Graphical Abstract
INTRODUCTION
Genomic instability is a hallmark of epithelial malignancy, caused by smoking, alcohol consumption, chronic inflammation, and aging [1]. Through the accumulation of genetic mutations, cancer cells acquire novel functions while simultaneously losing the functional characteristics of their nonmalignant counterparts. These newly acquired capabilities include increased cell proliferation, aggressiveness, immune evasion, angiogenesis, and adaptation to competition for nutrients. As driver mutations accumulate, cancer cells gain the capacity for autonomous cell division, which markedly enhances proliferative potential [2]. Immune evasion enables cancer cells to persist within the host, leading to the formation of immunosuppressive tumor microenvironments [3]. As tumors grow, they develop new vascular networks and shift their metabolism, such as enhanced glucose metabolism, to overcome nutrient competition. In 1956, Warburg demonstrated that tumors generate adenosine triphosphate (ATP) through oxidative phosphorylation and by enhancing the glycolytic pathway [4] This glycolytic shift is pronounced in gastric and lung cancers [5], and a subset of hepatocellular carcinomas (HCC) has also been shown to exhibit enhanced glycolysis [6]. Even in lactate-rich environments, both tumors and immunosuppressive cells continue to proliferate due to enhanced glycolysis [5].
Conversely, cancer cells may also lose normal functions, including their well-differentiated morphology and metabolic processes. During the multistep process of hepatocarcinogenesis, which progresses from regenerative nodules to dysplastic nodules and early HCC [7], blood flow from the portal vein and hepatic artery often decreases [8,9]. As early HCC dedifferentiates into moderately differentiated HCC, new blood vessels form, increasing tumor blood flow.8 This vascular increase is categorized as nonrim-arterial phase hyperenhancement (APHE) [10]. Tumor blood flow may become heterogeneous during the progression to poorly differentiated HCC, manifesting as inhomogeneous APHE/rim-APHE, particularly when intrahepatic cholangiocarcinoma (iCCA) components are present [11,12]. These changes are accompanied by tumor dedifferentiation, characterized by morphological abnormalities such as disorganized cell arrangements and nuclear pleomorphism [13]. The tumor architecture transitions from a microtrabecular pattern of well-differentiated HCC into a compact or macrotrabecular massive (MTM) pattern associated with poorly differentiated HCC. At this stage, HCC may lose hepatic-specific metabolic functions; however, research on the metabolic capabilities of tumor cells remains limited [14-17].
Building upon the concept of HCC classification proposed by Désert et al. [18], which was influenced by the liver zonation program, we previously emphasized the importance of reclassifying HCC based on liver-specific metabolic functions [19]. Traditional HCC classifications have primarily been based on cellular origin, pathological morphology, and genetic alterations, with molecular classifications, such as the Hoshida et al. [20,21], Calderaro et al. [22], and Boyault et al. [23] classifications. However, few attempts have been made to reclassify hepatocellular tumors based on their unique hepatocyte functions, including albumin production, bile acid synthesis, and lipogenesis. This reclassification provides a broader perspective on the biological characteristics of the disease.
In this study, the newly acquired glucose metabolism program (the Warburg effect) was compared with the preservation of amino acid and lipid metabolic functions typically observed in normal hepatocytes. We also evaluated the relationship between this metabolic classification and the existing classifications based on histopathological morphology, tumor hemodynamics, and molecular features.
MATERIALS AND METHODS
Patients and samples
This retrospective cohort study included 1,052 HCC patients. Of these, 136 HCC samples and their non-cancerous liver tissues were obtained at Kindai University Hospital be-tween December 2003 and July 2014 as fresh-frozen, clinicopathologically, and radiologically annotated samples from patients who underwent surgical resection of primary liver cancers. The median follow-up period was 107.3 months (95% confidence interval [CI] 93.5–121.2). HCC was diagnosed in accordance with the American Association for the Study of Liver Diseases [24]. The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA)-LIHC (n=373) [25], the International Cancer Genome Consortium (ICGC)-LIRI-JP (n=203), GSE14520 (n=225), and GSE76427 (n=115) cohorts were used to validate our findings.
In this study, we first performed clustering analysis using enrichment scores derived from gene sets registered in the Hallmark collection of the Molecular Signatures Database (MSigDB) (https://www.gsea-msigdb.org/gsea/msigdb). Gene sets related to metabolism were initially analyzed, followed by those associated with signaling pathways (details are provided in the Supplementary File). In the training cohort, we identified the combination of gene sets that yielded the highest silhouette coefficient. In the validation cohort, we evaluated the validity and reproducibility of this selected combination of gene sets. Figure 1 shows a flowchart of the study. Details of histopathological assessment, immunohistochemical staining, RNA sequencing, DNA cancer panel, and statistical analysis are provided in the online Supplemental File. This study adhered to the Declaration of Helsinki and was approved by the Kindai University Hospital IRB (Approval R04-179) in 2022. Informed consent for archived samples was waived with an opt-out approach under Japan’s Personal Information Protection Act.
RESULTS
Evaluation of tumor metabolism using MSigDB HALLMARK enrichment scores
Gene set variation analysis (GSVA) was performed on a training dataset and four external validation datasets using 50 gene sets from the Hallmark collection registered in MSigDB. Among the 50 gene sets, six were associated with metabolism (Supplementary Table 3A). Of these, the combination of the following four gene sets yielded the highest silhouette coefficients: “HALLMARK_BILE_ACID_ METABOLISM,” “HALLMARK_FATTY_ACID_METABOLISM,” “HALLMARK_GLYCOLYSIS,” and “HALLMARK_ XENOBIOTIC_METABOLISM” (Supplementary Table 3B). Figure 2A presents the hierarchical clustering (Ward’s method) using the enrichment scores of four gene sets, classifying the training 136 samples into two distinct groups: a glycolysis-enhanced subclass with suppressed liver-specific metabolic functions (glycolysis subclass, n=54, 39.7%) and a subclass with enhanced liver-specific metabolic functions, including bile acid, fatty acid, and xenobiotic metabolism with suppressed glycolytic pathways (rich metabolism subclass, n=82, 60.3%). The average silhouette coefficient was 0.61 (Supplementary Fig. 2A), and the first two principal components showed clear separation between the groups (Supplementary Fig. 2B).
The baseline patient characteristics are summarized in Table 1 and Supplementary Table 4. Patients in the glycolysis subclass had a significantly larger tumor size (5.00 cm [interquartile range (IQR) 3.00–7.00] vs. 3.50 cm [IQR 2.55–5.00], P=0.035) and a higher incidence of vascular invasion (46.3% vs. 19.5%, P=0.002). Despite no significant differences in etiology or liver fibrosis markers, including platelet count and FIB-4 index, the glycolysis subclass exhibited higher neutrophil-lymphocyte ratio (NLR) and Creactive protein (CRP) levels. In terms of tumor markers, alpha fetoprotein (AFP) (median 235.0 vs. 7.0 ng/dL, P<0.001) and des-γ-carboxy prothrombin (DCP) (median 227.5 vs. 115.5 mAU/mL, P=0.018) were significantly elevated in the glycolysis subclass.
Histopathologically, the glycolysis subclass showed a significantly higher prevalence of poorly differentiated HCC (42.6% vs. 4.9%, P<0.001), with a higher incidence of compact (48.1% vs. 21.7%, P=0.006) and MTM patterns (17.6% vs. 0%, P=0.001). In contrast, microtrabecular (36.0% vs. 81.1%, P<0.001) and pseudoglandular structures were less common (1.9% vs. 32.5%, P<0.001). Immunohistochemistry (IHC) showed a high positivity rate for biliary stem cell markers, such as epithelial cell adhesion molecule (Ep-CAM), cytokeratin 19 (CK19), and Spalt-like4 (SALL4) (Supplementary Table 4). The Wnt/β-catenin mutation was predominantly wild-type in the glycolysis subclass (87.0%, P=0.013), whereas TP53/cell cycle control mutations were observed in 46.3% of cases (P=0.066) (Fig. 2A, Table 1). No other genetic mutations showed significant differences between groups. Most cases in the glycolysis subclass were classified as Hoshida subclass S1/2 (98.1% vs. 17.1%, P<0.001) (Fig. 2A, Table 1).
Radiological findings (Fig. 2B) revealed a significantly higher incidence of inhomogeneous APHE/rim-APHE in the glycolysis subclass (70.8% vs. 32.4%, P<0.001). Among 61 patients who underwent gadolinium-ethoxybenzyl-diethylenetriamine (Gd-EOB-DTPA)-enhanced MRI, the glycolysis subclass had significantly fewer nodules with a relative enhancement ratio ≥0.9 (8.0% vs. 38.9%, P<0.05). Additionally, 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)-CT scans revealed high tumor-toliver uptake ratios (TLR) ≥2.0 in all five glycolysis subclass cases (100% vs. 0%, P=0.02).
Survival analysis (Kaplan–Meier, log-rank test) demonstrated a median overall survival (OS) of 58.2 months (95% CI 36.9–NA) in the glycolysis subclass, compared to 92.4 months (95% CI 75.9–NA) in the rich metabolism subclass (P=0.20) (Fig. 2C). The median recurrence-free survival (RFS) was 10.2 months (95% CI 4.63–24.0) for the glycolysis subclass and 21.3 months (95% CI 14.6–29.3) for the rich metabolism subclass (Fig. 2C), with a significantly higher rate of recurrence observed in the glycolysis subclass (P=0.01).
Differential expression, gene ontology (GO)-BP, Kyoto Encyclopedia of Genes and Genomes (KEGG) gene set enrichment analysis (GSEA), and pathway mapping (Supplementary Fig. 2C–2G) revealed high enrichment scores for cell cycle-related pathways in the glycolysis subclass, whereas fatty acid, xenobiotics, and drug metabolism pathways showed lower scores. Pathway mapping indicated a metabolic environment favoring the conversion of glucose to pyruvate and lactate (Supplementary Fig. 3A–3D).
Reclassification of the rich metabolism subclass HCC
Figure 3A shows hierarchical clustering (Ward’s method) based on the signaling pathway enrichment scores for HCC samples in the rich metabolism subclass (n=82, 60.3%). The pathways of MSigDB used are listed in Supplementary Table 5, and the top gene sets based on silhouette coefficients were HALLMARK_IL6_JAK_ STAT3_SIGNALING, HALLMARK_KRAS_SIGNALING_UP, and HALLMARK_WNT_BETA_CATENIN_SIGNALING (Supplementary Table 3B). The silhouette coefficients for the three identified clusters are shown in Supplementary Figure 4A. These three clusters were designated as Wnt/β-catenin-High (n=27, 19.9%), K-RAS-High (n=21, 15.4%), and IL6-JAK-STAT3-High (n=34, 25%) based on the most expressed pathway in each group.
Supplementary Table 6 compares the characteristics of the three groups. The Wnt/β-catenin-High group had a significantly higher proportion of males (92.6%, P=0.022) and a higher frequency of CTNNB1 (D32–S37) mutations (48.1% vs. 14.3% vs. 0%, P=0.001). In the IL6-JAK-STAT3-High group had a smaller maximum tumor diameter (4.00 cm [IQR 3.00, 5.15] vs. 4.50 cm [IQR 3.00, 6.00] vs. 3.00 cm [IQR 2.15, 3.90], P=0.022), and a lower frequency of pseudoglandular pattern (48.1% vs. 41.2% vs. 13.4%, P=0.041). No significant differences were observed in etiology, NLR, CRP, tumor markers, tumor differentiation, or microtrabecular structure frequency. The K-RAS-High group showed intermediate traits between those of the other two groups. RNA analysis revealed complementary ALB and GLUL expression (Fig. 3B). GLUL expression was elevated in the Wnt/β-catenin-High group, while ALB was higher in the IL6-JAK-STAT3-High group. Supplementary Figure 4B illustrates immune cell proportions and tumor immune microenvironment (TIME). The IL6-JAK-STAT3-High group had significantly higher immune/stromal score calculated by ESTIMATE [26], total immune cell counts, CD8+ T cell counts by quanTIseq algorithm [27,28], and CD8+ T cell counts by microscopy. The OS and RFS rates are shown in Figure 3C. The median OS was 51.4 months (45.7–123.2) for the Wnt/β-catenin-High, 80.3 months (66.9–NA) for the K-RASHigh, and 156.8 months (88.1–NA) for the IL6-JAK-STAT3-High group (P=0.06). The median PFS was 29.3 months (12.4–61.6) for the Wnt/β-catenin-High, 15.9 months (12.5– 29.3) for the K-RAS-High, and 26.1 months (14.6–NA) for the IL6-JAK-STAT3-High group (P=0.05).
Reclassification of the glycolysis subclass
Cluster analysis reclassified HCCs into the glycolysis subclass (n=54, 39.7%) using Hallmark signaling pathways from MSigDB. The optimal classification was based on the highest silhouette coefficient when applying four pathways: HALLMARK_MTORC1_SIGNALING, HALLMARK_ NOTCH_SIGNALING, HALLMARK_PI3K_AKT_MTOR_ SIGNALING, and HALLMARK_TGF_BETA_SIGNALING (Supplementary Table 3B). The hierarchical clustering results are presented in Figure 4, with silhouette analysis in Supplementary Figure 5A. The two subgroups identified were PI3K/mTOR-High (n=28, 20.6%) and NOTCH/TGF-β-High (n=26, 19.1%). Supplementary Table 7 outlines the patient characteristics of these subgroups. The PI3K/mTOR-High group had higher proportion of males (85.7% vs. 57.7%, P=0.046), larger tumor sizes, more frequent vascular invasion, and elevated tumor markers. No signifi-cant differences in histopathological features or genetic mutations were observed between the two groups. Immune cell infiltration, as estimated by the quanTIseq algorithm (Supplementary Fig. 5B), showed no significant differences in CD8+ T and regulatory T cell (Treg) counts, and ESTIMATE immune/stromal scores. The OS and RFS comparisons are shown in Supplementary Fig. 5C. The median OS was 51.8 months for the PI3K/mTOR-High (95% CI 21.6–NA) and 97.3 months (95% CI 50.6–NA) for the NOTCH/TGF-β-High group. The median RFS was 10.2 months (95% CI 3.6–29.1) for the PI3K/mTOR-High, and 10.3 months (95% CI 6.5–36.7) for the NOTCH/TGF-β-High group, with no significant differences.
Characteristics within the five subclasses
We present the patient characteristics, histopathological features, and genetic mutations for the five HCC subclasses (Fig. 5A, Table 2, and Supplementary Table 8). Representative samples from each group were evaluated using hematoxylin and eosin (H&E) staining, and IHC for Ep-CAM, CK19, and carbonic anhydrase 9 (CA9) are shown in Figure 5B, 5C. The approximate curves of enrichment scores and expression levels of metabolism-related genes (GLUL, CYP3A4, APOB, ALB) are shown in Supplementary Figure 6A. Supplementary Figure 6B illustrates MYC family genes, which are involved in cell proliferation and apoptosis, hypoxia-inducible factors (HIF1A, CA9, ESM1), and glucose transporters (SLC2A1-5). A comparison of the TIME is depicted in Supplementary Figure 6C, showing CD8+ T cells, Tregs, and M1/M2 macrophages. Supplementary Figure 6D provides ridge plots of the Hallmark gene set enrichment scores, and OS and RFS data are in Supplementary Figure 6E.
The Wnt/β-catenin-High group (n=27, 19.9%) is a part of the rich metabolism subclass and has a significantly higher frequency of CTNNB1 (D32–S37) mutations. This group exhibited a higher prevalence of normal hepatocyte-like cell arrangements (microtrabecular patterns, P<0.001), and pseudoglandular structures (48.1%, P<0.001). It had the highest proportion of males (92.6%, P=0.011), and the lowest values for AFP (4.00 ng/dL [IQR 3.00, 61.50]) and DCP (90.50 mAU/mL [IQR 28.0, 213.5], both P<0.05). Tumors in this group rarely showed poorly differentiated features (14.8%, P<0.001) or positive for biliary stem cell markers EpCAM, CK19, and SALL4 (P<0.001), with 85.1% corresponding to the Hoshida S3 subclass. The ESTIMATE immune/stromal scores were the lowest, and IHC revealed low expression of immune checkpoint molecules: 0% antiprogrammed cell death protein 1 (PD-1) positivity, 11.1% programmed death-ligand 1 (PD-L1) positivity, 0% lymphocyte-activation gene (LAG)-3 positivity, and 0% T-cell immunoglobulin mucin (TIM)-3 positivity (all P<0.05). The RNA expression of GLUL and CYP3A4 was high, whereas MYC family genes, hypoxia-inducible factors, and glucose transporters were low. Hallmark enrichment scores indicat-ed higher activity in DNA repair-related pathways and low levels in inflammatory response, interferon gamma response, epithelial-mesenchymal transition (EMT), and angiogenesis.
The IL6-JAK-STAT3-High group (n=34, 25.0%), also a part of the rich metabolism subclass, had the smallest median tumor diameter at 3.00 cm (IQR 2.15, 3.90) (P=0.005), the lowest incidence of vascular invasion (14.7%, P=0.001), and relatively low tumor marker levels, with AFP at 7.00 ng/dL (IQR 3.25, 72.00) and DCP at 80.00 mAU/mL (IQR 25.5, 304.0) (P<0.05). Poorly differentiated HCC were rare (17.6%) and pseudoglandular structures were uncommon (13.4%). This group showed a higher proportion of steatohepatitis HCC (16.7%, P=0.098) and Hoshida S3 subclass (76.5%, P<0.001). Although 29.4% had Wnt/β-catenin activating mutations, none involved the D32–S37 region of CTNNB1 (P<0.001). TIME analysis indicated high ESTIMATE immune/stromal scores and M1 macrophage infiltration. RNA showed relatively high ALB, low GLUL, and CYP3A4 expression, and low MYC family genes, hypoxiainducible factors, and glucose transporters. The Hallmark scores were high for inflammatory and interferon-gamma responses, and this group had the best RFS among the five groups (P=0.02). The K-RAS-High group (n=21, 15.4%) exhibited a phenotype intermediate between the Wnt/β-catenin-High and IL6-JAK-STAT3-High groups.
The PI3K/mTOR-High group (n=28, 20.6%), a part of the glycolysis subclass, had the largest median tumor diameter (5.50 cm [IQR 3.00, 10.35], P=0.005) and a high incidence of vascular invasion (60.7%, P=0.001). Poorly differentiated HCC was common (64.3%, P<0.001), with a predominance of compact (51.8%, P=0.031) and MTM (25.9%, P=0.001) patterns, and loss of microtrabecular structures. This group had a higher frequency of TP53/cell cycle control mutations (57.1%, P=0.091) and Hoshida S1/2 subclass (96.4%, P<0.001). This group elevated NLR of 2.57 (IQR 1.73, 4.25), and the highest median values for CRP (0.53 mg/L [IQR 0.14, 4.21]), AFP (333.0 ng/dL [IQR 18.00, 4068.3]), and DCP (729.0 mAU/mL [IQR 47.5, 10714.5]) (P<0.05). RNA analysis revealed downregulation of metabolic genes (GLUL, CYP3A4, APOB, ALB) and upregulation of hypoxia-inducible factors and glucose transporters. IHC revealed CA9-positive staining. The Hallmark signatures related to DNA repair and the G2M checkpoint were significantly en-riched. PD-1 and PD-L1 positivity rates of 32.1% and 42.9%, respectively. The ESTIMATE immune/stromal scores were mixed, with both high and low scores observed within this group.
The NOTCH/TGF-β-High group (n=26, 19.1%), a part of the glycolysis subclass, had few males (57.7%) and a higher median NLR of 2.17 (IQR 1.69, 3.95). Poorly differentiated HCC was observed in 38.5%, with higher frequencies of compact (44.0%), scirrhous (12.5%), and MTM (8.3%) patterns. All patients were classified as Hoshida S1/2. Gene expression showed downregulation of metabolic genes (GLUL, CYP3A4, APOB, ALB), and upregulation of MYC family genes, hypoxia-inducible factors, and glucose transporters (SLC2A1, SLC2A3, and SLC2A5). IHC revealed CA9-positive staining. The Hallmark enrichment scores were high for DNA repair and angiogenesis. In TIME, there was reduced CD8+ T-cell infiltration, slightly increased Tregs, and upregulation of immune checkpoint molecules (PD-1: 26.9%, PD-L1: 38.5%, LAG-3: 34.6%, TIM-3: 34.6%, all P<0.05).
External validation
External validation analysis was conducted on TCGA-LIHC (n=373), ICGC-LIRI-JP (n=203), GSE14520 (n=225), and GSE76427 (n=115) cohorts. These external cohorts were classified into five HCC subclasses based on metabolic functions and signaling pathway activation, following the same methodology applied to the training cohort. The results are shown in Figure 5D. Across all cohorts, subclasses were identified based on either enhanced glycolytic pathway activity or elevated bile acid, fatty acid, and xenobiotic metabolism. Notably, glycolysis and liver-specific metabolic functions were mutually exclusive. The rich metabolism subclass was predominantly associated with the Hoshida S3 subclass, while the glycolysis subclass corresponded mainly to the S1/S2 subclass. Furthermore, comparison of OS between the two groups revealed a trend toward poorer prognosis in the glycolysis subclass (shown in Fig. 5E). Within the rich metabolism subclass, three distinct groups were identified based on the activation of Wnt/β-catenin, K-RAS, and IL6-JAK-STAT3 signaling pathways. The Wnt/β-catenin-High group exhibited low ESTIMATE immune and stromal scores, whereas the IL6-JAK-STAT3-High group demonstrated elevated scores. Conversely, in the glycolysis subclass, two subgroups were identified based on activation of PI3K/mTOR and NOTCH/TGF-β signaling pathways, with ESTIMATE immune and stromal scores displaying heterogeneity.
Furthermore, we performed GSEA using each dataset to examine the association between metabolic pathways. The metabolic pathways analyzed included amino acid metabolism/catabolism, gluconeogenesis, and urea cycle, which are critical for metabolic reprogramming in cancer. Our findings revealed distinct subclass-dependent alterations in these pathways. The dot plot visualization demonstrated significant variations in normalized enrichment scores (NES) and adjusted P-values (–log10 transformed) among five subclasses, suggesting differential metabolic regulation in each subgroup (Fig. 5F). Meta-analysis across six datasets revealed that NOTCH/TGF-β-High exhibited a suppressive trend in all pathways, whereas IL6-JAKSTAT3-High showed significant activation in gluconeogenesis, urea cycle, amino acid metabolism, and amino acid catabolism (FDR <0.05) (see Supplementary Table 10, Fig. 5F).
As a representative example, the analysis workflow for TCGA-LIHC is presented in Supplementary Figure 7A–7D. Detailed patient characteristics are shown in Supplementary Table 9. Notably, the IL6-JAK-STAT3-High group exhibited a higher frequency of mutations in the JAK/STAT signaling genes (11.0%, P=0.039) and a greater prevalence of HCV infection (P=0.043).
DISCUSSION
This study elucidated that the newly acquired glucose metabolism program (glycolysis) in tumors and the maintenance of metabolic functions characteristic of normal hepatocytes were mutually exclusive. This metabolic shift closely aligns with histopathological features, such as disrupted cellular arrangement and nuclear pleomorphism. When the loss of liver-specific metabolism in cancer cells is defined as physiological dedifferentiation, the process of cellular dedifferentiation may exhibit both pathological and physiological duality (see graphic abstract). This concept, which has received limited attention, may offer a critical perspective in discussing tumor plasticity.
Pathological and physiological insights into differentiated or dedifferentiated HCC subclasses
In this study, HCC was classified into two major subtypes based on physiological-pathological differentiation and dedifferentiation. The first subtype is “rich metabolism” subclass, characterized by small tumor size, low serum tumor marker levels, a microtrabecular pattern, and well-to-moderately differentiated tumors (Fig. 6A). These tumors correspond to Hoshida S3, with homogeneous APHE, suggesting perfusion by uniform tumor blood flow [9]. They also exhibit higher enhancement nodules on hepatobiliary phase (HBP) images of Gd-EOB-DTPA-enhanced MRI and low 18F-FDG uptake on PET-CT, with downregulated glucose transporter gene expression. GO and KEGG analyses revealed enrichment in drug, fatty acid, and bile acid metabolism.
The second subtype is “glycolysis” subclass with suppressed liver-specific metabolic function, consisting of larger tumors, high serum AFP levels, compact or MTM types, and poorly differentiated features (Fig. 6A). These tumors correspond to Hoshida S1/2 and are frequently associated with TP53 mutations, upregulated cell cycle pathways, positive stem cell markers, high proliferative activity, and increased vascular invasion, reflecting their aggressive behavior. Imaging studies reveal inhomogeneous or rim-APHE, suggesting partly hypoxic blood flow regulation [9]. These tumors exhibit hypointense in HBP on Gd-EOB-DTPA-enhanced MRI and increased 18F-FDG uptake on PETCT, with high expression of glucose transporter genes, particularly SLC2A1, SLC2A3, and SLC2A5. They also show elevated levels of hypoxia-inducible factors. KEGG pathway analysis demonstrated upregulated glycolysis with abundant lactate production.
Among the highly differentiated HCC subgroup, the Wnt/β-catenin-High group often has CTNNB1 mutation in exon 3 (D32–S37 region), pseudoglandular structures, and a higher male prevalence, aligning with reports of androgen receptor influence on these structures under Wnt/β-catenin signaling [29]. Its TIME often exhibits an immune desert/excluded phenotype, consistent with previous studies [30-32]. This subclass is marked by preserved metabolic functions, including ammonia (GLUL), drug (CYP3A4), fatty acid (APOB), and bile acid metabolism (Fig. 3B, Supplementary Fig. 6A). In perivenous normal hepatocytes, Wnt/β-catenin activation drives differentiation into cells for bile acid, fatty acid, ammonia, and drug metabolism [33].
In contrast, HCCs with activated K-RAS or IL6-JAKSTAT3 signaling show upregulated ALB expression, enhanced gluconeogenesis and urea cycle activity, while GLUL, CYP3A4, and APOB are downregulated, resembling periportal hepatocytes (Fig. 3B, Supplementary Fig. 6A). The IL6-JAK-STAT3-High group often includes smaller steatohepatitic-type HCCs, with immune cell-rich TIME, enhanced IFN-γ signaling, and favorable prognosis and features frequent JAK/STAT pathway mutation. Periportal normal hepatocytes influenced by RAS signaling activation and Wnt/β-catenin signaling suppression acquire the phenotype characterized by active albumin production [33].
Among the poorly differentiated HCC subgroup, subclasses with activated NOTCH/TGF-β signaling pathways exhibited increased CK19 expression and a higher prevalence of scirrhous-type HCC phenotypes, which resemble iCCA. Conversely, activation of the PI3K/mTOR signaling pathway was associated with features reminiscent of fetal liver and an aggressive phenotype with vascular invasion. Activation of TGF-β and NOTCH pathways is linked to increased HNF1B and decreased HNF4A, associated with iCCA development [19]. HCC subclasses resembling iCCA, including combined HCC/iCCA and scirrhous-type HCC, activate these pathways [34,35] and show glandular structures, abundant extracellular matrix, and the expression of CK19, EPCAM, and EMT-related genes. Moreover, Boyault G3, known for MTM features, also activates the MAP Kinase and PI3K/AKT pathways, similar to the Boyault G1/G3 [22].
We previously emphasized the importance of reclassifying HCC by tumor metabolic functions, highlighting that similar signaling pathways regulate both HCC and normal hepatobiliary cell phenotypes via the liver zonation program [19]. These observations align with prior reports on HCC classification based on liver zonation principles [18]. However, our study extends this framework by incorporating an evaluation of tumor metabolic function. Interestingly, our study also demonstrated that HCC subclasses closely resemble hepatocellular adenoma (HCA). HCCs with activated Wnt/β-catenin signaling shared features with CTNNB1-mutated HCAs, including bile acid and ammonia metabolism. Moreover, HCCs with activated IL6-JAK-STAT3 signaling pathways resembled inflammatory HCAs in their specific signaling pathway and TIME (Supplementary Fig. 8).
We revealed that the signaling pathways involved in maturation of fetal to adult liver and liver zonation in normal hepatocytes may play a key role in determining the diversity of HCC phenotypes. This framework provides a compelling perspective for understanding the spatial and temporal heterogeneity of HCC. Results from all five cohorts collectively summarize the defining characteristics of these five HCC subclasses (Fig. 6B).
Glycolytic pathway and expression of hypoxiainducible factors and glucose transporters
Cancer cells generate ATP not only through mitochondrial oxidative phosphorylation but also via glycolysis, even under aerobic conditions [4]. This Warburg effect is characterized by high glucose transporter expression and enhanced glycolysis. Glucose is metabolized into fructose-6-phosphate and fructose-1,6-bisphosphate, producing abundant pyruvate and lactate. The glycolysis subclass exhibited high expression of hypoxia-inducible factors (e.g., HIF1A, CA9), and glucose transporters (e.g., GLUT1 [SLC2A1], GLUT3 [SLC2A3], GLUT5 [SLC2A5]) (Supplementary Fig. 6B). KEGG pathway mapping revealed activation of key glycolytic enzymes, including hexokinase, phosphofructokinase, glyceraldehyde-3-phosphate dehydrogenase, phosphoglycerate kinase 1/2, phosphoglycerate mutase 1/2, enolase 1/2/3, and pyruvate kinase M1/2 (Supplementary Fig. 3A, 3B), indicating rapid glucose conversion to pyruvate in tumor cells. This metabolic environment also affects TIME, creating a survival environment favorable to immunosuppressive cells such as Tregs [5].
Metastatic liver tumors exhibit increased expression of glycolysis-related molecules via upregulation of HIF1A expression, leading to elevated lactate levels [35]. Hypoxia triggers metabolic reprogramming, shifting from oxidative phosphorylation to glycolysis. HIF1A activation, often due to cancer-related mutations, promotes therapeutic resistance [36] and upregulates the aldolase A, a key enzyme that supports tumor growth [37]. The correlation between glucose transporters, particularly GLUT1 and GLUT3, and 18F-FDG uptake in HCC is well established, with high accumulation detectable by 18F-FDG/PET-CT [38-40]. Gene expression related to glycolysis and the HIF1A signaling also correlates with 18F-FDG uptake, consistent with our findings [6]. Enhanced glycolysis and lipid metabolism are mutually exclusive, as observed in our study [6]. Additionally, increased expression of genes related to the mTOR pathway correlates with 18F-FDG uptake [41], consistent with the PI3K/mTORHigh subclass in our cohort. These findings suggest that HCC with upregulated glucose transporter expression and activated glycolysis may correspond to PET-positive HCC. However, future research on HCC metabolic functions with high 18F-FDG uptake is required.
Limitation
Despite these important considerations, several limitations exist. First, although we reclassified HCC based on metabolic functions and signaling pathways and demonstrated their correlations, we could not establish a causal relationship. Specifically, we did not examine whether activation of these signaling pathways directly induces phenotypic changes in HCC cells. Second, although we showed a correlation between the preservation of metabolic functions and suppressed glycolysis and conversely, the loss of metabolic functions with enhanced glycolysis and increased hypoxia-inducible factor expression, the mechanisms linking these processes remain unclear, and the precise pathways driving metabolic changes in HCC were not explored. Third, the number of 18F-FDG/PET-CT cases was limited, and the association between glycolysis and the imaging findings was not thoroughly investigated. Further studies are required to elucidate these relationships.
Conclusion
In HCC, pathological dedifferentiation is associated with the loss of liver-specific metabolism and a shift towards a glycolysis-dominant metabolic environment. This shift is driven by the absence of homogeneous tumor blood flow and increased expression of hypoxia-inducible factors and glucose transporters. This pathological and physiological dedifferentiation stratifies HCC into two major subtypes: well-to-moderately differentiated HCC, in which key signaling pathways involved in liver zonation define the phenotype, and dedifferentiated HCC, which exhibits tumor characteristics resembling fetal liver or biliary epithelial cells. These findings highlight the role of hepatocyte-specific maturation processes in shaping the phenotypic diversity of HCC.
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