Targeting Wnt3a and Loxl2 Synergistically Induces Ferroptosis in Liver Cancer Stem Cells and Suppresses Tumorigenesis.

1
Introduction
Primary liver cancer is one of the most fatal malignant tumors, with hepatocellular carcinoma (HCC) being its predominant form. The incidence and mortality rates of HCC continue to rise, posing a major global health challenge [1, 2]. According to global cancer statistics, liver cancer accounts for 8.2% of all cancer‐related deaths, and the 5‐year survival rate for HCC and intrahepatic cholangiocarcinoma is only 20.8% [3, 4]. Due to its insidious onset and highly aggressive nature, most patients are diagnosed at an advanced stage and are ineligible for curative treatments. Although molecular targeted therapy and chemotherapy are available for some HCC patients, clinical outcomes remain unsatisfactory [5, 6]. Therefore, exploring early diagnostic methods and novel systemic therapies for HCC is of critical importance.
Liver cancer stem cells (LCSCs) represent a unique heterogeneous subpopulation of malignant cells with self‐renewal and differentiation capabilities. They are highly implicated in HCC initiation, immune evasion, metastasis, and recurrence [7, 8, 9]. Multiple mechanisms and factors, including mitophagy, epigenetic modifications, and the tumor microenvironment, contribute to regulating LCSC stemness [10]. In‐depth research into the characteristics and regulatory mechanisms of LCSCs is essential for developing more effective HCC treatment strategies [11]. The unique properties of LCSCs provide valuable insights for studying and treating liver cancer. Understanding the pathways through which LCSCs mediate tumorigenesis and identifying new regulatory mechanisms and therapeutic targets are currently important research directions.
Ferroptosis is a newly identified form of programmed and immunogenic cell death characterized by iron‐dependent accumulation of lipid peroxides. It can effectively enhance tumor resistance and is considered a potential target for cancer therapy [12, 13]. Key biomarkers of ferroptosis include ferrous iron (Fe2+), glutathione (GSH), and malondialdehyde (MDA). Studies have shown that inducing ferroptosis can inhibit HCC proliferation, while ferroptosis suppressors can modulate HCC progression through lipid metabolism [14]. Targeting ferroptosis inhibition effectively suppresses HCC development and influences sorafenib resistance [15]. However, the mechanism of ferroptosis in LCSCs has not yet been reported.
Wnt3a, a member of the Wnt family, plays a crucial role in the proliferation and differentiation of various types of stem cells [16]. Ferroptosis can influence multiple diseases via the Wnt signaling pathway. For example, expression levels of Wnt1, Wnt3a, β‐catenin, and vimentin can promote ferroptosis and induce Wnt/β‐catenin‐mediated pulmonary fibrosis [17]. Lysyl oxidase‐like 2 (Loxl2) is a promising biomarker in HCC and can affect ferroptosis in endometrial cancer via the PI3K/AKT signaling pathway [18]. Multiple studies have demonstrated that Loxl2 promotes HCC progression by regulating immune infiltration, metastatic niches, and vascular invasion [19, 20]. Our previous research has shown that knockout of Wnt3a and Loxl2 can inhibit the proliferation, migration, and invasion of LCSCs while inducing apoptosis and cell cycle arrest.
Our research group has long focused on the mechanisms of Wnt3a and Loxl2 in liver cancer. We have confirmed that single‐gene knockout inhibits LCSC stemness, reduces sphere formation, and suppresses tumor growth, with double‐gene knockout producing more pronounced and potentially synergistic effects. However, it remains unknown whether Wnt3a/Loxl2 knockout influences LCSCs via the regulation of ferroptosis. Therefore, this study will use bioinformatic analysis along with in vivo and in vitro experiments to further investigate the potential mechanism by which Wnt3a and Loxl2 affect ferroptosis in LCSCs. The aim is to identify novel potential targets for liver cancer screening and treatment from the perspective of ferroptosis.

2
Material and Methods
2.1
Bioinformatic Analysis
The GEO database and the GEO2R online tool (https://www.ncbi.nlm.nih.gov/geo/) were used to screen for differentially expressed genes (DEGs) between liver cancer tissues and normal tissues, with the criteria of adj.p.Val < 0.05 and |logFC| ≥ 1. The FerrDb data‐base (http://www.zhounan.org/ferrdb/current/) was employed to analyze ferroptosis‐related genes, including drivers and regulators of ferroptosis. The Venny online tool (https://bioinfogp.cnb.csic.es/tools/venny/) was used to intersect and map DEGs related to both liver cancer and ferroptosis. The STRING database (https://string‐db.org/) was utilized to construct a protein–protein interaction (PPI) network for the identified DEGs. The DAVID online platform (https://david.ncifcrf.gov) was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to identify key molecular functions and cellular pathways. ImageGP (http://www.ehbio.com/ImageGP/index.php/Home/Index/PCAplot.html) was used to generate GO and KEGG enrichment plots. Cytoscape software (v3.10.0) was applied for visualizing the PPI network, identifying key modules, and screening hub genes with high connectivity.

2.2
Cell Culture and Sorting
The Hep3B liver cancer cell line was purchased from Procell Life Science (Wuhan, China) and cultured in DMEM supplemented with 10% FBS and 1% penicillin–streptomycin at 37°C with 5% CO2. Hep3B cells in the logarithmic growth phase with good morphology were sorted using flow cytometry with CD133 (Biolegend, USA) and EpCAM (Biolegend, USA) antibodies to isolate CD133+/EpCAM+ LCSCs. LCSCs were cultured in DMEM/F12 medium containing B27, 1% penicillin–streptomycin, FGF, and EGF under the same conditions. LCSCs were suspended in low‐adherence 6‐well plates for sphere formation and purity identification. LCSCs stably expressing luciferase (LCSCs‐Luc) were used for constructing the orthotopic liver cancer model in mice.

2.3
Lentiviral Transduction of LCSCs
Lentiviral vectors carrying shRNA targeting Wnt3a (5′‐CCTTTGCAGTGACACGCTCAT‐3′) with puromycin resistance and GFP fluorescence, and shRNA targeting Loxl2 (5′‐ATTACTCCAACAACATCAT‐3′) with G418 resistance and RFP fluorescence were constructed. LCSCs were infected with Wnt3a‐shRNA or Loxl2‐shRNA lentiviruses for 12 h, and infection efficiency was observed under a fluorescence microscope after 72 h. LCSCs were divided into four groups: control, Wnt3a‐KD, Loxl2‐KD, and Wnt3a/Loxl2 double‐KD. Knockdown efficiency was verified using qPCR and Western blot.

2.4
Generation of Transgenic Mouse Models
C57BL/6 mice (Cyagen, China) were used. sgRNA target sequences for mouse Wnt3a and Loxl2 were designed based on the NCBI database and cloned into plasmid vectors. After superovulation, fertilized eggs were collected and microinjected with transcribed sgRNA and Cas9 mRNA. CRISPR/Cas9 was used to knock out one allele of the target genes. Embryos were transplanted into recipient females. Genotyping was performed using tail DNA extracted from one‐week‐old pups. Mice were raised under SPF conditions until 5–6 weeks of age. All animals were housed in the SPF barrier facility at Dalian Medical University under controlled conditions: temperature 20°C–25°C, humidity 50%–60%, and a 12 h light/dark cycle. All procedures were approved by the Institutional Animal Ethics Committee (approval no. AEE2305).

2.5
Orthotopic Liver Cancer Model in Mice
Mice were divided into four groups (n = 5, half male and half female): wild‐type (WT), Wnt3a knockout (Wnt3a−), Loxl2 knockout (Loxl2−), and Wnt3a/Loxl2 double knockout (Wnt3a−Loxl2−). The latter three groups were collectively referred to as knockout (KO) groups. Under anesthesia, a 1–2 cm midline laparotomy was performed to expose the liver. LCSCs‐Luc were inoculated into the left medial lobe. The wound was sutured, and mice were monitored every two days for general health and body weight. On Day 14, in vivo fluorescence imaging was performed. On day 30, mice were euthanized, tumors were dissected, and HE staining was performed after dewaxing, hydration, staining, dehydration, transparency, and mounting.

2.6
Detection of Ferroptosis‐Related Indicators
Ferrous iron (Fe2+) content in LCSCs and mouse tumor tissues was measured using an iron assay kit (MLBio, Shanghai). GSH and MDA levels were detected using ELISA kits (MLBio, Shanghai). Standard curves were prepared according to the manufacturer's instructions. Sample OD values were measured using a SimoliNano microspectrophotometer (General Electric, USA), and concentrations were calculated based on the standard curve.

2.7
qPCR
Total RNA was extracted using an RNA extraction kit (Alfalfa, China). RNA purity and concentration were measured using a SimoliNano microspectrophotometer (General Electric, USA). cDNA was synthesized using a reverse transcription kit (Alfalfa, China). miRNA was reverse transcribed using a stem‐loop RT kit (AG, China). qPCR was performed using SYBR Green PCR kits (Alfalfa, China) on a Thermo Fisher Scientific PCR system following manufacturer protocols.

2.8
Western Blot
Total protein was extracted using RIPA lysis buffer containing PMSF (Coolaber, China). Protein concentration was determined with a BCA kit (Abbkine, China). Proteins were separated by 10% SDS‐PAGE and transferred to PVDF membranes (Millipore, USA). Membranes were blocked with rapid blocking buffer (Cellor Lab, Shanghai) for 30 min and incubated overnight at 4°C with primary antibodies (1:500, Uptobiotech, China) and β‐actin (1:500, Abcam, USA). After incubation with HRP‐conjugated secondary antibodies for 1 h at room temperature, protein bands were visualized using enhanced chemiluminescence reagent (Abbkine, China) and imaged with a Non‐2500B gel imager (Tanon, China). Quantification was performed using Image‐Pro Plus 6.0 software.

2.9
Statistical Analysis
Statistical analysis was performed using SPSS 28.0. Measurement data are presented as mean ± standard deviation (SD). Data transformation and graphing were conducted using GraphPad Prism 9.5.0. All experiments were independently repeated three times with three technical replicates each. Comparisons between two groups were performed using an independent samples t‐test, and multi‐group comparisons were conducted using one‐way ANOVA. A p‐value < 0.05 was considered statistically significant.

3
Results
3.1
Bioinformatics Analysis
Analysis of the GSE208007 dataset from the GEO database using GEO2R identified a total of 8600 DEGs specific to HCC tissues compared to normal tissues. From the FerrDb database, 524 ferroptosis‐related genes were obtained. The intersection of these two datasets yielded 199 overlapping DEGs associated with ferroptosis‐related HCC, as shown in the Venn diagram (Figure 1). Functional annotation using the ferroptosis database indicated that these DEGs were primarily involved in driving or regulating ferroptosis (Figure 1). GO enrichment analysis revealed that the biological processes (BP) of the DEGs were mainly associated with cellular response to hypoxia, oxidative stress, MAPK cascade, and cellular iron ion homeostasis. For cellular components (CC), the DEGs were enriched in the cytoplasm, RNA polymerase II transcription regulator complex, nucleus, and exosomes. Molecular functions (MF) were predominantly related to enzyme binding, protein binding, and ubiquitin‐protein ligase binding. KEGG pathway analysis indicated significant enrichment of the DEGs in pathways such as chemical carcinogenesis–reactive oxygen species, human cytomegalovirus infection, hepatocellular carcinoma, and the mTOR signaling pathway (Figure 1). A PPI network of the overlapping DEGs was constructed using the STRING database and visualized with Cytoscape. Hub genes were identified based on degree ranking. The top 10 hub genes with the highest connectivity were HIF1A, PPARG, IL6, JUN, MTOR, SIRT1, IL1B, EGFR, ALB, ZEB1, and GSK3B. Based on the literature review, ZEB1 was the only highly connected hub gene simultaneously associated with the Wnt signaling pathway and Loxl2 [21]. Therefore, ZEB1 was selected as a key ferroptosis‐related differentially expressed gene in HCC for subsequent experimental validation.

3.2
Isolation and Purity Identification of CD133+/EpCAM+ LCSCs by Flow Cytometry
Flow cytometry was used to analyze the subpopulation distribution of CD133 and/or EpCAM antibody‐positive cells in the Hep3B cell line. The results showed that double‐negative control cells accounted for 99.4%, CD133+ single‐positive cells for 98.60%, EpCAM+ single‐positive cells for 58.6%, and CD133+/EpCAM+ double‐positive Hep3B cells (LCSCs) for 53.6% (Figure 2). After further culture of the isolated double‐positive LCSCs under sphere‐forming conditions, cells in the logarithmic growth phase were subjected to flow cytometric purity analysis, which indicated that CD133+/EpCAM+ LCSCs accounted for 94% of the population.

3.3
Knockdown of Wnt3a and/or Loxl2 Promotes Ferroptosis
Stable LCSC cell models with knockdown of Wnt3a and/or Loxl2 were established and divided into four groups: LCSCs, Wnt3a− LCSCs, Loxl2− LCSCs, and Wnt3a−/Loxl2− LCSCs. The ferrous iron colorimetric assay showed that Fe2+ levels were elevated in all knockdown groups, with the most significant increase observed in the double‐knockdown group, indicating enhanced ferroptosis in LCSCs following gene knockdown, which was most pronounced in the double‐knockdown group. The trend in Fe2+ content, reflecting the extent of ferroptosis, across groups was as follows: LCSCs < Wnt3a− LCSCs < Loxl2− LCSCs < Wnt3a−/Loxl2− LCSCs (Figure 3). ELISA results demonstrated that the level of MDA, a lipid peroxidation metabolite, increased after knockdown of Wnt3a and/or Loxl2, with the highest MDA content found in the double‐knockdown group. This suggests that knockdown of Wnt3a or Loxl2 promotes ferroptosis in LCSCs, with the strongest effect seen in the double‐knockdown group. In contrast, GSH levels decreased following knockdown, reaching the lowest level in the double‐knockdown group. These results further support that knockdown of Wnt3a and/or Loxl2 enhances ferroptosis in LCSCs, with the most substantial effect observed in the double‐knockdown group.

3.4
General Condition and In Vivo Imaging of Transgenic Orthotopic Liver Cancer Mice
After model establishment, the daily activities (including mental state, diet, water intake, and excretion) of the mice were observed every 2 days, and body weight was monitored. Analysis of body weight data revealed a declining trend across all groups, with the most pronounced weight loss observed in the WT group (Figure 4). Throughout the experiment, all groups exhibited normal feeding, drinking, and excretion without significant adverse reactions. On day 14 post‐modeling, in vivo imaging showed bioluminescent signals in the hepatic region of the abdomen in all groups (WT, Wnt3a−, Loxl2−, and Wnt3a−/Loxl2−), with a decreasing trend in signal intensity. The WT group exhibited the strongest bioluminescence, while the Wnt3a−/Loxl2− group showed the weakest signal (Figure 4).
On day 30, mice were dissected, and livers were collected. Macroscopic observation indicated that the WT group had the largest tumor coverage area, while the Wnt3a−/Loxl2− group had the smallest (Figure 4). Statistical analysis of tumor volume showed a significant reduction in all knockout (KO) groups compared to the WT group (p < 0.001). The double‐knockout group (Wnt3a−/Loxl2−) showed significantly smaller tumor volumes compared to both the Wnt3a− and Loxl2− groups (p < 0.001 and p < 0.05, respectively). Additionally, the Loxl2− group had smaller tumor volumes than the Wnt3a− group (p < 0.05) (Figure 4). The tumor inhibition rates, calculated according to formula (3), were 51% in the Wnt3a− group, 71% in the Loxl2− group, and 93% in the Wnt3a−/Loxl2− group. HE staining results revealed significant structural and cellular atypia in tumors across all groups. Tumor cells exhibited pronounced pleomorphism in size and shape, loss of polarity, enlarged and hyperchromatic nuclei, increased nuclear‐to‐cytoplasmic ratio, frequent abnormal mitotic figures, and abundant blood vessel formation (Figure 4). These features are characteristic of typical malignant histopathology.

3.5
Alterations in Ferroptosis‐Related Indicators in Tumor Tissues of Wnt3a and/or Loxl2 Knockout Mice
The results showed that, compared with the WT group, the tumor tissues of KO mice exhibited significantly increased levels of Fe2+ and MDA (p < 0.001) and significantly decreased GSH levels (p < 0.01). Moreover, the changes in Fe2+, MDA, and GSH were more pronounced in the Wnt3a−/Loxl2− group than in either the Wnt3a− group (p < 0.001) or the Loxl2− group (p < 0.01, p < 0.001, and p < 0.05, respectively). Additionally, the reduction in GSH was greater in the Loxl2− group than in the Wnt3a− group (p < 0.01) (Figure 5). These findings indicate that knockout of Wnt3a and/or Loxl2 promotes ferroptosis in mouse liver tumor tissues, with the dual knockout of Wnt3a and Loxl2 exerting a stronger pro‐ferroptotic effect.

3.6
Expression of the Differential Gene ZEB1
Cellular Level: The expression levels of ZEB1 across the four groups were detected using ELISA, qPCR, and Western blot. Results from all three methods consistently demonstrated that knockdown of Wnt3a and/or Loxl2 led to increased ZEB1 expression compared to the non‐knockdown group. The most pronounced upregulation was observed in the double‐knockdown group. Furthermore, ZEB1 levels in the Loxl2− group were significantly higher than those in the Wnt3a− group. All intergroup differences were statistically significant (Figure 6A–C).
Animal Level: Similarly, ZEB1 expression in tumor tissues was assessed via ELISA, qPCR, and Western blot. The results indicated that ZEB1 levels in all knockout (KO) groups were significantly higher than in the WT group (p < 0.001). Moreover, the Wnt3a−/Loxl2− group exhibited higher ZEB1 expression compared to either single‐knockout group (p < 0.001). Additionally, ZEB1 expression in the Loxl2− group was greater than that in the Wnt3a− group (p < 0.01). These findings indicate that knockdown of Wnt3a and/or Loxl2 promotes ZEB1 expression, with the dual knockout producing the most substantial effect (Figure 6D–F).

4
Discussion
Liver cancer, a malignant tumor severely impacting human health and quality of life, is prone to chemotherapy resistance and exhibits unique immunosuppressive properties. Therefore, research into its diagnosis and treatment is of great significance for human survival and development [22]. Cancer stem cells (CSCs) possess remarkable “anti‐death” characteristics. Ferroptosis, as a novel form of cell death, can promote tumor cell apoptosis by enhancing ferroptotic activity [23, 24, 25]. In‐depth exploration of the mechanisms through which ferroptosis regulates CSC death may open new avenues for HCC treatment and improve patient prognosis. This study demonstrates that knockdown of Wnt3a and/or Loxl2 promotes ferroptosis in LCSCs and transgenic mice, inhibits tumor growth, with the most pronounced effects observed in the double‐knockdown group. Numerous basic and clinical studies have explored promoting or inducing ferroptosis for cancer therapy [26, 27, 28].
LCSCs are considered a fundamental cause of phenotypic and functional heterogeneity in HCC. Through genetic mutations, epigenetic disruptions, signaling pathway dysregulation, or alterations in the microenvironment, they play a key role in regulating HCC tumorigenicity, metastasis, recurrence, and therapy resistance [11, 29, 30]. LCSCs express various surface markers, such as EpCAM, CD133, CD44, CD13, CD90, OV‐6, CD47, and side population phenotype [11]. Studies show that cells co‐expressing EpCAM and CD133 exhibit stronger CSC traits and enhanced tumor‐initiating capacity [31]. Therefore, this study utilized Hep3B cells double‐positive for CD133 and EpCAM to obtain an LCSC subpopulation with higher stemness for experimental investigation.
Wnt3a is a key ligand in the canonical Wnt/β‐catenin signaling pathway. It promotes β‐catenin stability and nuclear translocation to activate downstream target genes, playing a crucial role in tumor cell proliferation and metastasis [32]. Loxl2, as a multifunctional protein, contributes to the development and progression of solid tumors through various mechanisms, including regulating tumor cell proliferation, epithelial–mesenchymal transition (EMT), migration, extravasation, and formation of pre‐metastatic niches at distant sites [33]. High expression of both Wnt3a and Loxl2 is associated with poor prognosis in HCC [34, 35]. Our research group has long focused on the mechanisms by which Wnt3a and Loxl2 influence LCSCs in HCC progression. Previous experiments confirmed that knockout of these genes inhibits LCSC proliferation, and Wnt3a knockdown reduces Loxl2 expression in LCSCs, suggesting that the Wnt3a/Loxl2 axis modulates the biological functions of LCSCs in vitro.
Ferroptosis can selectively induce tumor cell death, enhance the efficacy of chemotherapeutic drugs, and reduce damage to normal cells [36]. Direct functional assessment of ferroptosis primarily includes the measurement of lipid peroxidation levels, such as using C11‐BODIPY or Liperfluo probes to dynamically monitor the real‐time process of lipid peroxidation. As a terminal product of lipid peroxidation, MDA serves as a classical and core functional indicator of ferroptosis, though it does not reflect real‐time dynamics. GSH levels are largely regulated by GPX4, a key inhibitor of ferroptosis, while increased Fe2+ is a necessary condition for ferroptosis; both GSH and Fe2+ are considered indirect predictive markers of ferroptosis. In this study, the use of Fe2+, GSH, and MDA assays to monitor ferroptosis has certain limitations. Future research will incorporate more direct functional indicators for further investigation. Studies have found intricate crosstalk between the Wnt signaling pathway and ferroptosis in cancer, regulating tumor development and drug resistance [37]. Various cytokines, such as UCHL3, can modulate cancer cell ferroptosis through the Wnt/β‐catenin pathway and influence tumor immune escape mechanisms [38, 39]. A study in endometrial cancer indicated that Loxl2 might promote cancer cell survival and ferroptosis by activating the PI3K/AKT signaling pathway [18]. Based on our previous research, this study employed bioinformatic analysis combined with in vivo and in vitro models and identified ZEB1 as a ferroptosis‐promoting factor in HCC associated with Wnt and Loxl2. In vitro experiments showed that knockdown of Wnt3a and Loxl2 in LCSCs altered ferroptosis markers (Fe2+, MDA, GSH), indicating increased ferroptosis, with the most significant effect observed in the double‐knockdown group, suggesting a potential synergistic effect. In vivo experiments demonstrated that knockout of Wnt3a and Loxl2 inhibited the growth of orthotopic tumors from mouse LCSCs, significantly elevated ferroptosis levels (Fe2+, MDA, GSH) in tumor tissues, and resulted in stronger tumor suppression with dual knockout. These results indicate that Wnt3a and Loxl2 can influence HCC development by regulating LCSC proliferation. The mechanism may involve changes in the oxidative stress network due to interaction between the Wnt/β‐catenin axis and the Nrf2‐antioxidant system, decreased transferrin expression, increased MDA, and reduced GSH levels [40, 41, 42].
As a transcription factor, ZEB1 can activate the Wnt/β‐catenin pathway and influence the EMT process [43, 44]. It can also promote Loxl2 expression, affecting tumor invasion and metastasis [45, 46]. Additionally, ZEB1 can regulate ferroptosis sensitivity through multiple signaling axes or pathways, thereby promoting cancer cell ferroptosis [47, 48, 49]. In osteosarcoma, miR‐144‐3p enhances ferroptosis by negatively regulating ZEB1, thereby inhibiting the proliferation, migration, and invasion of osteosarcoma cells [47]. In pancreatic cancer, suppression of ZEB1 inhibits the expression of heme oxygenase HMOX1, promotes ferroptosis, augments the cytotoxic effects of oxaliplatin, and increases drug sensitivity [49]. In lung cancer, the combination of D‐boronol and cisplatin inhibits ZEB1 and EMT, promotes ferroptosis, and enhances the antitumor efficacy of the drug [50]. This study demonstrated that knockdown of Wnt3a and Loxl2 significantly upregulates ZEB1 expression in both LCSCs and mouse xenograft tumor tissues, showing a consistent trend with ferroptosis levels, with the most pronounced effect observed in the dual‐gene knockout group. These results suggest that ZEB1 may function upstream of the Wnt3a/Loxl2 signaling axis in regulating ferroptosis, and its expression exhibits compensatory upregulation following gene knockdown. Based on these findings, we propose a working hypothesis: as an upstream regulator of Wnt3a and Loxl2, ZEB1 suppression enhances ferroptosis in LCSCs, thereby promoting tumor cell apoptosis. Dual‐gene knockdown appears to produce a synergistic effect, maximizing the activation of both ferroptosis and apoptotic pathways. We further speculate that ZEB1 may normally inhibit ferroptosis and subsequent LCSC apoptosis through activating Wnt3a/Loxl2 signaling, whereas ZEB1 inhibition sensitizes LCSCs to ferroptosis, a process potentially involving the regulation of EMT.
Integrating bioinformatic analysis with in vivo and in vitro experiments, this study reveals for the first time the mechanism by which dual knockout of Wnt3a and Loxl2 synergistically induces ferroptosis in LCSCs and significantly suppresses tumor growth through ZEB1 upregulation, thereby providing a novel potential therapeutic target for hepatocellular carcinoma. Nevertheless, this study has certain limitations. Future work will employ mechanistic experiments to further investigate the relationship between Wnt3a and/or Loxl2 and ferroptosis, as well as the regulatory interplay between ZEB1 and Wnt3a/Loxl2. These investigations will further elucidate the precise role of ZEB1 in this regulatory axis, ultimately providing a more solid theoretical foundation and potential therapeutic targets for HCC treatment.
Wnt3a/Loxl2 knockdown promotes the level of ferroptosis in LCSC and inhibits in situ tumor growth, and simultaneous knockdown of both genes has a more pronounced tumor‐suppressive effect, and ZEB1 may be an important regulator of this mechanism of action.

Author Contributions

Guanghui Ren: conceptualization, data curation, investigation, visualization, writing – original draft. Qingwei Cong: conceptualization, investigation, visualization, data curation, writing – original draft. Jing Wang: formal analysis, investigation, methodology, visualization. Wenyue Gao: formal analysis, investigation, visualization. Yunpeng Guan: writing – review and editing. Lianxin Zhu: supervision, writing – review and editing. Ying Zhu: conceptualization, writing – review and editing.

Funding
This study was funded by the National Natural Science Foundation of China (grant no. 82274260) and the Natural Science Foundation of Liaoning Province (grant no. 2015020305).

Ethics Statement
All procedures were approved by the Dalian Medical University Institutional Animal Ethics Committee (approval no. AEE2305).

Consent
The authors have nothing to report.

Conflicts of Interest
The authors declare no conflicts of interest.