Frizzled 7 drives amplification of cancer stem-cell subpopulations and the aggressiveness and poor differentiation of human hepatocellular carcinoma

Anaïs Lopez; Alexia Paturel; Nadim Fares; Floriane Pez; Guanxiong Wang; Patricia Gifu; Lydie Lefrançois; Jihed Chouaref; Pierre Saintigny; Janick Selves; Jean-Marie Peron; Michel Rivoire; Philippe Merle; Claude Caron de Fromentel

doi:10.1371/journal.pone.0332768

Abstract

FZD7 is one of the key players in the subset of WNT-TGFβ-activated hepatocellular carcinomas (HCC), but the consequences of its abnormal expression on hepatocarcinogenesis remain to be better understood. Herein, we aimed to investigate the role of the FZD7-mediated signaling in immature phenotype and aggressiveness of HCC. Firstly, 499 human HCCs were used for clinical and molecular comparisons regarding the expression of FZD7 and stemness-associated markers. We showed that FZD7 overexpression was associated with poor differentiation and, in combination with CD133, predicted a poor outcome of patients with aggressive recurrence. Next, the impact of WNT3/FZD7 signaling on the differentiation of hepatic cells was assessed in HCC cell lines, as well in the non-transformed progenitor HepaRG cell line and in primary human hepatocytes, transduced with WNT3 and FZD7-expressing lentiviruses. We demonstrated that the ectopic expression of WNT3 and FZD7 inhibited the differentiation behavior of HepaRG cells and human primary hepatocytes, amplified the pool of EpCAM⁽⁺⁾, CD90⁽⁺⁾ and CD133⁽⁺⁾ subsets of HCC cell lines, and increased their cancer stem cell features. Moreover, we found that WNT3/FZD7-mediated stemness properties of cancer cells were independent of the stemness-associated marker NANOG. In conclusion, we identified the FZD7⁽⁺⁾/CD133⁽⁺⁾ signature as a potential prognosis marker and molecular therapeutic target, and we strengthened the hypothesis for the involvement of FZD7 in the enrichment of a cancer stem cell pool in HCC.

Citation: Lopez A, Paturel A, Fares N, Pez F, Wang G, Gifu P, et al. (2025) Frizzled 7 drives amplification of cancer stem-cell subpopulations and the aggressiveness and poor differentiation of human hepatocellular carcinoma. PLoS One 20(10): e0332768. https://doi.org/10.1371/journal.pone.0332768

Editor: Rupasri Ain, CSIR-Indian Institute of Chemical Biology, INDIA

Received: May 30, 2025; Accepted: September 3, 2025; Published: October 7, 2025

Copyright: © 2025 Lopez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: This work was supported by French grants: PAIR-CHC 2009 (contract #2009-143) from Institut National du Cancer (INCa), and the Ligue Nationale Contre le Cancer (LNCC). F.P. and N.F. were supported by the INCA grant, A.L. by the LNCC grant and P.G. by BPI France (IMODI consortium). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: No competing interest.

Abbreviations: AFP, α-fetoprotein; BCLC, Barcelona-Clinic Liver Cancer Staging Classification; CFU, Colony formation unit; Chol, Cholangiocytes; CI, Confidence intervals; CSCs, Cancer stem cells; ESCs, Embryonic stem cells, EV, Empty vector; FACS, Fluorescence-activated cell sorting; FCS, Fetal Calf Serum; bFGF, basic fibroblast growth factor; FZD, Frizzled; FZD7; HCC, Hepatocellular carcinoma; Hep, Hepatocytes; HR, Hazard ratios; IF, Immunofluorescence; RT-qPCR, Reverse transcription-quantitative PCR; NL, Normal liver; NT, Non-tumor; OS, Overall survival; PFA, Paraformaldehyde; PHH, Primary human hepatocytes; RFS, Recurrence-free survival; SCs, Stem cells; SD, Standard deviation; SDS-PAGE, SDS-polyacrylamide gel electrophoresis; shRNA, small hairpin RNA; shSCR, sh Scramble; T, Tumor; TCGA, The Cancer Genome Atlas.

Introduction

Hepatocellular carcinoma (HCC) is one of the main causes of cancer death worldwide [1,2]. WNT/FZD signaling plays an important role in the origin and maintenance of stem cells (SCs) and cancer stem cells (CSCs). These functions are shared with other pathways such as Hedgehog, Notch, and TGF-β/BMP in various tissues and cancer types [3–7]. The WNT/FZD pathway can signal through the canonical β-catenin or the non-canonical PKC and JNK pathways, controlling cell homeostasis and behavior [8]. Regarding hepatocarcinogenesis, deregulation of the WNT/β-CATENIN pathway has been described as a potential key event due to activating CTNNB1, AXIN1, APC and ZNRF3 mutations, or deregulated expression of WNT/FZD complex-receptor elements [9,10]. Furthermore, some classifications clearly identified distinct clusters of HCCs associating both wild-type β- catenin activity and WNT/FZD complex-receptor activation [11–13]. Indeed, Frizzled 7 (FZD7), one of the 10 Frizzled receptors, was found to be overexpressed in some HCCs, activating wild-type β-catenin and functionally increasing survival, proliferation, motility and epithelial-mesenchymal transition [10,14–19]. Importantly, the auto-paracrine WNT3 soluble extracellular factor was clearly identified as a natural canonical activating ligand of FZD7 [17]. These data opened a wide range of perspectives and hope for the targeting of the WNT pathway for the treatment of HCC [9,20].

Herein, we ascertained in human HCC samples whether FZD7 overexpression correlates with a poor differentiation phenotype and could predict a poor outcome for patients due to tumor aggressiveness. Furthermore, we investigated the implication of canonical WNT3/FZD7-mediated signaling in HCC cell behavior and stemness. We studied its influence on the differentiation program of hepatocytes and liver progenitors, as well as on the amplification of immature HCC sub-populations with stemness and aggressiveness markers. We also assessed the implication of the WNT3/FZD7-mediated signaling for CSC properties. Finally, we investigated the existence of potential cross-talk in HCC CSCs between the WNT3/FZD7 pathway and EpCAM, a SC-associated marker [21–23]. To address these aims, the study was designed in three main steps: first, to analyze the expression pattern of FZD7 and stemness/tumor aggressiveness-associated genes by RT-qPCR in a cohort of HCC samples compared to normal livers; second, to assess the impact of ectopic WNT3/FZD7 signaling on hepatocyte differentiation in PHHs and HepaRG cells; and third, its impact on CSC features through functional assays and stemness marker expression.

Materials and methods

Human liver tissues

A collection of 170 frozen human HCC samples, derived from surgically HCC resections was obtained from the PaThLiv French Liver Biobank (Lyon, France, agreement # DC2025–7145), among them 150 with both the tumor (T) and the matched non-tumor liver parenchyma (NT) (at least 2 cm distant of the tumor burden). One hundred and forty samples were evaluable for survival rate analysis (were removed those with liver transplantation post-resection, or early death within 3 months after surgery unrelated to HCC recurrence). Eleven surgically resected non-tumor liver (NL) tissues surrounding colorectal adenocarcinoma metastasis were provided by the Biological Sample Management Plateform (PGEB) of the Centre Léon Bérard, Lyon, France, agreement # BB-0033–00050) and used as a reference. All clinical data linked to the samples were pseudonymized. Histological analysis was performed: i) on T to confirm the diagnosis and characteristics of HCCs; ii) on NT and NL to ensure the absence of microscopic tumor invasion by respectively HCC (for NT) and colon adenocarcinoma metastasis (for NL).

In addition to this first cohort, data of 329 HCCs were obtained from The Cancer Genome Atlas (TCGA), 275 of them being evaluable for survival rates.

Cell lines and primary human hepatocytes

The Focus (CVCL_7955), Huh7 (CVCL_0336) and HepaRG (CVCL_9720) human cell lines were cultured as previously described [24–26]. The absence of mycoplasma contamination was regularly verified by PCR. Primary Human Hepatocytes (PHHs) were isolated from the resected NLs (see above) as described in [27].

Plasmids and lentiviral transduction

Human FZD7 cDNA was cloned into the plenti6/V5-Tag directional TOPO lentivirus containing the blasticidin selection gene (Invitrogen, USA), with an empty vector serving as negative control (EV) [16]. The WNT3-HA sequence was extracted from the plenti6/WNT3-HA lentiviral vector [16] and cloned into the pBB/HA lentiviral vector containing the hygromycin B selection gene, with an empty vector as negative control. shFZD7 and shSCR sequences were cloned in the pLKO.1 lentiviral vector containing the puromycin selection gene. Constructs were sequenced in both strands. Virions were produced in HEK293T cells (Invitrogen, USA). Cell lines and PHHs were transduced at a multiplicity of infection of one (MOI-1), as previously described [27].

Quantitative real-time RT-PCR (RT-qPCR)

Total RNA of cultured cells or frozen tissues was extracted with Extract-all (Eurobio, France), whereas total RNA of FACS-sorted cells was extracted with Nucleospin RNA/XS (Macherey Nagel, France). M-MLV reverse transcriptase (Invitrogen, USA) was used for cDNA synthesis after pre-treatment by DNAse-I (Roche, Switzerland). PCR reactions were performed in the Light Cycler 480 (Roche, Switzerland), with a mix of 1X-Quantifast SYBR Green (Qiagen, Netherlands), 500 nM of each primer and cDNA equivalent to 12.5 ng of total RNA. The thermal cycling conditions comprised an initial step of 5 min at 95°C, followed by 40 cycles of 10 sec at 95°C and 30 sec at 60°C. Each PCR run included standard curves and a non-template control to eliminate remaining contaminating DNA. Analysis was performed using the comparative ∆Ct method, and each gene normalized to the 18S RNA as housekeeping gene [18]. Primers were selected using the Primer3 website (https://primer3plus.com/cgi-bin/dev/primer3plus.cgi), validated, and are available on request. Gene overexpression in T and NT tissues was assessed relative to mean + 2 SD of expression in NL.

Protein extraction and Western blot analysis

For the detection of excreted WNT3, 200 µL of cell supernatant were taken, heat-denatured and the proteins separated by SDS-polyacrylamide gel electrophoresis (SDS-PAGE). For the detection of membranous FZD7, cells were harvested by scrapping, incubated 30 minutes on ice in lysis buffer [10 mM Tris-HCl pH 7.4, 100 mM NaCl, 1 mM EDTA, 1 mM EGTA, 1 mM NaF, 20 mM Na4P2O7, 2 mM Na3VO4, 1% Triton X-100, 10% glycerol, 0.1% SDS, 0.5% desoxycholate, 10 µL/mL complete protease inhibitor cocktail (Roche, Switzerland)] and sonicated three times for 30 seconds. After centrifugation, protein concentration was measured with the BCA Protein assay kit (Pierce, USA) heat-denatured and separated by SDS-PAGE. Western blot was carried out using the following antibodies: anti-β-ACTIN 1/10,000 (A5316, Sigma, Germany), anti-V5 1/5,000 (R960-25, Invitrogen, USA), anti-HA 1/5,000 (H3663, Sigma, Germany). After adding the rabbit anti-mouse IgG horseradish peroxidase conjugated secondary antibody (A9044, Sigma, Germany), blots were visualized with the enhanced chemiluminescence detection system (Amersham, United Kingdom).

Immunofluorescence staining

For immunofluorescence (IF), cells were fixed with 4% Paraformaldehyde (PFA), and thereafter permeabilized by pre-chilled methanol for 5 min at room temperature. Slides were incubated for 1 h at room temperature in blocking buffer (PBS 1x, 2 mg/mL BSA, 1% gelatin, 5% goat serum and 0.1% triton X-100), and thereafter incubated with either mouse anti-V5 antibody 1/200 (R690-25, Invitrogen, USA), or rabbit anti-CD133 antibody 1/200 (ab-19898, Abcam, United Kingdom) overnight at 4°C. Alexa488-conjugated goat anti-mouse (150117, Abcam, United Kingdom) or anti-rabbit (150077, Abcam, United Kingdom) secondary antibodies were incubated for 1 h at room temperature.

Flow cytometry and fluorescence-activated cell sorting (FACS)

Cells were collected and stained as previously described [27]. Briefly, 0.5 x 10⁶ cells in 100 µL of staining buffer (1X PBS, 2% BSA) were incubated on ice for 10 min for non-specific antigen saturation. Staining was performed with an APC-conjugated anti-EpCAM antibody 1/20 (Clone EBA-1 347200, BD Biosciences, USA) or a PerCP Cy5-conjugated anti-CD90 antibody 1/20 (eBio 45–0909, clone 5E10, eBiosciences, USA), according to manufacturer’s instructions. Staining analysis was performed with the FACScan flow cytometer (Becton Dickinson). For fluorescence-activated cell sorting (FACS), after staining with the APC-conjugated anti-EPCAM monoclonal antibody, cells were washed, resuspended in PBS-1X, 2% Fetal Calf Serum (FCS) and sorted by using the BD FACS Aria II cell sorting system (BD Biosciences, USA).

Clonogenicity and hepatosphere formation assay

For colony formation unit (CFU) assay, 200 EpCAM^(-)- or EpCAM⁽⁺⁾-sorted cells were seeded in 6-well plates, and after 14 days of subsequent culture, were either fixed (20% methanol)/stained (0.5% crystal violet), or fixed (4% PFA)/labeled for IF. Regarding the hepatosphere formation assay, cells were enzymatically harvested and seeded at 50,000 cells/mL in ultra-low attachment 24 well plates (Corning, USA) with DMEM containing 1XB27 supplement (Life Technologies, USA), 20 ng/mL EGF (R&D Systems, USA), 20 ng/mL bFGF (StemCell Technologies, Canada), 4 µg/mL heparin, and 1% penicillin/streptomycin (Invitrogen, USA). Cells were cultured for 3 days at 37°C with 5% CO₂. Spheres were measured and counted by phase-contrast microscopy.

Bioinformatics analysis

The liver tissue samples used in this study are the same as those previously processed and described in [28]. The data, normalized gene-read counts generated from RNA-sequencing (RNASeq version 2 – level 3), were downloaded using the TCGA2STAT R-package [29], and were log2 transformed. Prior to the downloading of data, normalization was performed using MapSlice for the alignment and RSEM to perform the quantification [30,31]. Clinical data were retrieved from the cBioPortal database [32,33].

Statistical analysis

MedCalc Version 12.7.1.0 software was used for statistical analysis. Continuous variables and proportions were compared using the Mann-Whitney (Wilcoxon for paired samples), t-Student (paired samples t-test when appropriate), and Chi-squared or Fischer’s exact tests. Survival was assessed by the Cox proportional-hazards regression in univariate and multivariate analysis, hazard ratios (HR) with 95% confidence intervals (CI), and p values were calculated using the log-rank (Mantel–Cox) test. Tests were considered significant when p values were < 0.05. Unsupervised hierarchical clustering with dendrogram was performed with NCSS 10 software.

Results

Clinicopathological correlations and prognosis value of FZD7-related signatures in HCC

The expression pattern of FZD7 and genes associated with stemness and tumor aggressiveness (EPCAM, NANOG, and CD133) were analyzed by RT-qPCR in an initial cohort of 170 surgically resected HCCs and compared to a panel of normal livers (NLs). When found overexpressed, the genes were annotated FZD7⁽⁺⁾, EPCAM⁽⁺⁾, NANOG⁽⁺⁾ and CD133⁽⁺⁾. EPCAM⁽⁺⁾ populations strongly correlated with poor differentiation of HCCs, AFP level > 200 ng/mL and HCC multinodularity, whereas NANOG⁽⁺⁾ correlated with the appearance of extra-hepatic recurrence (Table 1). For FZD7⁽⁺⁾, a close link was found with the poor differentiation of tumors (Table 1) and confirmed by integrating 329 additional HCCs of the TCGA cohort (total HCCs, n = 499) in the analysis (S1 Table).

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Table 1. Prevalence of FZD7, EPCAM, NANOG and CD133 overexpression in a French cohort of 170 HCCs regarding clinicopathological and biological parameters. Chi-squared test (p value). p value < 0.05 was considered as significant (in bold). AFP, α-fetoprotein level; HBV, hepatitis B virus; HCV, hepatitis C virus; NASH, non-alcoholic steato-hepatitis;). ⁽⁺⁾, overexpression.

https://doi.org/10.1371/journal.pone.0332768.t001

Among the 170 HCC patients of the initial cohort, only 140 were evaluable for survival analysis with the baseline clinicopathological characteristics described in S2 Table, showing a balanced percentage between viral and non-viral etiologies, and a majority of patients with little impaired liver functions (Child-Pugh A) and BCLC-A stage tumor (S2 Table). As expected, in univariate analysis, recurrence-free survival (RFS) was worsened by the presence of satellite nodules, microvascular invasion, multinodularity, tumor size > 50 mm, and underlying cirrhosis in univariate analysis (Table 2, upper panel). In multivariate analysis, only microvascular invasion, tumor size > 50 mm, and cirrhosis remained significant (Table 2, lower panel).

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Table 2. Correlation between survival rates and stemness/clinicopathological features in 140 HCCs of the French cohort. Cox proportional-hazards regression in univariate (upper panel) and multivariate analysis (lower panel); hazard ratios (HR) with 95% confidence intervals (CI). p value < 0.05 was considered as significant (in bold).). ⁽⁺⁾, overexpression.

https://doi.org/10.1371/journal.pone.0332768.t002

Regarding overall survival (OS) in univariate analysis, satellite nodules and cirrhosis parameters correlated with poor outcome, as well as some stemness signatures, such as EPCAM⁽⁺⁾, FZD7⁽⁺⁾/CD133⁽⁺⁾, EPCAM⁽⁺⁾/NANOG⁽⁺⁾, and NANOG⁽⁺⁾/CD133⁽⁺⁾. The recurrent presence of NANOG in signatures suggested its potential key role in stemness. Interestingly, poor OS did not correlate to shorter RFS but rather to shorter survival after tumor recurrence, suggesting these recurrences as being enriched in stem-like cells and harboring an aggressive phenotype (Table 2, upper panel). In the multivariate analysis, some molecular signatures (NANOG⁽⁺⁾/CD133⁽⁺⁾ and EPCAM⁽⁺⁾/NANOG⁽⁺⁾) were excluded from Table 2 (lower panel) because, as shown in the heat map (S1 Fig), the majority of the NANOG⁽⁺⁾/CD133⁽⁺⁾ cluster is incorporated into the larger FZD7⁽⁺⁾/CD133⁽⁺⁾ cluster, and the EPCAM⁽⁺⁾/NANOG⁽⁺⁾ cluster into the larger EPCAM⁽⁺⁾ cluster. Furthermore, in multivariate analysis, only satellite nodules, cirrhosis and the FZD7⁽⁺⁾/CD133⁽⁺⁾ signature impacted on poor OS through shorter post-recurrence survival (Table 2, lower panel).

All of the analyzed stemness signatures impacting on OS correlated between each other and could not be identified as independent variables in multivariate analysis (Rho Spearman's coefficient of rank correlation, p value), for example, FZD7⁽⁺⁾/NANOG⁽⁺⁾ vs. FZD7⁽⁺⁾/CD133⁽⁺⁾ (0.409, p < 0.0001), FZD7⁽⁺⁾/NANOG⁽⁺⁾ vs. EPCAM⁽⁺⁾/NANOG⁽⁺⁾ (0.513, p < 0.0001), FZD7⁽⁺⁾/NANOG⁽⁺⁾ vs. NANOG⁽⁺⁾/CD133⁽⁺⁾ (0.661, p = 0.0001), FZD7⁽⁺⁾/CD133⁽⁺⁾ vs. EPCAM⁽⁺⁾/NANOG⁽⁺⁾ (0.399, p < 0.0001), or FZD7⁽⁺⁾/CD133⁽⁺⁾ vs. NANOG⁽⁺⁾/CD133⁽⁺⁾ (0.682, p < 0.0001) (S3 Table). In addition to FZD7⁽⁺⁾/CD133⁽⁺⁾ that appeared independent in multivariate analysis, NANOG⁽⁺⁾ seemed to be a key player, present in all stemness signatures with predictive outcome.

The addition of 275 samples from the TCGA cohort evaluable for survival analysis allowed confirmation in a total of 415 HCCs (140 + 275) that the FZD7⁽⁺⁾/CD133⁽⁺⁾ signature predicted poorer OS associated with rapid death after recurrence, while the NANOG⁽⁺⁾/CD133⁽⁺⁾ signature predicted a poorer OS, independently of both rapid death after recurrence and RFS (S4 Table). Of note, the FZD7⁽⁺⁾/NANOG⁽⁺⁾ signature was not significant to predict OS. This observation suggests that FZD7 and NANOG affect patient outcome through independent pathways.

WNT3/FZD7-mediated signaling impairs hepatocyte differentiation

Since FZD7⁽⁺⁾ correlated with a poor differentiation status in HCC cohorts, we assessed whether FZD7, canonically activated by WNT3 in an experimental setting, could reverse the terminal differentiation of PHHs (constitutively differentiated quiescent hepatocytes), and/or inhibit the differentiation process occurring in HepaRG progenitors when reaching confluence (proliferating liver progenitors when plated at low density, and thereafter differentiating into quiescent hepatocytes and cholangiocytes at confluence) [34]. Transgene expression was confirmed by Western blot in cell lysate for FZD7 and in supernatant for excreted WNT3 (Fig 1A) and FZD7 localization at the cell membrane by IF (Fig 1B). Then, HepaRG, cells were transduced when proliferating at low density, and then cultured under conditions for terminal differentiation. Striking morphological differences were observed at the end of the differentiation process after four weeks at confluence (Fig 1C and 1D). Like the parental HepaRG cells previously described [34], HepaRG-EV cells gave evidence for a bidirectional differentiation into hepatocyte (Hep) and cholangiocyte (Chol) clusters (Fig 1C). In contrast, HepaRG-WNT3/FZD7 cells did not establish any structured monolayer, and none of the morphologic criteria of hepatocytes or cholangiocytes was observed (Fig 1D). Regarding PHH, phase-contrast microscopy showed cellular stress after lentiviral delivery of WNT3/FZD7 vs. EV (Fig 1E and 1F). However, PHH-EV, PHH-WNT3/FZD7 did not enter the cell cycle and remained in a non-proliferative state.

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Fig 1. The expression of FZD7 and WNT3 transgenes in HepaRG cells and PHHs affects hepatocyte differentiation.

Western blot of HepaRG cells transduced with the combination of FDZ7 and WNT3 lentiviruses or with the corresponding empty vectors (EV). FZD7-V5 tagged expression was assessed in cell lysate and WNT3-HA tagged in supernatant, respectively (A). Uncropped blots are shown on S1 Raw Images. Immunofluorescence showing FZD7 localization at the cell membrane of HepaRG cells (magnifications 100x and 800x) (B). HepaRG-EV-transduced cells reached terminal differentiation into hepatocyte and cholangiocyte-like cells (C) at confluence, in contrast to HepaRG-WNT3/FZD7-transduced cells (D). Freshly isolated PHHs were platted at high density and subsequently transduced with EV (E) or WNT3/FZD7 lentiviruses (F). Phase-contrast microscopy examination of cells is shown, pictures taken in randomly selected fields representative of three independent experiments (magnification, 100x for C and D, 200x for E and F). Expression of hepatocyte differentiation markers assessed by RT-qPCR after three weeks of culture and antibiotic selection in three independent experiments in HepaRG (G) and PHH (H).

https://doi.org/10.1371/journal.pone.0332768.g001

Most of the hepatocyte differentiation markers observed in differentiated HepaRG cells and in PHH (HNF4, CYP3A4, ALBUMIN, AAT, TAT, TDO) drastically decreased in HepaRG-WNT3/FZD7 (Fig 1G) and in PHH-WNT3/FZD7 (Fig 1H) vs. their respective EV. These results suggested that WNT3/FZD7 could impair the differentiation status of terminally differentiated PHH and the differentiation dynamics of HepaRG cells.

WNT3/FZD7-mediated signaling enhances immature phenotype in HCC Cell lines

The Focus and Huh7 HCC cell lines do not have any identified mutation amongst the components of the canonical WNT pathway (wild-type status of CTNNB1, AXIN1 and APC genes) [35]. They are relevant paradigms, expressing moderate levels of endogenous FZD7 that drives a steady state level of WNT/β-catenin activity, that is easily up- or down-regulated in experimental settings [17,18]. Activation of canonical WNT3/FZD7-mediated signaling in transduced cells (intracellular accumulation of β-catenin and overexpression of the β-catenin-target genes (CYCLIN-D1 and AXIN2)) was previously described [36].

We evaluated the impact of ectopic WNT3/FZD7 on CSC properties by functional assays and expression of stemness-associated markers. The hepatosphere formation assay showed that Focus-WNT3/FZD7 cells were able to form a significantly higher number of large hepatospheres (>100 µm) than Focus-EV cells, suggesting an enrichment of CSC sub-populations in the Focus-WNT3/FZD7 cells (Fig 2A). CSCs, a small subset of cancer cells in both HCC tumor bulks and cell lines, can be detected by different classical stemness-associated markers such as EpCAM, CD90, and CD133 [37–39]. While the expression of either WNT3 or FZD7 alone did not result in significant changes, the WNT3/FZD7 combination amplified by 42% and 29% the pool of EpCAM⁽⁺⁾ and CD90⁽⁺⁾ cells, respectively, as compared to Focus-EV cells (Fig 2B and 2C and S5 Table, upper panel), but did not impact the pool of CD133⁽⁺⁾ cells (S5 Table, upper panel). Similar data were found with the Huh7 cell line regarding EpCAM and CD90 markers (S5 Table, upper panel). To test the hypothesis for the involvement of FZD7 in this enrichment of EpCAM⁽⁺⁾ cells, we invalidated FZD7 by lentivirus-delivery of shRNA. Indeed, the resulting Focus-EV/shFZD7 and Focus-WNT3/FZD7/shFZD7 cells displayed a significant decrease in EpCAM⁽⁺⁾ sub-populations (S5 Table, lower panel).

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Fig 2. The overexpression of WNT3/FZD7 increases hepatosphere formation.

Focus cells transduced with empty (EV) or WNT3/FZD7 lentiviruses were cultured in non-adherent conditions for three days. Hepatospheres larger than 100 µm were counted in each well (2 independent experiments in duplicates). Bars represent 100 µm. Paired t-test, (*) p < 0.05 (A). Increased expression of stemness-associated markers in WNT3/FZD7-overexpressing Focus cells. The percentage of Focus-EV, Focus-WNT3, Focus-FZD7 and Focus-WNT3/FZD7 cells expressing EpCAM (B) or CD90 (C) markers was determined by flow cytometry. Data are representative of 12 independent experiments. Bars represent means. Mann-Whitney test: (*) p < 0.05, (**) p < 0.01, (***) p < 0.001.

https://doi.org/10.1371/journal.pone.0332768.g002

To highlight potential differences of CSC properties between some subsets of Focus cells, EpCAM⁽⁺⁾ and EpCAM^(-) sub-populations were separated by FACS and then tested for their capability to form colonies in CFU assay (Fig 3A and 3B). Based on differences in macroscopic and microscopic morphology, colonies were defined as holoclones, meroclones and paraclones as previously described [40] (Fig 3C). Holoclones, which contain tumor initiating cells, are large clusters of homogeneous, small and tightly packed cells with regular and smooth colony borderlines. Paraclones consist of few, dispersed and larger cells, whereas meroclones exhibit intermediate morphologies. Para- and meroclones are composed of late and early transit amplifying cells, respectively, and are devoid of tumor initiating cells. Ectopic expression of WNT3/FZD7 in Focus cells significantly increased the number of holoclones per well in both EpCAM⁽⁺⁾ and EpCAM^(-) cells (Fig 3D), but not those of meroclones (Fig 3E). Paraclones were not counted since they lack stemness or aggressiveness significance. Interestingly, CD133 intracellular levels, assessed by IF in holoclones arising from EpCAM⁽⁺⁾ and EpCAM^(-) cells, were similar in the EpCAM^(-) sub-populations from EV or WNT3/FZD7 cells (S2A Fig), but strongly increased only in the EpCAM⁽⁺⁾ sub-population under WNT3/FZD7 pressure (S2B Fig). These data confirmed that WNT3/FZD7-mediated signaling amplified a pool of cells with CSC properties.

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Fig 3. Effect of WNT3/FZD7 overexpression on the immature phenotype of Focus cells.

Representative images of CFU formation of FACS-sorted EpCAM⁽⁺⁾ (A) and EpCAM^(-) (B) sub-populations of Focus-EV and Focus-WNT3/FZD7 cells. The arising colonies exhibited three morphological types termed holo-, mero-, and paraclones. Of note, to obtain a full-length image of the holoclone, three microscopic pictures were merged (C). Holoclones (D) and meroclones (E) were counted for EpCAM⁽⁺⁾ and EpCAM^(-) cell subpopulations (2 independent experiments in duplicates). Mann-Whitney test, (*) P < 0.05.

https://doi.org/10.1371/journal.pone.0332768.g003

Discussion

Hepatocarcinogenesis is a heterogeneous and only partially understood process [41,42]. Genetic and transcriptomic classifications have identified clusters of HCCs with activation of wild-type β-catenin associated with overexpression of FZD7 [11,12,43]. Activation of the canonical WNT pathway non-related to mutations (CTNNB1, AXIN1, APC or ZNRF3) but rather to transcriptomic changes, such as FZD7 overexpression, drives embryonic programs [44], but is also found in HCC subtypes resembling hepatic progenitor/stem cells and might be a key player involved in addiction loops for tumor cell survival [45]. The main molecular subclasses of HCC are derived from previously reported transcriptomic based classifications and our FZD7-overexpressing tumors very likely belong to the proliferative group, specifically within the G2-G3 subgroups, S1/cluster 3 “TGF-β/WNT”, shown to be associated with a global DNA hypomethylation status, as reviewed by Llovet and collaborators [12].

In the present study, we show that canonical WNT3/FZD7-mediated signaling could inhibit the differentiation program in hepatocytes, amplify the pool of CSCs and drive the aggressiveness of HCC and contribute to poor outcomes of patients. In our experimental strategy, we have used WNT3 as an ectopic ligand for FZD7, since it has been reported to be able to activate the canonical WNT pathway [17,36], keeping in mind that other ligands such as WNT5a can activate FZD7-mediated signaling, mainly non-canonically, as reported in breast cancer [46]. Other membrane complex-receptor components, such as the binding of Cripto-1 to FZD7, are also key drivers of stemness in HCC [47].

Herein, we observed that FZD7 up-regulation closely correlates to poor differentiation of human HCCs. To demonstrate that FZD7 could directly drive the dedifferentiation program, we used human non-tumorigenic liver cells, such as HepaRG progenitors and PHHs. We demonstrated that the WNT3/FZD7 combination inhibits the commitment of the HepaRG progenitors towards hepatocyte differentiation, but also represses some genes specific for terminal hepatocyte differentiation in PHHs. We then assessed the specific role of WNT3/FZD7 in HCC cell lines. In particular, we looked at several markers previously reported to be overexpressed in liver SCs and CSCs [48], such as CD90, CD133 and EPCAM [49,50]. We confirmed that WNT3/FZD7 enhances some stemness features, by amplifying the pool of EpCAM⁽⁺⁾ and CD90⁽⁺⁾ subpopulations, and the pool of CD133⁽⁺⁾ in the EpCAM⁽⁺⁾ subset only. We showed in human HCC tissues that FZD7⁽⁺⁾ closely correlates with EPCAM⁽⁺⁾ and CD133⁽⁺⁾, as well as with the poor differentiation status of HCCs. CD133⁽⁺⁾ HCC cells were reported to exhibit high proliferative and clonogenic capabilities in vitro, and were more prone to in vivo tumorigenesis [39,51]. FZD7 was previously described as overexpressed in HCC cell lines such as Huh7, Focus, and in side populations from HCC cell lines such as PLC/PRF/5 [18,52]. Interestingly, it has also been suggested that FZD7 could mediate CSC self-renewal and EMT in gastric cancer, via the canonical WNT signaling pathway [53].

To go further in the implication of WNT/FZD signaling for the stemness features of HCC, we explored the expression of FZD7 and CD133, EPCAM, and NANOG in human HCC tissues, all of them already associated to CSCs in HCC [9,49,50,54]. When separately evaluated, none of them gave per se any prognostic value on survival rates in both univariate and multivariate analyses. The poor prognosis value of CD133⁽⁺⁾ was only present in association with FZD7⁽⁺⁾. Indeed, the FZD7⁽⁺⁾/CD133⁽⁺⁾ combination signature was strongly associated with poor OS due to rapid death after recurrence of HCC, this remaining true in multivariate analysis. This is in accordance with our experimental setting where canonical FZD7-mediated signaling nonetheless amplified some specific cell subsets with specific stemness markers, but also enhanced the CSC properties of HCC cell lines as assessed by hepatosphere and holoclone formation in functional assays. This was very likely driven by epigenetic reprogramming of HCC cells that governs their malignant properties as previously shown [55].

In summary, some clusters of human HCCs clearly appear to be driven by the canonical WNT pathway in the absence of any mutation in this signaling cascade. Herein, we show that the canonical FZD7-mediated signaling can play a key role in amplifying some subsets of cells with stemness markers among which, some of them harbor CSC properties. We speculate that this process controls a poor differentiation phenotype. Finally, we show that the presence of both FZD7 and CD133 markers in HCCs confers a value of poor prognosis due to more aggressive recurrence. This co-expression profile could be integrated into personalized medicine strategies, enabling more accurate patient stratification and the tailoring of targeted therapies based on individual tumor biology [56,57].

Novelty and impact

This study highlights the critical role of FZD7 in hepatocellular carcinoma (HCC) aggressiveness by promoting cancer stem cell properties. We demonstrate that FZD7 overexpression enhances stemness features independently of NANOG and, in association with CD133 overexpression, predicts poor patient outcome. These findings establish the FZD7⁽⁺⁾/CD133⁽⁺⁾ signature as a potential prognostic marker and therapeutic target, offering new insights into WNT/FZD signaling-associated HCC and opening perspectives for precision medicine strategies.

Supporting information

S1 Fig. Expression patterns of FZD7, CD133, NANOG, and EPCAM in human HCC samples.

Gene expression was assessed by RT-qPCR in 140 HCCs from the French cohort evaluable for survival rates. For each gene per sample, data were centered on a panel of 11 NLs as described in Materials and Methods, and gene considered as upregulated for values > mean + 2 SD (red boxes), or non-upregulated for values ≤ mean + 2 SD (black boxes). Dendrogram of rows is presented on the right, with hierarchical clustering using the Ward’s minimum variance method, Euclidean distance type, range scale type (NCSS 11 software).

https://doi.org/10.1371/journal.pone.0332768.s001

(TIF)

S2 Fig. CD133 expression in Focus-EV and Focus-WNT3/FZD7 EpCAM^(-) and EpCAM⁽⁺⁾-sorted cells.

Holoclones from EpCAM^(-) (A) and EpCAM⁽⁺⁾ (B) sub-populations were tested for CD133 expression by IF (magnification 200x and 800x).

https://doi.org/10.1371/journal.pone.0332768.s002

(TIF)

S1 Raw Image. Uncropped blots corresponding to Fig 1A.

https://doi.org/10.1371/journal.pone.0332768.s003

(PDF)

S1 Table. Prevalence of FZD7 overexpression in the 499 HCCs depending of the differentiation of tumors.

170 HCCs from the French and 329 from the TCGA cohorts were analyzed. Chi-squared test (p value). (+), overexpression.

https://doi.org/10.1371/journal.pone.0332768.s004

(DOCX)

S2 Table. Clinicopathological characteristics of human HCCs.

140 HCCs were evaluable for survival rates in the French cohort. AFP, α-fetoprotein level; BCLC, Barcelona-Clinic Liver Cancer.

https://doi.org/10.1371/journal.pone.0332768.s005

(DOCX)

S3 Table. Correlations between stemness signatures influencing overall survival (OS).

Data are expressed as Spearman’s rank correlation coefficient (Rho) and p value. All these signatures correlated with each other and could not be identified as independent variables in multivariate analysis.

https://doi.org/10.1371/journal.pone.0332768.s006

(DOCX)

S4 Table. Correlation between survival rates and the overexpression of FZD7, NANOG and CD133 stemness markers in HCCs.

415 HCCs (140 from the French and 275 from the TCGA cohorts) were analyzed. Cox proportional-hazards regression in univariate analysis; hazard ratios (HR) with 95% confidence intervals (CI). p value < 0.05 was considered as significant (in bold). Stemness signatures correlated between each other by Spearman's coefficient of rank correlation (rho, p value). (+), overexpression.

https://doi.org/10.1371/journal.pone.0332768.s007

(DOCX)

S5 Table. Expression of stemness-associated markers in WNT3/FZD7-overexpressing Focus and Huh7 cells.

Upper panel: The percentage of EpCAM or CD90 positive cells was assessed by FACS in Focus-EV, Focus-WNT3/FZD7 (12 independent experiments), Huh7-EV and Huh7-WNT3/FZD7 (3 independent experiments in duplicate). The percentage of CD133 positive cells was assessed by IF in Focus-EV and Focus-WNT3/FZD7. Ten random fields were analyzed in three independent experiments. Lower panel: The percentage of EpCAM(+) cells was also evaluated in Focus-EV and Focus-WNT3/FZD7, after shRNA-mediated invalidation of FZD7 (5 independent experiments). ND, non-determined. p value < 0.05 was considered as significant (in bold). shSCR, sh scramble.

https://doi.org/10.1371/journal.pone.0332768.s008

(DOCX)

References

1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209–49. pmid:33538338
- View Article
- PubMed/NCBI
- Google Scholar
2. Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229–63. pmid:38572751
- View Article
- PubMed/NCBI
- Google Scholar
3. Chatterjee S, Sil PC. Targeting the crosstalks of Wnt pathway with Hedgehog and Notch for cancer therapy. Pharmacol Res. 2019;142:251–61. pmid:30826456
- View Article
- PubMed/NCBI
- Google Scholar
4. Abbaszadegan MR, Bagheri V, Razavi MS, Momtazi AA, Sahebkar A, Gholamin M. Isolation, identification, and characterization of cancer stem cells: A review. J Cell Physiol. 2017;232(8):2008–18. pmid:28019667
- View Article
- PubMed/NCBI
- Google Scholar
5. Geisler F, Strazzabosco M. Emerging roles of Notch signaling in liver disease. Hepatology. 2015;61(1):382–92. pmid:24930574
- View Article
- PubMed/NCBI
- Google Scholar
6. Iluta S, Nistor M, Buruiana S, Dima D. Notch and Hedgehog Signaling Unveiled: Crosstalk, Roles, and Breakthroughs in Cancer Stem Cell Research. Life. 2025;15(2):228.
- View Article
- Google Scholar
7. Gonzalez-Sanchez E, Vaquero J, Férnandez-Barrena MG, Lasarte JJ, Avila MA, Sarobe P, et al. The TGF-β Pathway: A Pharmacological Target in Hepatocellular Carcinoma?. Cancers (Basel). 2021;13(13):3248. pmid:34209646
- View Article
- PubMed/NCBI
- Google Scholar
8. Lerner UH, Ohlsson C. The WNT system: background and its role in bone. J Intern Med. 2015;277(6):630–49. pmid:25845559
- View Article
- PubMed/NCBI
- Google Scholar
9. Pez F, Lopez A, Kim M, Wands JR, Caron de Fromentel C, Merle P. Wnt signaling and hepatocarcinogenesis: molecular targets for the development of innovative anticancer drugs. J Hepatol. 2013;59(5):1107–17. pmid:23835194
- View Article
- PubMed/NCBI
- Google Scholar
10. Chan K, Lo R. Deregulation of Frizzled Receptors in Hepatocellular Carcinoma. Int J Mol Sci. 2018;19(1). https://pubmed.ncbi.nlm.nih.gov/29361730/
- View Article
- Google Scholar
11. Lachenmayer A, Alsinet C, Savic R, Cabellos L, Toffanin S, Hoshida Y, et al. Wnt-pathway activation in two molecular classes of hepatocellular carcinoma and experimental modulation by sorafenib. Clin Cancer Res. 2012;18(18):4997–5007. pmid:22811581
- View Article
- PubMed/NCBI
- Google Scholar
12. Llovet JM, Kelley RK, Villanueva A, Singal AG, Pikarsky E, Roayaie S, et al. Hepatocellular carcinoma. Nat Rev Dis Primers. 2021;7(1):6. pmid:33479224
- View Article
- PubMed/NCBI
- Google Scholar
13. Chen Y, Deng X, Li Y, Han Y, Peng Y, Wu W, et al. Comprehensive molecular classification predicted microenvironment profiles and therapy response for HCC. Hepatology. 2024;80(3):536–51. pmid:38537130
- View Article
- PubMed/NCBI
- Google Scholar
14. Merle P, Kim M, Herrmann M, Gupte A, Lefrançois L, Califano S, et al. Oncogenic role of the frizzled-7/beta-catenin pathway in hepatocellular carcinoma. J Hepatol. 2005;43(5):854–62. pmid:16098625
- View Article
- PubMed/NCBI
- Google Scholar
15. Bengochea A, de Souza MM, Lefrançois L, Le Roux E, Galy O, Chemin I, et al. Common dysregulation of Wnt/Frizzled receptor elements in human hepatocellular carcinoma. Br J Cancer. 2008;99(1):143–50. pmid:18577996
- View Article
- PubMed/NCBI
- Google Scholar
16. Nambotin SB, Lefrancois L, Sainsily X, Berthillon P, Kim M, Wands JR, et al. Pharmacological inhibition of Frizzled-7 displays anti-tumor properties in hepatocellular carcinoma. J Hepatol. 2011;54(2):288–99. pmid:21055837
- View Article
- PubMed/NCBI
- Google Scholar
17. Kim M, Lee HC, Tsedensodnom O, Hartley R, Lim Y-S, Yu E, et al. Functional interaction between Wnt3 and Frizzled-7 leads to activation of the Wnt/beta-catenin signaling pathway in hepatocellular carcinoma cells. J Hepatol. 2008;48(5):780–91. pmid:18313787
- View Article
- PubMed/NCBI
- Google Scholar
18. Merle P, de la Monte S, Kim M, Herrmann M, Tanaka S, Von Dem Bussche A, et al. Functional consequences of frizzled-7 receptor overexpression in human hepatocellular carcinoma. Gastroenterology. 2004;127(4):1110–22. pmid:15480989
- View Article
- PubMed/NCBI
- Google Scholar
19. Nambotin SB, Tomimaru Y, Merle P, Wands JR, Kim M. Functional consequences of WNT3/Frizzled7-mediated signaling in non-transformed hepatic cells. Oncogenesis. 2012;1(10):e31. pmid:23552403
- View Article
- PubMed/NCBI
- Google Scholar
20. Gajos-Michniewicz A, Czyz M. WNT/β-catenin signaling in hepatocellular carcinoma: The aberrant activation, pathogenic roles, and therapeutic opportunities. Genes Dis. 2023;11(2):727–46. pmid:37692481
- View Article
- PubMed/NCBI
- Google Scholar
21. Iv Santaliz-Ruiz LE, Xie X, Old M, Teknos TN, Pan Q. Emerging role of nanog in tumorigenesis and cancer stem cells. Int J Cancer. 2014;135(12):2741–8. pmid:24375318
- View Article
- PubMed/NCBI
- Google Scholar
22. Ranjbar-Niavol F, Rezaei N, Zhao Y, Mirzaei H, Hassan M, Vosough M. P53/NANOG balance; the leading switch between poorly to well differentiated status in liver cancer cells. Front Oncol. 2024;14:1377761. pmid:38846985
- View Article
- PubMed/NCBI
- Google Scholar
23. Caron de Fromentel C, Levrero M. p53 functional loss, stemness and hepatocellular carcinoma. HR. 2020.
- View Article
- Google Scholar
24. Ozturk M, Bellet D, Isselbacher KJ, Wands J. Ectopic beta-human chorionic gonadotropin production by a human hepatoma cell line (FOCUS): isolation and immunochemical characterization. Endocrinology. 1987;120(2):559–66. pmid:2433127
- View Article
- PubMed/NCBI
- Google Scholar
25. Gripon P, Rumin S, Urban S, Le Seyec J, Glaise D, Cannie I. Infection of a human hepatoma cell line by hepatitis B virus. Proc Natl Acad Sci USA. 2002;99(24):15655–60.
- View Article
- Google Scholar
26. Nakabayashi H, Taketa K, Miyano K, Yamane T, Sato J. Growth of human hepatoma cells lines with differentiated functions in chemically defined medium. Cancer Res. 1982;42(9):3858–63. pmid:6286115
- View Article
- PubMed/NCBI
- Google Scholar
27. Pez F, Gifu P, Degli-Esposti D, Fares N, Lopez A, Lefrançois L, et al. In vitro transformation of primary human hepatocytes: Epigenetic changes and stemness properties. Exp Cell Res. 2019;384(2):111643. pmid:31557464
- View Article
- PubMed/NCBI
- Google Scholar
28. Gerossier L, Dubois A, Paturel A, Fares N, Cohen D, Merle P, et al. PARP inhibitors and radiation potentiate liver cell death in vitro. Do hepatocellular carcinomas have an achilles’ heel?. Clin Res Hepatol Gastroenterol. 2021;45(5):101553. pmid:33183998
- View Article
- PubMed/NCBI
- Google Scholar
29. Wan YW, Allen GI, Liu Z. TCGA2STAT: simple TCGA data access for integrated statistical analysis in R. Bioinformatics. 2016;32(6):952–4.
- View Article
- Google Scholar
30. Wang K, Singh D, Zeng Z, Coleman SJ, Huang Y, Savich GL, et al. MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res. 2010;38(18):e178. pmid:20802226
- View Article
- PubMed/NCBI
- Google Scholar
31. Li B, Ruotti V, Stewart RM, Thomson JA, Dewey CN. RNA-Seq gene expression estimation with read mapping uncertainty. Bioinformatics. 2010;26(4):493–500. pmid:20022975
- View Article
- PubMed/NCBI
- Google Scholar
32. Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401–4. pmid:22588877
- View Article
- PubMed/NCBI
- Google Scholar
33. Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013;6(269):pl1. pmid:23550210
- View Article
- PubMed/NCBI
- Google Scholar
34. Parent R, Marion M-J, Furio L, Trépo C, Petit M-A. Origin and characterization of a human bipotent liver progenitor cell line. Gastroenterology. 2004;126(4):1147–56. pmid:15057753
- View Article
- PubMed/NCBI
- Google Scholar
35. Cagatay T, Ozturk M. P53 mutation as a source of aberrant beta-catenin accumulation in cancer cells. Oncogene. 2002;21(52):7971–80. pmid:12439747
- View Article
- PubMed/NCBI
- Google Scholar
36. Rima M, Daghsni M, Lopez A, Fajloun Z, Lefrancois L, Dunach M, et al. Down-regulation of the Wnt/β-catenin signaling pathway by Cacnb4. Mol Biol Cell. 2017;28(25):3699–708. pmid:29021340
- View Article
- PubMed/NCBI
- Google Scholar
37. Yamashita T, Ji J, Budhu A, Forgues M, Yang W, Wang H-Y, et al. EpCAM-positive hepatocellular carcinoma cells are tumor-initiating cells with stem/progenitor cell features. Gastroenterology. 2009;136(3):1012–24. pmid:19150350
- View Article
- PubMed/NCBI
- Google Scholar
38. Yang ZF, Ho DW, Ng MN, Lau CK, Yu WC, Ngai P, et al. Significance of CD90+ cancer stem cells in human liver cancer. Cancer Cell. 2008;13(2):153–66. pmid:18242515
- View Article
- PubMed/NCBI
- Google Scholar
39. Yin S, Li J, Hu C, Chen X, Yao M, Yan M, et al. CD133 positive hepatocellular carcinoma cells possess high capacity for tumorigenicity. Int J Cancer. 2007;120(7):1444–50. pmid:17205516
- View Article
- PubMed/NCBI
- Google Scholar
40. Tan L, Sui X, Deng H, Ding M. Holoclone forming cells from pancreatic cancer cells enrich tumor initiating cells and represent a novel model for study of cancer stem cells. PLoS One. 2011;6(8):e23383. pmid:21826251
- View Article
- PubMed/NCBI
- Google Scholar
41. Safri F, Nguyen R, Zerehpooshnesfchi S, George J, Qiao L. Heterogeneity of hepatocellular carcinoma: from mechanisms to clinical implications. Cancer Gene Ther. 2024;31(8):1105–12. pmid:38499648
- View Article
- PubMed/NCBI
- Google Scholar
42. Youness RA, Hassan HA, Abaza T, Hady AA, El Magdoub HM, Ali M, et al. A Comprehensive Insight and In Silico Analysis of CircRNAs in Hepatocellular Carcinoma: A Step toward ncRNA-Based Precision Medicine. Cells. 2024;13(15):1245. pmid:39120276
- View Article
- PubMed/NCBI
- Google Scholar
43. Choi JH, Thung SN. Advances in Histological and Molecular Classification of Hepatocellular Carcinoma. Biomedicines. 2023;11(9):2582. pmid:37761023
- View Article
- PubMed/NCBI
- Google Scholar
44. Fernandez A, Huggins IJ, Perna L, Brafman D, Lu D, Yao S, et al. The WNT receptor FZD7 is required for maintenance of the pluripotent state in human embryonic stem cells. Proc Natl Acad Sci U S A. 2014;111(4):1409–14. pmid:24474766
- View Article
- PubMed/NCBI
- Google Scholar
45. Yamashita T, Budhu A, Forgues M, Wang XW. Activation of hepatic stem cell marker EpCAM by Wnt-beta-catenin signaling in hepatocellular carcinoma. Cancer Res. 2007;67(22):10831–9. pmid:18006828
- View Article
- PubMed/NCBI
- Google Scholar
46. Yin P, Bai Y, Wang Z, Sun Y, Gao J, Na L, et al. Non-canonical Fzd7 signaling contributes to breast cancer mesenchymal-like stemness involving Col6a1. Cell Commun Signal. 2020;18(1):143. pmid:32894152
- View Article
- PubMed/NCBI
- Google Scholar
47. Lo RC-L, Leung CO-N, Chan KK-S, Ho DW-H, Wong C-M, Lee TK-W, et al. Cripto-1 contributes to stemness in hepatocellular carcinoma by stabilizing Dishevelled-3 and activating Wnt/β-catenin pathway. Cell Death Differ. 2018;25(8):1426–41. pmid:29445127
- View Article
- PubMed/NCBI
- Google Scholar
48. Sukowati CH, El-Khobar K, Jasirwan COM, Kurniawan J, Gani RA. Stemness markers in hepatocellular carcinoma of Eastern vs. Western population: Etiology matters? Ann Hepatol. 2024;29(1):101153. pmid:37734662
- View Article
- PubMed/NCBI
- Google Scholar
49. Flores-Téllez TN, Villa-Treviño S, Piña-Vázquez C. Road to stemness in hepatocellular carcinoma. World J Gastroenterol. 2017;23(37):6750–76. pmid:29085221
- View Article
- PubMed/NCBI
- Google Scholar
50. Wang N, Wang S, Li MY, Hu B guang, Liu L ping, Yang S li. Cancer stem cells in hepatocellular carcinoma: an overview and promising therapeutic strategies. Ther Adv Med Oncol. 2018;10:175883591881628.
- View Article
- Google Scholar
51. Suetsugu A, Nagaki M, Aoki H, Motohashi T, Kunisada T, Moriwaki H. Characterization of CD133+ hepatocellular carcinoma cells as cancer stem/progenitor cells. Biochem Biophys Res Commun. 2006;351(4):820–4. pmid:17097610
- View Article
- PubMed/NCBI
- Google Scholar
52. Chiba T, Kita K, Zheng Y-W, Yokosuka O, Saisho H, Iwama A, et al. Side population purified from hepatocellular carcinoma cells harbors cancer stem cell-like properties. Hepatology. 2006;44(1):240–51. pmid:16799977
- View Article
- PubMed/NCBI
- Google Scholar
53. Li G, Su Q, Liu H, Wang D, Zhang W, Lu Z, et al. Frizzled7 Promotes Epithelial-to-mesenchymal Transition and Stemness Via Activating Canonical Wnt/β-catenin Pathway in Gastric Cancer. Int J Biol Sci. 2018;14(3):280–93. pmid:29559846
- View Article
- PubMed/NCBI
- Google Scholar
54. Zhao X, Sun B, Sun D, Liu T, Che N, Gu Q, et al. Slug promotes hepatocellular cancer cell progression by increasing sox2 and nanog expression. Oncol Rep. 2015;33(1):149–56. pmid:25339068
- View Article
- PubMed/NCBI
- Google Scholar
55. Raggi C, Factor VM, Seo D, Holczbauer A, Gillen MC, Marquardt JU, et al. Epigenetic reprogramming modulates malignant properties of human liver cancer. Hepatology. 2014;59(6):2251–62. pmid:24449497
- View Article
- PubMed/NCBI
- Google Scholar
56. Hamdy NM, Zaki MB, Rizk NI, Abdelmaksoud NM, Abd-Elmawla MA, Ismail RA, et al. Unraveling the ncRNA landscape that governs colorectal cancer: A roadmap to personalized therapeutics. Life Sci. 2024;354:122946. pmid:39122108
- View Article
- PubMed/NCBI
- Google Scholar
57. Hamdy NM, Basalious EB, El-Sisi MG, Nasr M, Kabel AM, Nossier ES, et al. Advancements in current one-size-fits-all therapies compared to future treatment innovations for better improved chemotherapeutic outcomes: a step-toward personalized medicine. Curr Med Res Opin. 2024;40(11):1943–61. pmid:39412377
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209–49. pmid:33538338
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229–63. pmid:38572751
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Chatterjee S, Sil PC. Targeting the crosstalks of Wnt pathway with Hedgehog and Notch for cancer therapy. Pharmacol Res. 2019;142:251–61. pmid:30826456
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref4] 4. Abbaszadegan MR, Bagheri V, Razavi MS, Momtazi AA, Sahebkar A, Gholamin M. Isolation, identification, and characterization of cancer stem cells: A review. J Cell Physiol. 2017;232(8):2008–18. pmid:28019667
View Article
PubMed/NCBI
Google Scholar

[14] View Article

[15] PubMed/NCBI

[16] Google Scholar

[ref5] 5. Geisler F, Strazzabosco M. Emerging roles of Notch signaling in liver disease. Hepatology. 2015;61(1):382–92. pmid:24930574
View Article
PubMed/NCBI
Google Scholar

[18] View Article

[19] PubMed/NCBI

[20] Google Scholar

[ref6] 6. Iluta S, Nistor M, Buruiana S, Dima D. Notch and Hedgehog Signaling Unveiled: Crosstalk, Roles, and Breakthroughs in Cancer Stem Cell Research. Life. 2025;15(2):228.
View Article
Google Scholar

[22] View Article

[23] Google Scholar

[ref7] 7. Gonzalez-Sanchez E, Vaquero J, Férnandez-Barrena MG, Lasarte JJ, Avila MA, Sarobe P, et al. The TGF-β Pathway: A Pharmacological Target in Hepatocellular Carcinoma?. Cancers (Basel). 2021;13(13):3248. pmid:34209646
View Article
PubMed/NCBI
Google Scholar

[25] View Article

[26] PubMed/NCBI

[27] Google Scholar

[ref8] 8. Lerner UH, Ohlsson C. The WNT system: background and its role in bone. J Intern Med. 2015;277(6):630–49. pmid:25845559
View Article
PubMed/NCBI
Google Scholar

[29] View Article

[30] PubMed/NCBI

[31] Google Scholar

[ref9] 9. Pez F, Lopez A, Kim M, Wands JR, Caron de Fromentel C, Merle P. Wnt signaling and hepatocarcinogenesis: molecular targets for the development of innovative anticancer drugs. J Hepatol. 2013;59(5):1107–17. pmid:23835194
View Article
PubMed/NCBI
Google Scholar

[33] View Article

[34] PubMed/NCBI

[35] Google Scholar

[ref10] 10. Chan K, Lo R. Deregulation of Frizzled Receptors in Hepatocellular Carcinoma. Int J Mol Sci. 2018;19(1). https://pubmed.ncbi.nlm.nih.gov/29361730/
View Article
Google Scholar

[37] View Article

[38] Google Scholar

[ref11] 11. Lachenmayer A, Alsinet C, Savic R, Cabellos L, Toffanin S, Hoshida Y, et al. Wnt-pathway activation in two molecular classes of hepatocellular carcinoma and experimental modulation by sorafenib. Clin Cancer Res. 2012;18(18):4997–5007. pmid:22811581
View Article
PubMed/NCBI
Google Scholar

[40] View Article

[41] PubMed/NCBI

[42] Google Scholar

[ref12] 12. Llovet JM, Kelley RK, Villanueva A, Singal AG, Pikarsky E, Roayaie S, et al. Hepatocellular carcinoma. Nat Rev Dis Primers. 2021;7(1):6. pmid:33479224
View Article
PubMed/NCBI
Google Scholar

[44] View Article

[45] PubMed/NCBI

[46] Google Scholar

[ref13] 13. Chen Y, Deng X, Li Y, Han Y, Peng Y, Wu W, et al. Comprehensive molecular classification predicted microenvironment profiles and therapy response for HCC. Hepatology. 2024;80(3):536–51. pmid:38537130
View Article
PubMed/NCBI
Google Scholar

[48] View Article

[49] PubMed/NCBI

[50] Google Scholar

[ref14] 14. Merle P, Kim M, Herrmann M, Gupte A, Lefrançois L, Califano S, et al. Oncogenic role of the frizzled-7/beta-catenin pathway in hepatocellular carcinoma. J Hepatol. 2005;43(5):854–62. pmid:16098625
View Article
PubMed/NCBI
Google Scholar

[52] View Article

[53] PubMed/NCBI

[54] Google Scholar

[ref15] 15. Bengochea A, de Souza MM, Lefrançois L, Le Roux E, Galy O, Chemin I, et al. Common dysregulation of Wnt/Frizzled receptor elements in human hepatocellular carcinoma. Br J Cancer. 2008;99(1):143–50. pmid:18577996
View Article
PubMed/NCBI
Google Scholar

[56] View Article

[57] PubMed/NCBI

[58] Google Scholar

[ref16] 16. Nambotin SB, Lefrancois L, Sainsily X, Berthillon P, Kim M, Wands JR, et al. Pharmacological inhibition of Frizzled-7 displays anti-tumor properties in hepatocellular carcinoma. J Hepatol. 2011;54(2):288–99. pmid:21055837
View Article
PubMed/NCBI
Google Scholar

[60] View Article

[61] PubMed/NCBI

[62] Google Scholar

[ref17] 17. Kim M, Lee HC, Tsedensodnom O, Hartley R, Lim Y-S, Yu E, et al. Functional interaction between Wnt3 and Frizzled-7 leads to activation of the Wnt/beta-catenin signaling pathway in hepatocellular carcinoma cells. J Hepatol. 2008;48(5):780–91. pmid:18313787
View Article
PubMed/NCBI
Google Scholar

[64] View Article

[65] PubMed/NCBI

[66] Google Scholar

[ref18] 18. Merle P, de la Monte S, Kim M, Herrmann M, Tanaka S, Von Dem Bussche A, et al. Functional consequences of frizzled-7 receptor overexpression in human hepatocellular carcinoma. Gastroenterology. 2004;127(4):1110–22. pmid:15480989
View Article
PubMed/NCBI
Google Scholar

[68] View Article

[69] PubMed/NCBI

[70] Google Scholar

[ref19] 19. Nambotin SB, Tomimaru Y, Merle P, Wands JR, Kim M. Functional consequences of WNT3/Frizzled7-mediated signaling in non-transformed hepatic cells. Oncogenesis. 2012;1(10):e31. pmid:23552403
View Article
PubMed/NCBI
Google Scholar

[72] View Article

[73] PubMed/NCBI

[74] Google Scholar

[ref20] 20. Gajos-Michniewicz A, Czyz M. WNT/β-catenin signaling in hepatocellular carcinoma: The aberrant activation, pathogenic roles, and therapeutic opportunities. Genes Dis. 2023;11(2):727–46. pmid:37692481
View Article
PubMed/NCBI
Google Scholar

[76] View Article

[77] PubMed/NCBI

[78] Google Scholar

[ref21] 21. Iv Santaliz-Ruiz LE, Xie X, Old M, Teknos TN, Pan Q. Emerging role of nanog in tumorigenesis and cancer stem cells. Int J Cancer. 2014;135(12):2741–8. pmid:24375318
View Article
PubMed/NCBI
Google Scholar

[80] View Article

[81] PubMed/NCBI

[82] Google Scholar

[ref22] 22. Ranjbar-Niavol F, Rezaei N, Zhao Y, Mirzaei H, Hassan M, Vosough M. P53/NANOG balance; the leading switch between poorly to well differentiated status in liver cancer cells. Front Oncol. 2024;14:1377761. pmid:38846985
View Article
PubMed/NCBI
Google Scholar

[84] View Article

[85] PubMed/NCBI

[86] Google Scholar

[ref23] 23. Caron de Fromentel C, Levrero M. p53 functional loss, stemness and hepatocellular carcinoma. HR. 2020.
View Article
Google Scholar

[88] View Article

[89] Google Scholar

[ref24] 24. Ozturk M, Bellet D, Isselbacher KJ, Wands J. Ectopic beta-human chorionic gonadotropin production by a human hepatoma cell line (FOCUS): isolation and immunochemical characterization. Endocrinology. 1987;120(2):559–66. pmid:2433127
View Article
PubMed/NCBI
Google Scholar

[91] View Article

[92] PubMed/NCBI

[93] Google Scholar

[ref25] 25. Gripon P, Rumin S, Urban S, Le Seyec J, Glaise D, Cannie I. Infection of a human hepatoma cell line by hepatitis B virus. Proc Natl Acad Sci USA. 2002;99(24):15655–60.
View Article
Google Scholar

[95] View Article

[96] Google Scholar

[ref26] 26. Nakabayashi H, Taketa K, Miyano K, Yamane T, Sato J. Growth of human hepatoma cells lines with differentiated functions in chemically defined medium. Cancer Res. 1982;42(9):3858–63. pmid:6286115
View Article
PubMed/NCBI
Google Scholar

[98] View Article

[99] PubMed/NCBI

[100] Google Scholar

[ref27] 27. Pez F, Gifu P, Degli-Esposti D, Fares N, Lopez A, Lefrançois L, et al. In vitro transformation of primary human hepatocytes: Epigenetic changes and stemness properties. Exp Cell Res. 2019;384(2):111643. pmid:31557464
View Article
PubMed/NCBI
Google Scholar

[102] View Article

[103] PubMed/NCBI

[104] Google Scholar

[ref28] 28. Gerossier L, Dubois A, Paturel A, Fares N, Cohen D, Merle P, et al. PARP inhibitors and radiation potentiate liver cell death in vitro. Do hepatocellular carcinomas have an achilles’ heel?. Clin Res Hepatol Gastroenterol. 2021;45(5):101553. pmid:33183998
View Article
PubMed/NCBI
Google Scholar

[106] View Article

[107] PubMed/NCBI

[108] Google Scholar

[ref29] 29. Wan YW, Allen GI, Liu Z. TCGA2STAT: simple TCGA data access for integrated statistical analysis in R. Bioinformatics. 2016;32(6):952–4.
View Article
Google Scholar

[110] View Article

[111] Google Scholar

[ref30] 30. Wang K, Singh D, Zeng Z, Coleman SJ, Huang Y, Savich GL, et al. MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res. 2010;38(18):e178. pmid:20802226
View Article
PubMed/NCBI
Google Scholar

[113] View Article

[114] PubMed/NCBI

[115] Google Scholar

[ref31] 31. Li B, Ruotti V, Stewart RM, Thomson JA, Dewey CN. RNA-Seq gene expression estimation with read mapping uncertainty. Bioinformatics. 2010;26(4):493–500. pmid:20022975
View Article
PubMed/NCBI
Google Scholar

[117] View Article

[118] PubMed/NCBI

[119] Google Scholar

[ref32] 32. Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401–4. pmid:22588877
View Article
PubMed/NCBI
Google Scholar

[121] View Article

[122] PubMed/NCBI

[123] Google Scholar

[ref33] 33. Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013;6(269):pl1. pmid:23550210
View Article
PubMed/NCBI
Google Scholar

[125] View Article

[126] PubMed/NCBI

[127] Google Scholar

[ref34] 34. Parent R, Marion M-J, Furio L, Trépo C, Petit M-A. Origin and characterization of a human bipotent liver progenitor cell line. Gastroenterology. 2004;126(4):1147–56. pmid:15057753
View Article
PubMed/NCBI
Google Scholar

[129] View Article

[130] PubMed/NCBI

[131] Google Scholar

[ref35] 35. Cagatay T, Ozturk M. P53 mutation as a source of aberrant beta-catenin accumulation in cancer cells. Oncogene. 2002;21(52):7971–80. pmid:12439747
View Article
PubMed/NCBI
Google Scholar

[133] View Article

[134] PubMed/NCBI

[135] Google Scholar

[ref36] 36. Rima M, Daghsni M, Lopez A, Fajloun Z, Lefrancois L, Dunach M, et al. Down-regulation of the Wnt/β-catenin signaling pathway by Cacnb4. Mol Biol Cell. 2017;28(25):3699–708. pmid:29021340
View Article
PubMed/NCBI
Google Scholar

[137] View Article

[138] PubMed/NCBI

[139] Google Scholar

[ref37] 37. Yamashita T, Ji J, Budhu A, Forgues M, Yang W, Wang H-Y, et al. EpCAM-positive hepatocellular carcinoma cells are tumor-initiating cells with stem/progenitor cell features. Gastroenterology. 2009;136(3):1012–24. pmid:19150350
View Article
PubMed/NCBI
Google Scholar

[141] View Article

[142] PubMed/NCBI

[143] Google Scholar

[ref38] 38. Yang ZF, Ho DW, Ng MN, Lau CK, Yu WC, Ngai P, et al. Significance of CD90+ cancer stem cells in human liver cancer. Cancer Cell. 2008;13(2):153–66. pmid:18242515
View Article
PubMed/NCBI
Google Scholar

[145] View Article

[146] PubMed/NCBI

[147] Google Scholar

[ref39] 39. Yin S, Li J, Hu C, Chen X, Yao M, Yan M, et al. CD133 positive hepatocellular carcinoma cells possess high capacity for tumorigenicity. Int J Cancer. 2007;120(7):1444–50. pmid:17205516
View Article
PubMed/NCBI
Google Scholar

[149] View Article

[150] PubMed/NCBI

[151] Google Scholar

[ref40] 40. Tan L, Sui X, Deng H, Ding M. Holoclone forming cells from pancreatic cancer cells enrich tumor initiating cells and represent a novel model for study of cancer stem cells. PLoS One. 2011;6(8):e23383. pmid:21826251
View Article
PubMed/NCBI
Google Scholar

[153] View Article

[154] PubMed/NCBI

[155] Google Scholar

[ref41] 41. Safri F, Nguyen R, Zerehpooshnesfchi S, George J, Qiao L. Heterogeneity of hepatocellular carcinoma: from mechanisms to clinical implications. Cancer Gene Ther. 2024;31(8):1105–12. pmid:38499648
View Article
PubMed/NCBI
Google Scholar

[157] View Article

[158] PubMed/NCBI

[159] Google Scholar

[ref42] 42. Youness RA, Hassan HA, Abaza T, Hady AA, El Magdoub HM, Ali M, et al. A Comprehensive Insight and In Silico Analysis of CircRNAs in Hepatocellular Carcinoma: A Step toward ncRNA-Based Precision Medicine. Cells. 2024;13(15):1245. pmid:39120276
View Article
PubMed/NCBI
Google Scholar

[161] View Article

[162] PubMed/NCBI

[163] Google Scholar

[ref43] 43. Choi JH, Thung SN. Advances in Histological and Molecular Classification of Hepatocellular Carcinoma. Biomedicines. 2023;11(9):2582. pmid:37761023
View Article
PubMed/NCBI
Google Scholar

[165] View Article

[166] PubMed/NCBI

[167] Google Scholar

[ref44] 44. Fernandez A, Huggins IJ, Perna L, Brafman D, Lu D, Yao S, et al. The WNT receptor FZD7 is required for maintenance of the pluripotent state in human embryonic stem cells. Proc Natl Acad Sci U S A. 2014;111(4):1409–14. pmid:24474766
View Article
PubMed/NCBI
Google Scholar

[169] View Article

[170] PubMed/NCBI

[171] Google Scholar

[ref45] 45. Yamashita T, Budhu A, Forgues M, Wang XW. Activation of hepatic stem cell marker EpCAM by Wnt-beta-catenin signaling in hepatocellular carcinoma. Cancer Res. 2007;67(22):10831–9. pmid:18006828
View Article
PubMed/NCBI
Google Scholar

[173] View Article

[174] PubMed/NCBI

[175] Google Scholar

[ref46] 46. Yin P, Bai Y, Wang Z, Sun Y, Gao J, Na L, et al. Non-canonical Fzd7 signaling contributes to breast cancer mesenchymal-like stemness involving Col6a1. Cell Commun Signal. 2020;18(1):143. pmid:32894152
View Article
PubMed/NCBI
Google Scholar

[177] View Article

[178] PubMed/NCBI

[179] Google Scholar

[ref47] 47. Lo RC-L, Leung CO-N, Chan KK-S, Ho DW-H, Wong C-M, Lee TK-W, et al. Cripto-1 contributes to stemness in hepatocellular carcinoma by stabilizing Dishevelled-3 and activating Wnt/β-catenin pathway. Cell Death Differ. 2018;25(8):1426–41. pmid:29445127
View Article
PubMed/NCBI
Google Scholar

[181] View Article

[182] PubMed/NCBI

[183] Google Scholar

[ref48] 48. Sukowati CH, El-Khobar K, Jasirwan COM, Kurniawan J, Gani RA. Stemness markers in hepatocellular carcinoma of Eastern vs. Western population: Etiology matters? Ann Hepatol. 2024;29(1):101153. pmid:37734662
View Article
PubMed/NCBI
Google Scholar

[185] View Article

[186] PubMed/NCBI

[187] Google Scholar

[ref49] 49. Flores-Téllez TN, Villa-Treviño S, Piña-Vázquez C. Road to stemness in hepatocellular carcinoma. World J Gastroenterol. 2017;23(37):6750–76. pmid:29085221
View Article
PubMed/NCBI
Google Scholar

[189] View Article

[190] PubMed/NCBI

[191] Google Scholar

[ref50] 50. Wang N, Wang S, Li MY, Hu B guang, Liu L ping, Yang S li. Cancer stem cells in hepatocellular carcinoma: an overview and promising therapeutic strategies. Ther Adv Med Oncol. 2018;10:175883591881628.
View Article
Google Scholar

[193] View Article

[194] Google Scholar

[ref51] 51. Suetsugu A, Nagaki M, Aoki H, Motohashi T, Kunisada T, Moriwaki H. Characterization of CD133+ hepatocellular carcinoma cells as cancer stem/progenitor cells. Biochem Biophys Res Commun. 2006;351(4):820–4. pmid:17097610
View Article
PubMed/NCBI
Google Scholar

[196] View Article

[197] PubMed/NCBI

[198] Google Scholar

[ref52] 52. Chiba T, Kita K, Zheng Y-W, Yokosuka O, Saisho H, Iwama A, et al. Side population purified from hepatocellular carcinoma cells harbors cancer stem cell-like properties. Hepatology. 2006;44(1):240–51. pmid:16799977
View Article
PubMed/NCBI
Google Scholar

[200] View Article

[201] PubMed/NCBI

[202] Google Scholar

[ref53] 53. Li G, Su Q, Liu H, Wang D, Zhang W, Lu Z, et al. Frizzled7 Promotes Epithelial-to-mesenchymal Transition and Stemness Via Activating Canonical Wnt/β-catenin Pathway in Gastric Cancer. Int J Biol Sci. 2018;14(3):280–93. pmid:29559846
View Article
PubMed/NCBI
Google Scholar

[204] View Article

[205] PubMed/NCBI

[206] Google Scholar

[ref54] 54. Zhao X, Sun B, Sun D, Liu T, Che N, Gu Q, et al. Slug promotes hepatocellular cancer cell progression by increasing sox2 and nanog expression. Oncol Rep. 2015;33(1):149–56. pmid:25339068
View Article
PubMed/NCBI
Google Scholar

[208] View Article

[209] PubMed/NCBI

[210] Google Scholar

[ref55] 55. Raggi C, Factor VM, Seo D, Holczbauer A, Gillen MC, Marquardt JU, et al. Epigenetic reprogramming modulates malignant properties of human liver cancer. Hepatology. 2014;59(6):2251–62. pmid:24449497
View Article
PubMed/NCBI
Google Scholar

[212] View Article

[213] PubMed/NCBI

[214] Google Scholar

[ref56] 56. Hamdy NM, Zaki MB, Rizk NI, Abdelmaksoud NM, Abd-Elmawla MA, Ismail RA, et al. Unraveling the ncRNA landscape that governs colorectal cancer: A roadmap to personalized therapeutics. Life Sci. 2024;354:122946. pmid:39122108
View Article
PubMed/NCBI
Google Scholar

[216] View Article

[217] PubMed/NCBI

[218] Google Scholar

[ref57] 57. Hamdy NM, Basalious EB, El-Sisi MG, Nasr M, Kabel AM, Nossier ES, et al. Advancements in current one-size-fits-all therapies compared to future treatment innovations for better improved chemotherapeutic outcomes: a step-toward personalized medicine. Curr Med Res Opin. 2024;40(11):1943–61. pmid:39412377
View Article
PubMed/NCBI
Google Scholar

[220] View Article

[221] PubMed/NCBI

[222] Google Scholar

Figures

Abstract

Introduction

Materials and methods

Human liver tissues

Cell lines and primary human hepatocytes

Plasmids and lentiviral transduction

Quantitative real-time RT-PCR (RT-qPCR)

Protein extraction and Western blot analysis

Immunofluorescence staining

Flow cytometry and fluorescence-activated cell sorting (FACS)

Clonogenicity and hepatosphere formation assay

Bioinformatics analysis

Statistical analysis

Results

Clinicopathological correlations and prognosis value of FZD7-related signatures in HCC

WNT3/FZD7-mediated signaling impairs hepatocyte differentiation

WNT3/FZD7-mediated signaling enhances immature phenotype in HCC Cell lines

Discussion

Novelty and impact

Supporting information

S1 Fig. Expression patterns of FZD7, CD133, NANOG, and EPCAM in human HCC samples.

S2 Fig. CD133 expression in Focus-EV and Focus-WNT3/FZD7 EpCAM(-) and EpCAM(+)-sorted cells.

S1 Raw Image. Uncropped blots corresponding to Fig 1A.

S1 Table. Prevalence of FZD7 overexpression in the 499 HCCs depending of the differentiation of tumors.

S2 Table. Clinicopathological characteristics of human HCCs.

S3 Table. Correlations between stemness signatures influencing overall survival (OS).

S4 Table. Correlation between survival rates and the overexpression of FZD7, NANOG and CD133 stemness markers in HCCs.

S5 Table. Expression of stemness-associated markers in WNT3/FZD7-overexpressing Focus and Huh7 cells.

References

S2 Fig. CD133 expression in Focus-EV and Focus-WNT3/FZD7 EpCAM^(-) and EpCAM⁽⁺⁾-sorted cells.