and dedifferentiation, and that the extent of liver-specific metabolic function loss can serve as a prognostic indicator for HCC patients [
6]. We also identified a metabolic subtype based on glycolysis and liver-specific ketone body metabolism pathways, which may serve as a promising biomarker for predicting prognosis and therapeutic responses to ketogenic dietary [
7]. Recently, we proposed a novel molecular classification, termed the fatty acid degradation (FAD) subtype (F1, F2, and F3), based on the FAD pathway and demonstrated its potential for guiding personalized treatment strategies [
8]. We commend the authors for proposing the metabolic-signaling subtype of HCC. However, it is important to note that they did not establish a correlation between these subclasses and personalized therapeutic options, which may limit the study’s applicability in clinical decision-making. To bridge this gap, we validated the metabolic-signaling subclasses in two external cohorts and examined their correlation with therapeutic responses, providing further insights into their clinical relevance in HCC treatment.
First, we investigated the association between the metabolic-signaling subclasses and response to anti-PD-1 immunotherapy. Our in-house PD-1 cohort (GSE202069, n=41) and the Korean PD-1 cohort (ERP117672, n=40) were integrated as previously described [
9], forming the newly termed Asian PD-1 cohort (n=81) (
Supplementary Methods). The metabolic classifier stratified patients into the glycolysis subclass and the rich metabolism subclass (
Supplementary Fig. 1A), with an average silhouette coefficient of 0.61, confirming the robustness of the classification (
Fig. 1A). Further subclassification based on oncogenic signaling pathways divided the glycolysis and rich metabolism subclasses into two and three groups, respectively, with an acceptable silhouette coefficient (
Supplementary Fig. 1B,
Fig. 1B). The glycolysis subclass predominantly corresponded to the Inflamed, Hoshida S1, FAD F1, and Chiang proliferation subtypes, whereas the rich metabolism subclass was mainly associated with the Non-inflamed, Hoshida S3, FAD F3, and Chiang CTNNB1/Poly7 subtypes (
Fig. 1C,
Supplementary Table 1). Overall, the glycolysis subclass exhibited higher immune enrichment, response signatures, and elevated expression of immune cell markers, cytokines, and checkpoint molecules compared to the rich metabolism subclass. Notably, the NOTCH/TGF-β-High subclass, associated with the FAD F1 and Hoshida S1 subtypes, exhibited the highest level of immune infiltration, whereas the PI3K/mTOR-High and IL6-JAK-STAT3-High subclasses demonstrated intermediate immune activity. In contrast, the Wnt/β-catenin-High and KRAS-High subclasses were classified as immune-excluded (
Fig. 1C,
Supplementary Table 2). Interestingly, steatohepatitic hepatocellular carcinoma (SH-HCC), a distinct pathological subtype of HCC, was predominantly classified within the rich metabolism subclass, particularly within the IL6-JAK-STAT3-High and K-RAS-High groups. This finding is consistent with previous report indicating that the steatohepatitic subtype frequently exhibits IL-6/JAK/STAT pathway activation [
10].
Clinically, the glycolysis subclass demonstrated a significantly higher anti-PD-1 response rate compared to the rich metabolism subclass (54.5% vs. 5.7%,
P<0.001,
Fig. 1D left panel,
Supplementary Table 1). No significant differences in response rates were observed between the PI3K/mTOR-High and NOTCH/TGF-β-High subclasses. However, all patients in the IL6-JAK-STAT3-High and Wnt/β-catenin-High subclasses were non-responders to anti-PD-1 therapy (
Fig. 1D right panel,
Supplementary Table 2). This finding aligns with previous reports that IL-6/JAK1 and Wnt/β-catenin pathway activation promote cancer immune evasion in HCC [
11,
12].
Next, we classified a transcatheter arterial chemoembolization (TACE) cohort (GSE104580) based on metabolic and signaling pathway features. The silhouette coefficients and heatmap for the identified metabolic and signaling clusters are shown in
Supplementary Figure 2. The rich metabolism subclass exhibited a significantly higher TACE response rate compared to the glycolysis subclass (78.7% vs. 30.6%,
P<0.001,
Fig. 1E left panel,
Supplementary Table 3). Notably, the PI3K/mTOR-High subclass had the lowest TACE response rate, while the IL6-JAK-STAT3-High subclass demonstrated the highest response rate (
Fig. 1E right panel,
Supplementary Table 4). Collectively, these findings suggest that patients in the rich metabolism subclass, particularly those with IL6-JAK-STAT3 pathway activation, are more likely to benefit from TACE.
Despite these insights, our study has several limitations. First, the analysis was conducted using retrospective cohorts, which may introduce selection bias. Second, as this study is based on correlation analysis, the precise molecular mechanisms underlying the differential therapeutic responses among the metabolic-signaling subclasses remain to be fully elucidated.
In conclusion, we commend the authors for their pivotal and promising study. Our research provides additional external validation of metabolic-signaling subclasses and demonstrates their predictive value for clinical responses to immunotherapy and TACE. Further investigations in larger prospective cohorts are warranted to explore the clinical utility of the metabolic-signaling classification in guiding personalized therapy for HCC patients.
FOOTNOTES
-
Authors’ contribution
All authors contributed to this study at different levels. All authors read and approved the final version.
Study concept and design (Binghua Li, Weiwei Hu, Decai Yu); acquisition of data (Binghua Li, Yanchao Xu, Yican Zhu, Yukun Zhang, Tianci Luo, Zijie Wu, Laizhu Zhang; statistical analysis and interpretation of data (Binghua Li, Yanchao Xu, Yican Zhu, Zijie Wu, Laizhu Zhang); drafting of the manuscript (Binghua Li); critical revision of the manuscript for important intellectual content (Binghua Li, Yanchao Xu, Yican Zhu, Weiwei Hu, Decai Yu).
-
Acknowledgements
This work was supported by grants from the National Natural Science Foundation of China (No. 82372834, 82173129, and 82373911), Jiangsu Outstanding Youth Foundation (BK20240119) and the Fundings for Clinical Trials from the Affiliated Drum Tower Hospital, Medical School of Nanjing University (2023-LCYJ-PY-32).
-
Conflicts of Interest
The authors have no conflicts to disclose.
SUPPLEMENTARY MATERIAL
Supplementary material is available at Clinical and Molecular Hepatology website (
http://www.e-cmh.org).
Supplementary Figure 1.
Violin plots illustrating the ssGSEA scores of Hallmark pathways across different subclasses. (A) Violin plots comparing the metabolic activity of various pathways between the Glycolysis and Rich Metabolism subclasses. (B) Violin plots showing the ssGSEA scores of different pathways across distinct signaling subclasses.
Supplementary Figure 2.
Metabolic and signaling pathway-based molecular classification predicts therapeutic response to TACE in HCC. (A) Silhouette analysis of the two metabolic pathway-based subclasses and five signaling pathway-based subclasses in the TACE cohort. (B) Heatmap illustrating the ssGSEA enrichment scores of metabolic and signaling pathways across different subclasses and their correlation with TACE response. TACE, Transcatheter Arterial Chemoembolization; HCC, hepatocellular carcinoma.
Figure 1.Metabolic-signaling subtypes predict response to immunotherapy and TACE in hepatocellular carcinoma. (A, B) Silhouette analysis of the two metabolic pathway-based subclasses (A) and the five signaling pathway-based subclasses (B) in the Asian PD-1 cohort. (C) Heatmap illustrating correlations between clinicopathologic and immunologic characteristics across metabolic and signaling pathway-based subclasses. P-values comparing the Glycolysis and Rich metabolism subclasses are annotated on the right. (D) Proportion of anti-PD-1 responders and non-responders across the two metabolic subclasses (left panel) and five signaling subclasses (right panel) in the Asian PD-1 cohort. (E) Distribution of TACE responders and non-responders across the two metabolic subclasses (left panel) and five signaling subclasses (right panel) in the TACE cohort (GSE104580). TACE, Transcatheter Arterial Chemoembolization.
Abbreviations
steatohepatitic hepatocellular carcinoma
Transcatheter Arterial Chemoembolization
REFERENCES
- 1. Lu LC, Hsu CH, Hsu C, Cheng AL. Tumor heterogeneity in hepatocellular carcinoma: Facing the challenges. Liver Cancer 2016;5:128-138.
- 2. Dhanasekaran R, Suzuki H, Lemaitre L, Kubota N, Hoshida Y. Molecular and immune landscape of hepatocellular carcinoma to guide therapeutic decision-making. Hepatology 2025;81:1038-1057.
- 3. Shimada S, Tanaka S. The chronicles of hepatocellular carcinoma classification: Subtyping, modeling, and treatment. Hepatology 2024;79:261-263.
- 4. Wang MD, Diao YK, Yao LQ, Fan ZQ, Wang KC, Wu H, et al. Emerging role of molecular diagnosis and personalized therapy for hepatocellular carcinoma. iLIVER 2024;3:100083.
- 5. Aoki T, Nishida N, Kurebayashi Y, Sakai K, Fujiwara N, Tsurusaki M, et al. Molecular classification of hepatocellular carcinoma based on zoned metabolic feature and oncogenic signaling pathway. Clin Mol Hepatol 2025;31:981-1002.
- 6. Li B, Xu T, Liu C, Meng G, Sun Y, Qian L, et al. Liver-enriched genes are associated with the prognosis of patients with hepatocellular carcinoma. Sci Rep 2018;8:11197.
- 7. Qian L, Li Y, Cao Y, Meng G, Peng J, Li H, et al. Pan-cancer analysis of glycolytic and ketone bodies metabolic genes: Implications for response to ketogenic dietary therapy. Front Oncol 2021;11:689068.
- 8. Li B, Li Y, Zhou H, Xu Y, Cao Y, Cheng C, et al. Multiomics identifies metabolic subtypes based on fatty acid degradation allocating personalized treatment in hepatocellular carcinoma. Hepatology 2024;79:289-306.
- 9. Li B, Cao Y, Li Y, Cheng C, Yu D. Letter to the editor: the inflamed subclass predicts immunotherapy response - external validations. Gut 2022 Jul 16. doi: 10.1136/gutjnl-2022-328130.
- 10. Calderaro J, Couchy G, Imbeaud S, Amaddeo G, Letouzé E, Blanc JF, et al. Histological subtypes of hepatocellular carcinoma are related to gene mutations and molecular tumour classification. J Hepatol 2017;67:727-738.
- 11. Chan LC, Li CW, Xia W, Hsu JM, Lee HH, Cha JH, et al. IL-6/JAK1 pathway drives PD-L1 Y112 phosphorylation to promote cancer immune evasion. J Clin Invest 2019;129:3324-3338.
- 12. Ruiz de Galarreta M, Bresnahan E, Molina-Sánchez P, Lindblad KE, Maier B, et al. β-catenin activation promotes immune escape and resistance to anti-PD-1 Therapy in hepatocellular carcinoma. Cancer Discov 2019;9:1124-1141.
Citations
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