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. 2025 Dec 28;117(3):670–682. doi: 10.1111/cas.70296

Targeting SPP1‐CD44‐Hedgehog Axis Elicits Therapeutic Effects in Hepatocellular Carcinoma by Suppressing Intratumoral Fibrosis

Atsushi Nara 1,2, Shu Shimada 1,✉, Yoshimitsu Akiyama 1, Megumi Hatano 1, Yusuke Chino 3, Suguru Miyazawa 1,2, Hanako Tamura 1,2, Daisuke Asano 2, Yoshiya Ishikawa 2, Hiroki Ueda 2, Shuichi Watanabe 2, Eriko Katsuta 2, Keiichi Akahoshi 2, Kenichi Ohashi 3, Minoru Tanabe 2, Daisuke Ban 2, Shinji Tanaka 1,2,✉
PMCID: PMC12951113  PMID: 41456866

ABSTRACT

Advanced hepatic fibrosis is a major risk factor for cirrhosis and hepatocellular carcinoma (HCC), and it is required to identify a key mediator involved in intratumoral fibrosis and HCC development. Transcriptomic analysis of 372 HCC samples using publicly available datasets revealed that SPP1 was significantly upregulated in fibrotic HCC tissues and associated with unfavorable outcomes. Immunohistochemical analysis of 103 HCC tissues and single‐cell RNA sequencing (scRNA‐seq) analysis of 228,564 live cells identified SPP1 overexpression in HCC cells, which strongly correlated with intratumoral fibrosis. In xenograft models, HCC cells with SPP1 overexpression (SPP1‐OE) exhibited enhanced fibrosis and tumor growth. Coculture assay demonstrated that SPP1‐OE cells stimulated hepatic stellate cells (HSCs), and gene set enrichment analysis and differential gene expression analysis elucidated the activation of the Hedgehog signaling pathway and upregulation of GLI1 in HSCs. Cell–cell interaction prediction analysis using scRNA‐seq data suggested that SPP1‐CD44 signaling transduction might contribute to HSC activation. Pharmacological inhibition of GLI1 with the SMO inhibitor vismodegib suppressed HSC activation in vitro and reduced fibrosis and tumor growth in vivo. These findings indicate that SPP1 promotes intratumoral fibrosis and HCC progression through the SPP1‐CD44‐GLI1 axis, highlighting its potential as a prognostic biomarker and therapeutic target. Inhibition of SPP1‐CD44‐Hedgehog signaling may provide a promising strategy to mitigate fibrosis and improve HCC patient outcomes.

Keywords: CD44, hedgehog, hepatic stellate cells, intratumoral fibrosis, SPP1

SPP1 was identified as a key driver of intratumoral fibrosis and hepatocellular carcinoma (HCC) progression through transcriptomic, histological, and functional analyses. Mechanistically, SPP1 promotes HSC activation and fibrosis via the SPP1–CD44–GLI1 axis, and pharmacologic inhibition of Hedgehog signaling effectively reduces tumor growth and fibrosis, highlighting a potential therapeutic strategy.

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Abbreviations

AFP

α‐fetoprotein

ECM

extracellular matrix

FDA

Food and Drug Administration

HBV

hepatitis B virus

HCC

hepatocellular carcinoma

HCV

hepatitis C virus

HSC

hepatic stellate cell

MALC

metabolism‐associated liver cancer

MASH

metabolic dysfunction‐associated steatohepatitis

MASLD

metabolic dysfunction‐associated steatotic liver disease

OS

overall survival

RFS

recurrence‐free survival

scRNA‐seq

single‐cell RNA sequencing

siRNA

small interfering RNA

SPP1‐OE

SPP1‐overexpressing

SVR

sustained virological response

TCGA

The Cancer Genome Atlas Research Network

WHO

World Health Organization

1. Introduction

Hepatocellular carcinoma (HCC) accounts for 75%–85% of primary liver cancers and is associated with various risk factors including chronic hepatitis B virus (HBV) and hepatitis C virus (HCV) infections, alcohol abuse, and metabolic disorders [1]. The residual risk of post‐sustained virologic response (SVR) HCC and the rising prevalence of metabolism‐associated liver cancer (MALC) remain substantial challenges. Despite the achievement of SVR through interferon‐ and direct‐acting antiviral‐based treatments, the 5‐year cumulative incidence of HCC is approximately 2%–8%, and predictive factors for post‐SVR HCC including liver fibrosis and FIB‐4 index have been highlighted [2, 3]. In contrast, the incidence rate of HCC is approximately 5.29 per 1000 person‐years in patients with metabolic dysfunction‐associated steatotic liver disease (MASLD), with the rate reaching 11%–13% over 5 years in those who progress to metabolic dysfunction‐associated steatohepatitis (MASH)‐related cirrhosis [4]. Fattovich et al. [5] have reported that 80%–90% of hepatocellular carcinoma (HCC) cases arise from fibrotic livers, underscoring that fibrosis is a crucial risk factor for hepatocellular carcinogenesis.

Liver fibrosis is characterized by the formation of fibrotic scars due to the accumulation of extracellular matrix (ECM) proteins, primarily type I and type III collagens, in which hepatic stellate cells (HSCs) play critical roles. Liver injury activates HSCs, resulting in their transformation into myofibroblasts that produce type I collagen, migration to the injury sites, and secretion of ECM components that drive fibrosis [6]. Given that various mediators, such as osteopontin, TGFβ, TNFα, PDGF, and IL‐1β, induce the activation and proliferation of HSCs, targeting these key pathways holds promise as a potential treatment option for liver fibrosis [7]. Cytoglobin and FGF18 have recently been identified to suppress HSC activity, ameliorating fibrosis [8, 9]. SPP1, which encodes osteopontin, is implicated in reducing the efficacy of immune checkpoint inhibitors and is suggested to attenuate the therapeutic response to atezolizumab plus bevacizumab [10, 11]. However, the detailed molecular mechanisms remain unclear, and effective therapeutic strategies are still under development.

To address this issue, the present study initially explored genes expressed in liver cancer cells and closely connected to fibrosis using publicly available transcriptomics provided by The Cancer Genome Atlas Research Network (TCGA). Among genes upregulated in tumors from patients with high Ishak fibrosis scores [12], SPP1 was most strongly associated with poor prognosis. Immunohistochemical analysis of clinical samples further confirmed that SPP1 expression was also markedly elevated in fibrotic HCC specimens. Moreover, single‐cell analysis of HCC tissues and cell–cell interaction analysis of HCC cells and HSCs clarified that the SPP1‐CD44‐GLI1 axis contributes to the activation of HSCs and the promotion of intratumoral fibrosis and tumor growth.

2. Materials and Methods

2.1. Statistics

Statistical analysis was performed using Python (Python Software Foundation, Wilmington, DE, USA), R (R Foundation for Statistical Computing, Vienna, Austria), EZR (Saitama Medical Center, Jichi Medical University, Saitama, Japan), or GraphPad Prism (GraphPad Software, La Jolla, CA, USA). p < 0.05 was considered statistically significant. p values were calculated by the χ 2 test to examine the relationship between categorical variables. p values were calculated by the log‐rank test or generalized Wilcoxon test to compare patient survival. The Cox proportional hazards model was employed for univariate and multivariate analyses. Variables with p < 0.05 in the univariate analysis were included in the multivariate analysis with bidirectional stepwise regression. For parametric data, p values were calculated by the Welch's t‐test or ANOVA with the Tukey–Kramer post hoc test, and data are the mean ± SD in the figures. For nonparametric data, p values were calculated by the Mann–Whitney U‐test or the Kruskal–Wallis test with the Steel‐Dwass post hoc test, and boxes represent the 25th, 50th, and 75th percentiles in the figures.

Detailed information can be found in Data S1 including Table S1.

3. Results

3.1. Identification of Genes Related to Fibrosis in HCC Tissues

We surveyed genes playing essential roles in fibrosis using the TCGA dataset. First, we assessed gene expression patterns between groups with the high (fibrosishigh) and low (fibrosislow) Ishak fibrosis scores and identified 59 upregulated and 93 downregulated genes in the fibrosishigh group compared to the fibrosislow group (Figure 1A; |log2 fold‐change| > 1 and p < 0.05). Prognostic analysis revealed that 15 upregulated and 21 downregulated genes were importantly associated with adverse outcomes (p < 0.05). Among these 36 candidates, we focused on SPP1 as the gene upregulated in the fibrosishigh group, which was most significantly associated with poor patient prognosis (Figure 1B,C).

FIGURE 1.

FIGURE 1

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Association between SPP1 expression and fibrosis in human HCC clinical specimens. (A) Volcano plot of gene differentially expressed between groups with the high (fibrosishigh; n = 110) and low (fibrosislow; n = 107) Ishak fibrosis scores in tumor tissues using the TCGA dataset. (B) List of the top five genes significantly upregulated or downregulated in the fibrosishigh group. (C) Kaplan–Meier curves of overall survival in 372 HCC patients from the TCGA dataset, stratified by SPP1 expression. p value was calculated by the log‐rank test. (D) Representative immunohistochemical images of SPP1 in HCC specimens surgically resected at our institute. HE, hematoxylin and eosin staining; N, adjacent liver tissue; T, tumor tissue. (E) Kaplan–Meier curves of recurrence‐free survival and overall survival in 103 HCC patients undergoing surgical resection at our institute, stratified by SPP1 expression. p values are calculated by the log‐rank test. (F) Forest plots showing the hazard ratios and 95% confidence intervals calculated from multivariate Cox regression analysis for recurrence‐free survival and overall survival. DCP, des‐γ‐carboxy prothrombin; PV, portal vein.

3.2. Histological Evaluation of SPP1 Expression in Human HCC Clinical Samples

To investigate the expression pattern and clinicopathological relevance of SPP1, immunohistochemical staining of SPP1 protein was performed on 103 HCC specimens collected at our institute. SPP1 was rarely expressed in normal liver tissues but was overexpressed in 34 HCC tissues (Figure 1D). The SPP1high group demonstrated poorer recurrence‐free survival (RFS) and overall survival (OS) than the SPP1low group (Figure 1E), which is consistent with the findings from the bioinformatics analysis (Figure 1C). Clinical and pathological examination of the SPP1high HCC group revealed a strong correlation not only with liver and tumor fibrosis, both of which were linked to unfavorable RFS and OS (Figure S1), but also with tumor number and portal vein invasion (Table S2). Notably, scirrhous HCC was frequently observed in SPP1high HCC group, which is a rare, aggressive subtype of HCC characterized by microscopic signs of intratumoral fibrosis [13] and poorer clinical outcomes than other histological types of HCC [14, 15, 16, 17]. Furthermore, univariate and multivariate analyses revealed that elevated SPP1 expression was an independent unfavorable prognostic factor, along with tumor size and differentiation (Tables S3 and S4 and Figure 1F).

3.3. Tumor Cell‐Derived SPP1 in HCC Progression and Fibrosis

Although the important roles of SPP1 in hepatocarcinogenesis have been reported [10, 11], it remains controversial which cell types, such as epithelial (cancer) cells or macrophages, exhibit high SPP1 expression in HCC tissues [18]. To address this controversy, we integrated six scRNA‐seq datasets and annotated each cell type, and UMAP plots illustrated SPP1 overexpression in both ALB‐positive cancer cells and CD68‐positive macrophages (Figure 2A). Because SPP1‐positive HCC cells were detected more frequently than SPP1‐positive macrophages in immunohistochemical and bioinformatics analyses (Figures 1D and 2B), we investigated the biological roles and molecular mechanisms of SPP1 released from HCC cells. Since the protein expression level was higher in JHH4 cells than in other HCC cell lines (Figure 2C), we derived SPP1‐overexpressing subclones (SPP1‐OE) from HuH7, HLF, and JHH5 cell lines (Figure 2D) and observed higher proliferation, migration, and invasion abilities in SPP1‐OE cells compared to control cells (Figure S2).

FIGURE 2.

FIGURE 2

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Biological effects of SPP1 overexpression in liver cancer cells in vivo. (A) UMAP plots showing gene expression patterns of 228,564 live cells across six scRNA‐seq datasets. ALB and CD68 serve as markers for epithelial (cancer) cells and macrophages, respectively. (B) Box plots showing the expression level of SPP1 for each cell type. Only 10% of cells are displayed for clarity. Colors indicate cell types as follows: dark blue for epithelial cells (Epi), orange for fibroblasts (FB), green for endothelial cells (Endo), red for helper T cells (Th), purple for cytotoxic T cells (CTL), brown for cytotoxic T cells and natural killer cells (CTL, NK), pink for regulatory T cells (Treg), light green for B cells (B), sky blue for plasma cells (PC), and pale blue for macrophages (MΦ). (C) Western blot analysis of SPP1 in human HCC cell lines. (D) Western blot analysis of SPP1 in SPP1‐OE cells. (E) Tumorigenicity assay of HuH7‐SPP1‐OE cells in immunodeficient mice (n = 8, respectively). Representative photo images of tumor tissues derived from HuH7‐SPP1‐OE cells are shown in the right panel. Data are the mean ± SE. p value was calculated by the Welch's t‐test. (F) Representative immunohistochemical photo images (left) and quantitative data (right) of SPP1 in tumor tissues derived from HuH7‐SPP1‐OE cells. The area proportion of SPP1‐positive cells was evaluated at ×100 magnification in six different fields for each tumor. p value was calculated by the Welch's t‐test.

To determine the biological effects of SPP1 on tumor growth and the tumor microenvironment, HuH7‐SPP1‐OE cells were subcutaneously injected into immunodeficient mice, resulting in a substantial increase in tumor volume (Figure 2E). Immunohistochemical analysis confirmed upregulation of SPP1 expression in the transplanted tumor specimens of HuH7‐SPP1‐OE cells. Additionally, Sirius Red staining showed enhancement of fibrosis in HuH7‐SPP1‐OE tumor tissues (Figure 2F), consistent with the findings from human SPP1high HCC samples (Figure 1D). Taken together, SPP1‐expressing HCC cells exhibit a malignant phenotype both in vitro and in vivo and contribute to intratumoral fibrosis.

3.4. HSC Activation by Coculture With SPP1‐Overexpressing Liver Cancer Cells

To evaluate the biological effect of SPP1 on liver cancer fibrosis, we examined the interaction between SPP1‐expressing HCC cells and HSCs, which primarily contribute to liver fibrosis. First, we conducted a transwell assay using conditioned medium from HuH7‐SPP1‐OE cells on two human HSC lines (LX2 and TWNT4) and a rat HSC line (HSCT6) and discovered that the conditioned medium containing SPP1 protein notably attracted all three HSC lines by increasing their migratory activity (Figure 3A). To further explore the direct interaction between HCC cells and HSCs, we introduced lentiviral vectors for ectopic expression of GFP and mCherry fluorescent proteins into HuH7‐SPP1‐OE cells and LX2 or TWNT4 cells, respectively, and cocultured these two cell types. Quantitative analysis of their proximity revealed that HuH7‐SPP1‐OE cells proliferated and recruited HSCs (Figure 3B).

FIGURE 3.

FIGURE 3

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HSC activation by coculture with SPP1‐expressing liver cancer cells. (A) Migration assays of HSC lines using conditioned medium from HuH7‐SPP1‐OE cells. Representative photo images and quantitative data are shown in the left and right panels, respectively. The number of cells was evaluated at ×100 magnification in 10 different fields for each sample. Experiments were independently performed three times, and representative data are shown. p values were calculated by the Welch's t‐test. (B) Coculture assays of HSC lines with mCherry expression and HuH7‐SPP1‐OE cells with GFP expression. Representative immunofluorescent images and quantitative data are shown in the left and right panels, respectively. The number and percentage of contacts between HuH7 cells (green) and HSCs (red) were evaluated at ×100 magnification in three different fields for each sample. Experiments were independently performed twice, and representative data are shown. p values were calculated by the Welch's t‐test.

3.5. SPP1‐CD44‐GLI1 Axis in HCC Fibrosis

To clarify the molecular mechanism underlying the interaction between SPP1‐OE HCC cells and HSCs, we cocultured HuH7‐control and HuH7‐SPP1‐OE cells expressing GFP with LX2 cells expressing mCherry for 2 days, followed by separation using flow cytometry (Figure 4A). Gene set enrichment analysis predicted activation of the Hedgehog signaling pathway, and differential gene expression analysis identified upregulation of GLI1 expression in LX2 cells cocultured with HuH7‐SPP1‐OE cells (Figure 4B). GLI1 expression was also elevated in LX2 and TWNT4 cells cocultured with HuH7‐SPP1‐OE cells, exposed to conditioned medium from HuH7‐SPP1‐OE cells, and treated with recombinant human SPP1 protein (Figure 4C). Western blot analysis validated the nuclear accumulation of GLI1 in LX2 cells cultured under the SPP1‐containing conditioned medium (Figure 4D). We investigated GLI1 expression in HCC clinical samples using the TCGA dataset. Comparative analysis revealed a close positive correlation between SPP1 and GLI1 expression levels (Figure 4C; R = 0.28; p = 4.16 × 10−8). Prognostic analysis further demonstrated a negative impact of GLI1 on overall survival (Figure 4D), consistent with the association between high SPP1 expression and poor overall survival (Figure 1C).

FIGURE 4.

FIGURE 4

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GLI1 nuclear translocation in HSCs through SPP1‐CD44 signaling. (A) Representative flow cytometric plots of LX2 cells with mCherry expression (L) and HuH7‐SPP1‐OE cells with GFP expression (H) in coculture. (B) Gene set enrichment analysis (left) and differential gene expression analysis (right) in LX2 cells cocultured with HuH7‐SPP1‐OE cells. In the differential gene expression analysis, the top 10 genes with the highest fold‐change from the significantly enriched gene sets are shown. NES, normalized enrichment score. (C) Comparative analysis between SPP1 and GLI1 expression levels in tumor tissues using the TCGA dataset. R value and p value were calculated using Pearson's correlation. (D) Kaplan–Meier curves of overall survival in 372 HCC patients from the TCGA dataset, stratified by GLI1 expression. p value was calculated by the log‐rank test. (E) Quantitative RT‐PCR analysis of GLI1 in HSC lines cocultured with HuH7‐SPP1‐OE cells (left), exposed to conditioned medium from HuH7‐SPP1‐OE cells (middle), and treated with recombinant human SPP1 protein (right). The expression levels were quantified by the ΔΔCT method and normalized to 18S rRNA. Experiments were independently performed three times. p values were calculated by the Welch's t‐test. (F) Western blot analysis of GLI1 nuclear accumulation in LX2 cells exposed to conditioned medium from HuH7‐SPP1‐OE cells. (G) Sankey diagram of ligand–receptor interactions between HCC cells and HSCs using scRNA‐seq datasets. (H) Quantitative RT‐PCR analysis of CD44 and GLI1 in LX2 cells with CD44 knockdown exposed to conditioned medium from HuH7‐SPP1‐OE cells. The expression levels were quantified by the ΔΔCT method and normalized to 18S rRNA. Experiments were independently performed three times. p values were calculated by ANOVA with the Tukey–Kramer post hoc test. (I) Western blot analysis of GLI1 nuclear accumulation and CD44 in LX‐2 cells.

To determine the cascade between SPP1 and GLI1, we performed CellPhoneDB‐based analysis using the integrated scRNA‐seq datasets (Figure 2A), which predicted SPP1‐CD44 signaling as the most significant ligand–receptor interaction between cancer cells and fibroblasts (Figure 4E). Notably, CD44 has been reported to interact with the Hedgehog signaling pathway [19], further supporting our hypothesis. In line with this, CD44 knockdown reduced the mRNA expression level of GLI1 and impaired its nuclear accumulation (Figure 4F,G), suggesting that the SPP1‐CD44‐GLI1 axis is essential for the signal transduction from HCC cells to HSCs.

Considering the positive feedback loop of the Hedgehog signaling pathway [20], we evaluated the inhibitory effects of genetic and pharmacological interventions targeting this pathway on HSCs. Knockdown of GLI1 and administration of the Food and Drug Administration (FDA)‐approved SMO inhibitor vismodegib diminished the expression level of GLI1 in both HuH7‐control and HuH7‐SPP1‐OE cells (Figure 5A,B). GLI1 inhibition attenuated the recruitment of HSCs cocultured with HuH7‐SPP1‐OE cells but exerted no effect on HSCs cocultured with HuH7‐control cells (Figure 5C,D). Collectively, SPP1 released from HCC cells activates the CD44‐GLI1 cascade, driving the positive feedback loop of the Hedgehog signaling pathway in HSCs.

FIGURE 5.

FIGURE 5

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Impairment of HSC recruitment by GLI inhibition. (A, B) Quantitative RT‐PCR analysis of GLI1 in LX2 cells with GLI1 knockdown (A) and under vismodegib treatment (B) exposed to conditioned medium from HuH7‐SPP1‐OE cells. The expression levels were quantified by the ΔΔCT method and normalized to 18S rRNA. Experiments were independently performed three times. p values were calculated by ANOVA with the Tukey–Kramer post hoc test (A) and the Welch's t‐test (B). (C, D) Coculture assay of GLI1‐knockdown (C) and vismodegib‐treated (D) LX2 cells with mCherry expression and HuH7‐SPP1‐OE cells with GFP expression. Representative immunofluorescent images and quantitative data are shown in the left and right panels, respectively. Veh, vehicle; Vis, vismodegib. The percentage of contacts between HuH7 cells (green) and HSCs (red) was evaluated at ×100 magnification in three different fields for each sample. Experiments were independently performed twice, and representative data are shown. p values were calculated by ANOVA with the Tukey–Kramer post hoc test.

3.6. Attenuation of Tumor Growth and Fibrosis by GLI1 Pharmacological Inhibition

We subsequently examined whether Hedgehog signaling inhibition could attenuate SPP1‐induced tumor fibrosis and progression. After subcutaneous inoculation of HuH7‐control and HuH7‐SPP1‐OE cells into nude mice, we intraperitoneally administered vismodegib to the tumor‐bearing mice. Vismodegib treatment markedly suppressed the tumor growth of HuH7‐SPP1‐OE cells but had no impact on HuH7‐control cells in vivo (Figure 6A), without apparent body weight loss. Sirius Red and type I collagen staining further elucidated that pharmacological inhibition of GLI1 disrupted fibrosis progression (Figure 6B,C). We performed immunohistochemical staining for GLI1 in the subcutaneous xenograft model. Consistent with our in vitro findings (Figure 4E,F), GLI1 expression was markedly elevated in stromal fibroblasts within the SPP1‐OE tumors compared with the control tumors (Figure 6D, left panel). The area proportion of GLI1‐positive fibroblasts was also increased in the SPP1‐OE tumor specimens, which was abolished by vismodegib treatment (Figure 6D, right panel). Therefore, vismodegib treatment holds the potential to suppress SPP1‐driven fibrotic tumors.

FIGURE 6.

FIGURE 6

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Attenuation of fibrosis and tumor growth by GLI1 pharmacological inhibition. (A) Tumorigenicity assay of HuH7‐SPP1‐OE cells in immunodeficient mice treated with vismodegib (n = 12, respectively). Representative photo images of tumor tissues are shown in the middle panel. Data are the mean ± SE. p values were calculated by ANOVA with the Tukey–Kramer post hoc test. Veh, vehicle; Vis, vismodegib. (B) Representative Sirius Red staining images (left) and quantitative data (right) in subcutaneous tumor tissues derived from HuH7‐SPP1‐OE cells in immunodeficient mice treated with vismodegib. The area proportion of Sirius Red‐positive cells was evaluated at ×100 magnification in six different fields for each group. p values were calculated by ANOVA with the Tukey–Kramer post hoc test. (C) Representative type I collagen staining images (left) and quantitative data (right) in subcutaneous tumor tissues derived from HuH7‐SPP1‐OE cells in immunodeficient mice treated with vismodegib. The area proportion of type I collagen‐positive cells was evaluated at ×100 magnification in six different fields for each group. p values were calculated by ANOVA with the Tukey–Kramer post hoc test. (D) Representative GLI1 staining images (left) and quantitative data (right) in subcutaneous tumor tissues derived from HuH7‐SPP1‐OE cells in immunodeficient mice treated with vismodegib. The area proportion of GLI1‐positive cells was evaluated at ×100 magnification in six different fields for each group. p values were calculated by ANOVA with the Tukey–Kramer post hoc test.

3.7. Enhancement of Fibrosis by Tumor‐Secreted SPP1 in the Orthotopic Transplantation Model

To explore the profibrotic effect of SPP1 on the microenvironment within the liver, HuH7 subclones were directly injected into the livers of immunodeficient mice. Four weeks after orthotopic transplantation, tumors developed in both HuH7‐control and HuH7‐SPP‐OE groups. Similar to the subcutaneous tumor model, Sirius Red and type I collagen staining were more intense in the intrahepatic tumor tissues derived from HuH7‐SPP1‐OE cells than in those derived from HuH7‐control cells (Figure 7A,B), indicating that SPP1 promoted fibrosis by activating HSCs. We conducted double immunofluorescence staining for GLI1 (red) and the HSC markers α‐SMA or desmin (green) in the orthotopic liver transplantation model. The merged images demonstrated the localization of GLI1 within HSCs (yellow), and the area proportions of GLI1/α‐SMA double‐positive and GLI1/desmin double‐positive HSCs were increased in the SPP1‐OE transplanted tumors compared with controls (Figure 7C). These findings further support our hypothesis of the SPP1‐CD44‐GLI1 axis in HSCs.

FIGURE 7.

FIGURE 7

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Enhancement of fibrosis by tumor‐secreted SPP1 in the orthotopic transplantation model. (A) Representative Sirius Red staining images (left) and quantitative data (right) in intrahepatic tumor tissues derived from HuH7‐SPP1‐OE cells in immunodeficient mice. The area proportion of Sirius Red‐positive cells was evaluated at ×100 magnification in six different fields for each group. p value was calculated by the Welch's t‐test. (B) Representative type I collagen staining images (left) and quantitative data (right) in intrahepatic tumor tissues derived from HuH7‐SPP1‐OE cells in immunodeficient mice. The area proportion of type I collagen‐positive cells was evaluated at ×100 magnification in six different fields for each group. p value was calculated by the Welch's t‐test. (C) Representative immunofluorescence staining of GLI1 (red) and αSMA (green) images (left) and quantitative data (right) in intrahepatic tumor tissues derived from HuH7‐SPP1‐OE cells in immunodeficient mice. The area proportion of GLI1/αSMA‐double‐positive cells (yellow) was evaluated at ×100 magnification in six different fields for each group. p value was calculated by the Welch's t‐test. (D) Representative immunofluorescence staining of GLI1 (red) and desmin (green) images (left) and quantitative data (right) in intrahepatic tumor tissues derived from HuH7‐SPP1‐OE cells in immunodeficient mice. The area proportion of GLI1/desmin double‐positive cells (yellow) was evaluated at ×100 magnification in six different fields for each group. p value was calculated by the Welch's t‐test.

4. Discussion

SPP1 was initially discovered as a secreted, heavily phosphorylated glycoprotein in osteoblasts, where it is highly expressed to form bone matrix [21]. Further investigations revealed its expression in both epithelial and mesenchymal cells, including smooth muscle cells, macrophages, and activated T cells [22]. SPP1 overexpression is implicated in several liver diseases, such as acute liver failure, chronic hepatitis B and C, alcoholic liver disease [23], and MASLD/MASH, as well as in solid tumors including not only HCC but also lung, breast, gastric, and colorectal cancers [24, 25]. Additionally, Shang et al. [26] have reported that SPP1 exhibits superior diagnostic performance for HCC compared to AFP, particularly demonstrating high sensitivity in the diagnosis of early‐stage HCC. Several reports have indicated that SPP1 promotes tumor formation and metastasis and may be a promising target for HCC treatment [27, 28, 29].

SPP1 is a ligand for multiple receptors including integrins and CD44, facilitating cell survival, migration, and invasion. CD44 is a transmembrane glycoprotein with multiple isoforms [19], and functions as a signaling platform, transmitting signals to membrane‐bound cytoskeletal proteins to regulate the expression levels of genes involved in cell adhesion to the matrix, migration, proliferation, differentiation, and survival [30]. Klement et al. have clarified that mouse colorectal cancer cells suppress CD8+ T cell activation through the SPP1‐CD44 axis, thereby conferring host tumor immune tolerance [31]. CD44 signal transduction intracellularly interacts with various signaling pathways, including the MAPK, PI3K‐AKT, Wnt/β‐catenin, and Hedgehog signaling pathways [29]. Particularly, inhibition of Hedgehog signaling is reported to reverse sorafenib resistance in CD44‐positive HCC by decreasing CD44 levels [32]. Since Hedgehog signaling, once stimulated by the SPP1‐CD44 axis, amplifies itself through the positive feedback loop [20], the present study employed the SMO inhibitor vismodegib to disrupt this loop.

The Hedgehog signaling pathway collaborates with other profibrotic proteins and cytokines, such as SPP1, CTGF, TGFβ, and WISP‐1, to activate fibroblasts and promote fibrosis [33]. In MASH, Hedgehog signaling is also reported to shift HSCs from a quiescent to an active state, leading to fibrosis [34]. SMO antagonists have been developed to inhibit the Hedgehog signaling pathway, and vismodegib has been approved by the FDA for the treatment of advanced basal cell carcinoma [35]. Since SMO inhibition stabilizes intratumoral delivery of gemcitabine and improves prognosis in a pancreatic cancer mouse model [36], a Phase II trial was conducted to evaluate the combination of vismodegib with gemcitabine or nab‐paclitaxel in clinical settings. However, no statistically significant differences were observed in drug delivery or therapeutic efficacy between groups with and without vismodegib [37, 38]. Regarding fibrosis treatment, clinical trials of drugs targeting the Hedgehog signaling pathway are currently underway to improve idiopathic pulmonary fibrosis [39]. Although no Phase II or III clinical trials of vismodegib have been conducted for the treatment of HCC, its clinical benefit may be more pronounced in fibrotic HCC. Moreover, severe hepatotoxicity has not been reported as an adverse effect of vismodegib [35] and dose adjustment is not necessary even in patients with hepatic impairment [40], suggesting its potential feasibility and safety in HCC patients.

In this study, bioinformatics and immunohistochemical analyses indicated a close correlation between SPP1 overexpression and liver/tumor fibrosis, with unfavorable outcomes in HCC. Single‐cell analysis predicted SPP1‐CD44 signaling between HCC cells and HSCs, supported by cell–cell interaction analysis of the two cell types. Differential gene expression analysis estimated GLI1 signaling activated by SPP1‐CD44 signaling, and treatment with vismodegib impaired HSC activation in vitro, inhibiting fibrosis and tumor growth in vivo. Thus, SPP1 may serve not only as a potential surrogate marker for liver/tumor fibrosis but also as a companion diagnostic tool for Hedgehog signaling pathway‐targeted therapy.

Author Contributions

Atsushi Nara: conceptualization, data curation, formal analysis, investigation, methodology, resources, visualization, writing – original draft, writing – review and editing. Shu Shimada: conceptualization, formal analysis, funding acquisition, methodology, project administration, software, supervision, visualization, writing – review and editing. Yoshimitsu Akiyama: methodology, project administration, supervision, writing – review and editing. Megumi Hatano: methodology, project administration, supervision, writing – review and editing. Yusuke Chino: methodology, resources. Suguru Miyazawa: data curation, resources. Hanako Tamura: data curation, resources. Daisuke Asano: writing – review and editing. Yoshiya Ishikawa: writing – review and editing. Hiroki Ueda: writing – review and editing. Shuichi Watanabe: writing – review and editing. Eriko Katsuta: supervision, writing – review and editing. Keiichi Akahoshi: supervision, writing – review and editing. Kenichi Ohashi: methodology, supervision, writing – review and editing. Minoru Tanabe: supervision, writing – review and editing. Daisuke Ban: project administration, resources, supervision, writing – review and editing. Shinji Tanaka: conceptualization, funding acquisition, methodology, project administration, resources, supervision, visualization, writing – original draft, writing – review and editing.

Funding

This work was supported by Grants‐in‐Aid for Scientific Research (A, 19H01055; B, 23H02979, and 23K27670; C, 22K08864 and 25K11997) and Challenging Research (Exploratory, 20K21627, 22K19554, 25K22573, and 25K22639) from the Ministry of Education, Culture, Sports, Science and Technology of Japan; P‐CREATE (JP19cm0106540) and Program for Basic and Clinical Research on Hepatitis (JP23fk0210102, JP25fk0210136, JP25fk0210149, JP25fk0210163, JP25fk0210167, JP25fk0310546, and JP11AA400168) from AMED (Japan Agency for Medical Research and Development); and a Research Grant from the Princess Takamatsu Cancer Research Fund.

Disclosure

Shinji Tanaka is an editorial board member of Cancer Science.

Ethics Statement

Approval of the research protocol by an Institutional Reviewer Board: G2017‐018, Medical Research Ethics Committee for Life Science of Tokyo Medical and Dental University; G2018‐132C5, Medical Research Ethics Committee for Genetic Research of Tokyo Medical and Dental University. All animal procedures were approved by A2019‐263C2, Institutional Animal Care and Use Committee of Tokyo Medical and Dental University.

Consent

All the patients provided informed consent before enrollment and were anonymously coded following ethical guidelines.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Figure S1: Correlation of liver and tumor fibrosis with poor prognosis in HCC patients.

CAS-117-670-s004.pdf (1.9MB, pdf)

Figure S2: Biological effect of SPP1 overexpression on HCC cells in vitro.

CAS-117-670-s005.pdf (2.2MB, pdf)

Table S1: Primer sequences for qRT‐PCR.

CAS-117-670-s001.pdf (115.9KB, pdf)

Table S2: Relationship between SPP1 expression and clinicopathological factors.

CAS-117-670-s002.pdf (100.8KB, pdf)

Table S3: Univariate and multivariate analyses of SPP1 expression and clinicopathological factors contributing to recurrence‐free survival.

CAS-117-670-s006.pdf (129.5KB, pdf)

Table S4: Univariate and multivariate analyses of SPP1 expression and clinicopathological factors contributing to overall survival.

CAS-117-670-s007.pdf (128.8KB, pdf)

Data S1: Materials and methods.

CAS-117-670-s003.pdf (165.1KB, pdf)

Acknowledgments

We gratefully thank Ms. Hiromi Onari for clerical assistance. Plasmids for lentiviral infection (pCMVΔR8.2 and pHCMV‐VSV‐G) were gifted by Prof. Feng Zhang and Prof. Irvin Chen, respectively.

Contributor Information

Shu Shimada, Email: shimada.monc@tmd.ac.jp.

Shinji Tanaka, Email: tanaka.monc@tmd.ac.jp.

Data Availability Statement

All data are available in this manuscript and the Supporting Information. The data supporting the findings of this study are available from the corresponding authors upon reasonable request. Data for RNA sequencing analysis reported in this publication can be accessed in the Gene Expression Omnibus site (GSE290501). Bulk and single‐cell datasets were obtained from the cBioPortal site (Hepatocellular Carcinoma—TCGA, GDC) and the Gene Expression Omnibus site (GSE125449, GSE146115, GSE149614, GSE151530, GSE189903, and GSE242889).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure S1: Correlation of liver and tumor fibrosis with poor prognosis in HCC patients.

CAS-117-670-s004.pdf (1.9MB, pdf)

Figure S2: Biological effect of SPP1 overexpression on HCC cells in vitro.

CAS-117-670-s005.pdf (2.2MB, pdf)

Table S1: Primer sequences for qRT‐PCR.

CAS-117-670-s001.pdf (115.9KB, pdf)

Table S2: Relationship between SPP1 expression and clinicopathological factors.

CAS-117-670-s002.pdf (100.8KB, pdf)

Table S3: Univariate and multivariate analyses of SPP1 expression and clinicopathological factors contributing to recurrence‐free survival.

CAS-117-670-s006.pdf (129.5KB, pdf)

Table S4: Univariate and multivariate analyses of SPP1 expression and clinicopathological factors contributing to overall survival.

CAS-117-670-s007.pdf (128.8KB, pdf)

Data S1: Materials and methods.

CAS-117-670-s003.pdf (165.1KB, pdf)

Data Availability Statement

All data are available in this manuscript and the Supporting Information. The data supporting the findings of this study are available from the corresponding authors upon reasonable request. Data for RNA sequencing analysis reported in this publication can be accessed in the Gene Expression Omnibus site (GSE290501). Bulk and single‐cell datasets were obtained from the cBioPortal site (Hepatocellular Carcinoma—TCGA, GDC) and the Gene Expression Omnibus site (GSE125449, GSE146115, GSE149614, GSE151530, GSE189903, and GSE242889).

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