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. 2025 Oct 21;16:1926. doi: 10.1007/s12672-025-03761-z

Pan-cancer analysis of role of LOXL4 and experiment validation in osteosarcoma

Wenjie Chen 1,#, Fujie Xie 1,#, Jie Lv 1,#, Dixi Huang 1, Ronghao Zhong 1, Zhijia Wen 1, Jiangsen Sun 1, Shaowei Zheng 1, Weile Liu 1, Haobo Zhong 1,✉, Shoubin Huang 1,✉
PMCID: PMC12540211  PMID: 41118012

Abstract

Background

Osteosarcoma is an aggressive bone malignancy with high metastatic potential and poor prognosis, primarily affecting children and adolescents. Although lysyl oxidase-like 4 (LOXL4) has been implicated in tumor progression, its functional role and mechanistic contributions in Osteosarcoma remain unclear.

Methods

We performed integrated bioinformatics analysis using GTEx, TARGET, and TCGA datasets to evaluate LOXL4 expression and prognostic significance across cancers. Genetic alteration, immune infiltration, and RNA methylation analysis were carried to explore the different roles of LOXL4 in tumors. Functional assays, including Cell Counting Kit-8 (CCK-8), colony formation, and Matrigel transwell assays, were conducted in Osteosarcoma cell lines. Besides, western blotting and gene set enrichment analysis (GSEA) were used to explore LOXL4’s mechanistic roles.

Results

LOXL4 was significantly upregulated in Osteosarcoma tissues and associated with poor patient survival. Pan-cancer analysis revealed LOXL4 is upregulated in multiple cancer types and exhibits tumor-type-specific genetic alteration patterns, most frequently mutated in melanoma and amplified in endometrial carcinoma. Besides, LOXL4 expression significantly correlated with immune infiltration levels, the expression of immune checkpoint molecules, and RNA methylation across multiple cancers. Functional experiments demonstrated that LOXL4 knockdown suppressed cell proliferation, invasion, and epithelial–mesenchymal transition (EMT), whereas LOXL4 overexpression enhanced these malignant phenotypes. Mechanistically, LOXL4 activated the Wnt/β-catenin signaling pathway. Inhibition of Wnt/β-catenin signaling with XAV-939 reversed LOXL4-induced oncogenic effects.

Conclusion

LOXL4 promotes Osteosarcoma progression via Wnt/β-catenin-mediated EMT and cell proliferation. Its pan-overexpression and associations with the tumor immune microenvironment underscore its potential as a therapeutic target. Targeting LOXL4 or its downstream pathway may offer novel therapeutic strategies for Osteosarcoma.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-025-03761-z.

Keywords: LOXL4, Osteosarcoma, Progression, Wnt/beta-catenin signaling pathway, Bioinformatics

Introduction

Osteosarcoma primarily impacts children and adolescents [1]. The disease is characterized by strong local invasion and high susceptibility to distant metastasis, significantly contributing to its poor survival outcomes [2]. Studies indicate that 10% to 15% of individuals with Osteosarcoma eventually develop metastases [3–5]. Although advancements in adjuvant chemotherapy have improved survival outcomes for many cancers, the metastatic potential of Osteosarcoma continues to pose a major therapeutic challenge, contributing significantly to mortality [6, 7]. Treatment options for Osteosarcoma remain limited. This underscores an urgent need to understand its progression mechanisms and develop more effective therapies.

The lysyl oxidase (LOX) family comprises five members (LOX and lysyl oxidase-like 1–4). These enzymes maintain tissue architectural stability by promoting collagen and elastin cross-linking in the extracellular matrix (ECM) [8]. Collagen is a critical element of the ECM and a primary component of the basement membrane (BM). Notably, aberrant collagen expression has been identified as an early marker of invasive cancer characteristics [9–11]. Collagen changes are commonly seen in cancer cells from different origins. These alterations can impact tumor behavior, such as invasion and metastasis, influencing the progression and treatment outcomes of cancers including Osteosarcoma [12]. Understanding ECM components and their interactions with tumor cells is crucial for developing targeted therapies to potentially inhibit the aggressive and invasive nature of Osteosarcoma. Accumulating evidence highlights the diverse biological functions of the LOX family, particularly its critical roles in tumor development, epithelial-mesenchymal transition (EMT), and metastasis [13–15]. Among its members, lysyl oxidase-like (LOXL4), a secretory lysyl oxidase, has been increasingly associated with tumor growth and progression [16, 17] and is also classified among BM genes (BMGs) [18]. LOXL4 contributes to gastric cancer progression through FAK/Src pathway activation [8] and regulates metastasis, angiogenesis, and cell-matrix adhesion in liver cancer [19]. These studies indicate that LOXL4 is an oncogene. Additionally, LOXL4 catalyzes cross-linking of cell surface annexin A2 into multimers, inhibiting integrin β-1 internalization, and promoting cancer cell outgrowth [20]. Despite these findings, the roles of LOXL4 in different cancers remain inconsistent and even contradictory [21–23], and its broader biological functions are still not fully elucidated. In particular, the role of LOXL4 in Osteosarcoma has not been previously explored. We hypothesize that LOXL4 may influence BM properties and facilitate Osteosarcoma progression.

In this study, we demonstrate that LOXL4 is significantly overexpressed in Osteosarcoma tissues and is associated with poor clinical outcomes. Mechanistically, we show that LOXL4 promotes invasion, proliferation, and EMT in Osteosarcoma cells via activation of the Wnt/β-catenin signaling pathway. Our findings identify LOXL4 as a potential oncogene in Osteosarcoma and suggest its therapeutic relevance as a candidate target for intervention.

Materials and methods

Gene expression and survival prognosis analysis

The Osteosarcoma sample data and clinical sources were acquired from the University of California, Santa Cruz (UCSC) Xena platform (https://xena.ucsc.edu/). Patients with Osteosarcoma without survival data were excluded, resulting in a final cohort of 85 patients with Osteosarcoma having complete survival and gene expression data for analysis. Differentially expressed genes (DEGs) between Osteosarcoma and normal samples were identified on the count data using DESeq2, applying a significance threshold of p < 0.05 and |log2fold change (FC)| >1. A volcano plot and heatmap of DEGs were generated using the ggplot2 and ComplexHeatmap packages, respectively. To investigate the effect of LOXL4 gene on the survival of Osteosarcoma patients, univariate Cox regression analysis was conducted using the survival package. Kaplan-Meier (K-M) survival analysis was then employed to compare survival differences between risk groups. Abbreviations and full names of the corresponding tumors are given in Supplementary Table 1. The CCLE dataset (https://portals.broadinstitute.org/ccle) was utilized to obtain the mRNA expression matrix in tumor cell lines. The analysis was conducted using the ggplot2 package in R software.

Single-cell analysis

Besides, this study estimated the LOXL4 expression level in cell types across numerous cancers via Tumor Immune Single-cell Hub (TISCH) database, an online platform designed for multiple single-cell analysis (http://tisch.comp-genomics.org/home/). We download the Osteosarcoma single-cell data (GSE152048) in GEO dataset, and the influence of unique molecular identifiers (UMIs) and mitochondrial content (%) was eliminated using Seutat’s ScaleData function. Subsequently, the batch effect was removed using the Harmony R package. The top 30 principal components and top 2000 variable genes were selected for cell clustering and the uniform manifold approximation and projection (UMAP) visualization [24]. Re-cluster the cells using the t-distributed stochastic neighbor embedding method [25].

Analysis of pan-cancer gene expression

We downloaded the uniformly normalized pan-cancer dataset from UCSC: TCGA, TARGET, GTEx (PANCAN, N = 19131, G = 60499). Further, we extracted the expression data of the LOXL4 gene in each sample from it. In addition, we also filtered the samples with an expression level of 0. Further, we performed a log2(x + 0.001) transformation on each expression value. Finally, we also eliminated the Cancer types with less than 3 samples in a single cancer type. Finally, the expression data of 34 cancer types were obtained. We used R software (version 3.6.4) to calculate the expression differences between normal samples and tumor samples in each tumor, and used unpaired Wilcoxon Rank Sum and Signed Rank Tests for significance analysis of the differences. Then, the different expression situations of LOXL4 were visualized by the Sangerbox website (http://www.sangerbox.com/) [26]. At the same time, we use UALCAN online tools (http://ualcan.path.uab.edu/) and the Gene Expression Profiling Interactive Analysis (GEPIA2) website to analyze the generic cancer data in the Osteosarcoma and LOXL4 expression in normal tissue. Besides, we used the “Gene_DE” function of the Tumor Immune Estimation Resource, version 2 (TIMER2.0, http://timer.cistrome.org/) website to observe the expression of LOXL4 in tumor tissues and corresponding normal tissues.

Analysis of pan-cancer gene survival prognosis

We use the R package maxstat (Maximally selected rank statistics with several p-values approximations), Version 0.7–25 calculated the optimal cutoff value of LOXL4. Based on this, the patients were divided into high and low groups. Further, the survfit function of the R software package survival was used to analyze the prognostic differences between the two groups. The log-rank test method was used to evaluate the significance of prognostic differences among samples in different groups.

Analysis of protein structure and subcellular localization

AlphaFold is an effective artificial intelligence (AI) technology that uses deep learning on both genetic and structural data to predict protein structures from amino acid sequences, and it also assesses the amount of trust in these forecasts to assure their dependability [27]. In this study, we used AlphaFold to identify the three-dimensional (3D) structures of LOXL4. The Human Protein Atlas (HPA) (http://www.proteinatlas.org) includes high-resolution images of immunohistochemistry (IHC) [28]. The HPA database provides comprehensive insights into the spatial distribution of proteins within human cells, tissues, and organs. To determine the subcellular localization of LOXL4, we employed the HPA databases.

Genetic alterations and methylation levels analysis

The analysis of genetic alterations was conducted by cBioPortal (https://www.cbioportal.org/), and we downloaded the detailed information of alteration frequencies and types. Moreover, we also download all the level4 TCGA samples of Simple Nucleotide mutation Variation data set from GDC database (https://portal.gdc.cancer.gov/) and then integrate the sample data. The information of the genetic alterations was obtained by using the R software package maftools. We extracted the expression data of the LOXL4 gene along with 44 marker genes representing three types of RNA modifications—m1A (10 genes), m5C (13 genes), and m6A (21 genes)—from each tumor sample. Next, we calculated the Spearman correlation between LOXL4 and the marker genes of RNA modifications.

Immune analysis

We analyzed the relationship between LOXL4 and the tumor immune-related situation using SangerBox. We conducted an investigation to determine the correlation between LOXL4 and 60 immune checkpoints, marker genes of 5 types of immune-associated biomarkers, and 3 immune infiltration scores (StromalScore, ImmuneScore, and EstimateScore). These scores predicted the tumor immune microenvironment by calculating the abundance of stroma and immune cells in tumor tissues. Among them, StromalScore reflects the interstitial components (such as fibroblasts and blood vessels), ImmuneScore represents the infiltration level of immune cells (such as lymphocytes and macrophages), and ESTIMATEScore is a combination of the StromalScore and ImmuneScore, which can indirectly predict tumor purity [29]. The higher the score, the more non-tumor cell components are present and the lower the tumor purity. Studies have shown that a higher StromalScore is usually associated with lower tumor purity and a relatively better prognosis [30].

Gene enrichment analysis

Gene set enrichment analysis (GSEA) was used to explore the potential biological functions and signaling pathways regulated by LOXL4 in Osteosarcoma. To achieve this, the “gmt” data of the hallmark gene set (h.all.v7.4.symbols.gmt) was extracted from the MSigDB database (https://www.gsea-msigdb.org/gsea/index.jsp). The analysis process was carried out via R packages such as “clusterProfiler,” “enrichplot,” and “ggplot2,” and the results were visualized.

Cell culture and transfection

The hFOB1.19, MNNG/HOS, MG63, and U2OS cell lines were sourced from the Cell Bank of the Shanghai Chinese Academy of Sciences. Culture media were differentially employed: hFOB1.19 cells in hFOB-specific medium, MNNG/HOS and MG63 in Dulbecco’s modified Eagle medium, and U2OS in McCoy’s 5A medium. GenePharma (Shanghai, China) supplied the pcDNA3.4- LOXL4, pcDNA3.4-NC, two LOXL4-targeting siRNAs (si-LOXL4#1: 5’-GCUGAAGAGCCUGACGAAUTT-3’; si-LOXL4#2: 5’-CCAAGUCUGCGGAUCACAUTT-3’) and corresponding negative control (si-NC). Cellular transfection was conducted following standard protocols using Lipofectamine™ 3000 (Thermo Fisher Scientific, Waltham, MA, USA).

Western blotting and reagents

Cellular lysis was performed using RIPA buffer (Beyotime, China) after phosphate-buffered saline (PBS) washing. Lysates underwent centrifugation (13,000 rpm, 15 min, 4 °C), and protein quantification was conducted via bicinchoninic acid (BCA) assay (FudeBiologic Technology, China). Electrophoretic separation using 4–10% SDS-PAGE preceded protein transfer to polyvinylidene difluoride membranes. Blocking with Protein Free Rapid Blocking Buffer (EpiZyme, China) was followed by overnight incubation with target-specific primary antibodies at 4 °C. Anti-β-actin (22957, 1:2000, Signalway Antibody), anti-LOXL4 (88186, 1:1000, Abcam), anti-Vimentin (60330-1-Ig, 1:50000, Proteintech), anti-N-cadherin (66219-1-Ig, 1:50000, Proteintech), anti-E-cadherin (20874-1-AP, 1:20000, Proteintech), anti-β-Catenin (51067-2-AP, 1:20000, Proteintech), anti-Cyclin D1 (26939-1-AP, 1:20000, Proteintech), anti-c-MYC (10828-1-AP, 1:10000, Proteintech), Goat anti-mouse IgG H&L (HRP) (ab205719, 1:10000, Abcam), and Goat anti-rabbit IgG H&L (HRP) (ab205718, 1:30000, Abcam). MCE (USA) provided the pathway inhibitor XAV-939 (HY-15147).

Colony formation assays

A cell suspension containing 200 quantified viable cells was plated into 12-well culture plates post-transfection [31]. Throughout the 14-day culture period, fresh medium was replaced every 3 days. The wells were cleaned with PBS, the colonies were fixed with methanol for 30 min, colored with crystal violet, and photographed using a camera.

Cell viability assay

In 96-well plates seeded with 3 × 10³ cells/well, Cell Counting Kit-8 (CCK-8) reagent (FudeBiologic Technology, China) was introduced at 24, 48, or 72 h post-transfection, followed by 2 h incubation. Optical density measurements at 450 nm were conducted using an ELISA microplate reader.

Matrigel transwell assays

Transwell upper chambers (Corning, USA) were loaded with 2 × 10⁴ cells/plate suspended in 200 µl serum-free medium, coated with 60 µl Matrigel matrix (Corning, USA). Lower compartments contained 600 µl serum-supplemented medium (10% FBS). Subsequent to 48 h incubation at 37 °C, chamber membranes underwent paraformaldehyde fixation and 20-min crystal violet staining. Migratory cell quantification was performed using Olympus light microscopy (Japan).

Statistical analysis

Statistical evaluations were conducted using GraphPad Prism (version 10.0; USA) and SPSS software, with significance thresholds defined at p < 0.05.

Results

Identification of hub genes in osteosarcoma

We analyzed gene expression using GTEx and TARGET datasets, comprising 386 normal tissues and 88 Osteosarcoma samples, respectively. Differential expression was visualized via a heatmap and a volcano plot (Fig. 1A and B). We identified intersecting genes among those upregulated in tumors (p < 0.05), prognostic genes (p < 0.05), and BMGs, yielding 4 hub genes (Fig. 1C). Among these, COL13A1, ROBO1, and EFEMP2 have established roles in Osteosarcoma progression and prognosis [32–35], supporting the validity of our screening approach. While LOXL4 was also identified as a prognostic hub gene, its functional significance in Osteosarcoma remained unclear, prompting further investigation.

Fig. 1.

Fig. 1

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Identification of key differentially expressed genes in Osteosarcoma. A Heatmap illustrating upregulated and downregulated genes in Osteosarcoma compared to normal tissue. B Volcano plot highlighting the identification of genes with significant expression changes. C Venn diagram showing common genes between upregulated genes in tumor tissue, genes highly expressed with poor prognosis, and BMGs. D Analysis of GTEx and TARGET datasets reveals significantly elevated expression of LOXL4 in Osteosarcoma tissues. E Left: Dimensionality reduction visualization displays cellular subpopulations through t-SNE clustering, with color-coded annotations distinguishing distinct cellular subtypes. Center: Spatial mapping of transcriptional profiles illustrates target gene expression gradients across cell clusters using a nonlinear dimensionality reduction approach. Expression intensity is represented by a gradient color scale (low: dark hues; high: light chromatic values), correlating with transcript abundance. Right: Quantitative expression analysis demonstrates differential transcriptional activity of target genes among cellular subgroups via categorical comparison, visualized through a barplot format encoding expression magnitudes. F Kaplan–Meier survival curves correlating the expression of LOXL4 with Osteosarcoma prognosis. G The GTEx, TCGA, and TARGET databases show the expression levels of LOXL4 in both cancerous and healthy tissues. H Expression of LOXL4 in Osteosarcoma cell lines and hFOB1.19 cells at translational levels. I Three-dimensional protein structure of LOXL4. J-K The subcellular location of LOXL4 is shown by HPA database and immunofluorescence staining. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001

The results of bioinformatics analysis indicate that LOXL4 was significantly upregulated in Osteosarcoma (Fig. 1D). Single-cell analysis of GSE162454 data across 8 cell types confirmed higher LOXL4 expression in Osteosarcoma compared to osteoblasts (Fig. 1E). Kaplan–Meier survival analysis indicated that elevated LOXL4 expression was associated with poorer prognosis (Fig. 1F).

Pan-cancer analysis revealed differential LOXL4 expression across multiple tumor types compared to normal tissues (Fig. 1G and Supplementary Figure. 1). Furthermore, high LOXL4 expression correlated with worse prognosis in GBM, GBMLGG, LGG, OV, STAD, and UVM (Supplementary Figure. 2).

Western blotting confirmed higher LOXL4 protein levels in Osteosarcoma cell lines (MNNG/HOS, MG63, U2OS) than in normal osteoblasts (Fig. 1H). The predicted 3D structure of LOXL4 is shown in Fig. 1I. Utilizing data from the HPA database, LOXL4 was found to predominantly localize to vesicles within U2OS cells, as depicted in Fig. 1J. Notably, the subcellular localization of LOXL4 in U2OS cells was determined by employing nucleus, microtubule, and endoplasmic reticulum (ER) markers (Fig. 1K).

Genetic alteration analysis

Genomic analysis via cBioPortal revealed tumor-specific LOXL4 alterations (Fig. 2A). Melanoma showed the highest alteration frequency (8.41%), primarily missense mutations. Soft tissue sarcoma exhibited 5.88% alterations, mainly deep deletions, while endometrial carcinoma had 5% frequency, predominantly amplifications. Comprehensive genomic characterization data, including alteration categories, molecular positions, and affected case numbers, are systematically detailed in Fig. 2B.

Fig. 2.

Fig. 2

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Genomic alteration profiles of LOXL4 across different cancer types. A The frequency and types of LOXL4 genetic alterations from the cBioPortal database, and alteration types mainly include missense mutations, amplifications, and deep deletions. B Structural mapping and mutation profile of LOXL4 across various cancers

Immune related analysis

Given the therapeutic relevance of immune checkpoint blockade, we performed the correlational analysis of LOXL4-immune checkpoint proteins (ICP) interactions. LOXL4 showed positive associations with ICPs in many tumors, reinforcing its potential as a novel immunotherapeutic target (Fig. 3).

Fig. 3.

Fig. 3

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Correlation analysis between LOXL4 expression and immune checkpoint (ICP) genes across multiple cancer types. The heatmap depicts correlation coefficients of LOXL4 expression with ICPs, and LOXL4 shows widespread positive correlations with immune checkpoints. *p < 0.05

Pan-cancer analyses of LOXL4 revealed its broad immunomodulatory roles. Systematic co-expression profiling demonstrated significant associations between LOXL4 and diverse immune-related biomarkers—including chemotactic factors, receptor systems, major histocompatibility complex (MHC) components, immunosuppressive mediators, and immune-activating signals—across multiple malignancies (Supplementary Figure. 3, Supplementary Table 2).

Furthermore, we also evaluated the correlation between LOXL4 expression and 3 immune scoring (StromalScore, ImmuneScore, and EstimateScore). In most tumor types, the expression of LOXL4 is positively correlated with StromalScore, ImmuneScore, and EstimateScore (Supplementary Table 3).

Analysis of LOXL4 methylation levels in pan-cancer

It is reported that mRNA modifications regulate the occurrence and development of tumors, such as RNA methylation, especially m6A (N6-adenylate methylation) modification, and abnormal methylation may lead to tumorigenesis [36–39]. LOXL4 is correlated with 21 M6A-related genes in tumors, and the figure indicates that the m6A methylation level is mainly positively correlated with LOXL4 in various malignant tumors, including FTO, METTL3, ALKBH5, and METTL14, etc. (Fig. 4 and Supplementary Table 4).

Fig. 4.

Fig. 4

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Correlation between LOXL4 expression and RNA methylation regulators across multiple cancer types. The heatmap displays correlation between LOXL4 and regulators of m1A, m5C, and m6A RNA modifications, including writers, readers, and erasers. *p < 0.05

LOXL4 promotes the proliferation of osteosarcoma cells in vitro

Given the close association of LOXL4 with tumor progression and immunity across multiple cancers based on pan-cancer analyses, we further investigated its specific role in Osteosarcoma. Transcriptional expression profiles of 8 Osteosarcoma cell lines were obtained from the CCLE database (Fig. 5A). Based on these expression patterns, MNNG/HOS and U2OS cells were selected for knockdown experiments, while MNNG/HOS and MG63 cells were chosen for overexpression studies. Following transfection with si-LOXL4 or overexpression (OE) -LOXL4 plasmid, knockdown and overexpression of LOXL4 were confirmed at the protein level by western blotting (Fig. 5B and E). Functional assays revealed that LOXL4 knockdown significantly impaired colony formation ability in U2OS and MNNG/HOS cells, whereas its overexpression enhanced clonogenic growth in MG63 and MNNG/HOS cells (Fig. 5C and F). Consistent with these findings, CCK-8 assays showed that LOXL4 downregulation reduced cell viability, while its upregulation promoted proliferation (Fig. 5D and G). Collectively, these results demonstrate that LOXL4 facilitates the proliferation of F cells.

Fig. 5.

Fig. 5

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Osteosarcoma cell growth is regulated in vitro by LOXL4. A Transcriptomic expression of LOXL4 genes in Osteosarcoma cell lines. B and E Validation of the transfection effect of LOXL4 in Osteosarcoma cell lines. C and F The effect of LOXL4 on Osteosarcoma cells’ ability to colony formation was assessed (black scale bar = 2 mm). D and G The effect of LOXL4 on Osteosarcoma cell line proliferation was assessed using CCK-8 assays. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001

LOXL4 facilitates osteosarcoma cell invasion and induces EMT

To assess LOXL4’s impact on Osteosarcoma cell invasion, Matrigel transwell assays were performed. LOXL4 silencing significantly reduced invasion in U2OS and MNNG/HOS cells, whereas LOXL4 overexpression enhanced invasion in MG63 and MNNG/HOS cells (Fig. 6A and B). Copper-dependent monoamine oxidase enzymes belong to LOXL4 and these enzymes facilitate the oxidative deamination of lysine residues in collagen and elastin, which is essential for the crosslinking of soluble collagen and elastin during the ECM remodeling process [40]. This process is pivotal for EMT, a biological phenomenon enabling polarized epithelial cells to acquire mesenchymal characteristics, thereby promoting tumor migration and invasion [41, 42]. GSEA of Osteosarcoma data revealed enrichment of EMT signaling in the LOXL4 high-expression group, suggesting its role in EMT activation (Fig. 6C–E). Consistently, western blotting showed that LOXL4 silencing increased E-cadherin and decreased vimentin and N-cadherin in U2OS and MNNG/HOS cells, while overexpression produced the opposite effect (Fig. 6F–G). These results indicate that LOXL4 promotes both invasion and EMT in Osteosarcoma cells.

Fig. 6.

Fig. 6

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The Osteosarcoma cells’ capacities for invasion and EMT are affected by LOXL4. A-B Matrigel transwell assays were used to determine if LOXL4 affected the Osteosarcoma cell lines’ capacity for invasion (black scale bar = 400 μm). C-E The results of the gene set enrichment analysis (GSEA) show a positive association between EMT processes and genes in the LOXL4 high expression group. F-G The effect of LOXL4 on the EMT of Osteosarcoma cell lines was evaluated. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001

LOXL4 regulates WNT/β-catenin signaling in osteosarcoma

To elucidate the mechanisms by which LOXL4 influences Osteosarcoma progression, we performed single-sample GSEA (ssGSEA) and GSEA using transcriptomic data from the TARGET database. Patients were stratified into high- and low-LOXL4 groups based on median expression levels. The high-LOXL4 expression group exhibited significant activation of the Wnt/β-catenin signaling pathway (Fig. 7A, B). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis further revealed upregulation of key markers including Cyclin D1, c-Myc, and β-catenin in this group (Supplementary Figure. 4). Moreover, LOXL4 expression positively correlated with WNT/β-catenin signaling marker β-catenin/catenin beta 1 (CTNNB1) mRNA levels (Supplementary Figure. 5). Experimental validation confirmed that LOXL4 knockdown downregulated Cyclin D1, c-Myc, and β-catenin protein expression in U2OS and MNNG/HOS cells (Fig. 7C), while overexpression elevated their levels (Fig. 7D). These results demonstrate that LOXL4 activates the Wnt/β-catenin signaling pathway in Osteosarcoma.

Fig. 7.

Fig. 7

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LOXL4 regulates WNT/β-catenin signaling in Osteosarcoma. A The pathway score between the low and high expression groups of LOXL4 across ssGSEA (Osteosarcoma patients were classified into low and high expression groups based on the median LOXL4 expression level). B Genes in the LOXL4 high-expression group have a positive connection with WNT/β-catenin signaling according to the results of the gene set enrichment analysis (GSEA). C LOXL4 affected the Osteosarcoma cell lines’ levels of Cyclin D1, c-Myc, and β-catenin protein expression. D The impacts of LOXL4 and Wnt/β-catenin signaling pathway inhibitor on the protein expression level of Cyclin D1, c-Myc, and β-catenin of Osteosarcoma cell lines were assessed by western blotting. * p < 0.05, ** p < 0.01, *** p < 0.001

Osteosarcoma cell proliferation, invasion, and EMT are regulated by LOXL4 via WNT/β-catenin signaling

To further validate the functional role of Wnt/β-catenin signaling in LOXL4-mediated oncogenicity, we treated MG63 and MNNG/HOS cells with the Wnt/β-catenin inhibitor XAV-939. XAV-939 significantly suppressed pathway activity, as evidenced by reduced expression of Cyclin D1, c-Myc, and β-catenin. Subsequent overexpression of LOXL4 partially restored the levels of these proteins (Fig. 8A). Cell proliferation assays revealed a reduction in cell viability and growth in MG63 and MNNG/HOS cells upon suppression of the Wnt/β-catenin signaling. Notably, LOXL4 overexpression reversed these inhibitory effects. (Fig. 8B and C). Similarly, Matrigel transwell assays showed that XAV-939 impaired cell invasion, which was rescued by LOXL4 upregulation (Fig. 8D). Furthermore, Wnt/β-catenin inhibition downregulated vimentin and N-cadherin while upregulating E-cadherin, and these effects were likewise attenuated by LOXL4 overexpression (Fig. 8E). These results collectively indicate that LOXL4 promotes malignant phenotypes in Osteosarcoma cells through activation of the Wnt/β-catenin pathway Fig. 9.

Fig. 8.

Fig. 8

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LOXL4 regulates the biological functions of Osteosarcoma cells through the Wnt/β-catenin signaling. A The impacts of LOXL4 and Wnt/β-catenin signaling pathway inhibitor XAV-939 on the protein expression level of Cyclin D1, c-Myc, and β-catenin of Osteosarcoma cell lines. B The impacts of LOXL4 and Wnt/β-catenin signaling pathway inhibitor XAV-939 on the proliferation of Osteosarcoma cell lines. C The impacts of LOXL4 and Wnt/β-catenin signaling pathway inhibitor XAV-939 on the colony-forming ability of Osteosarcoma cell lines (black scale bar = 2 mm). D The impacts of LOXL4 and Wnt/β-catenin signaling pathway inhibitor XAV-939 on the invasion capacity of Osteosarcoma cell lines (black scale bar = 400 μm). E The impacts of LOXL4 and Wnt/β-catenin signaling pathway inhibitor XAV-939 on the EMT process of Osteosarcoma cell lines. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001

Fig. 9.

Fig. 9

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LOXL4 Promotes Osteosarcoma Progression via Wnt/β-catenin Signaling

Discussion

Osteosarcoma’s high metastatic rate is a major driver of its poor prognosis [43]. The lack of effective therapies underscores the need to identify novel molecular drivers. While LOXL4 is a known oncogene in other cancers [8, 19, 20], its role in Osteosarcoma was unclear. Our study addresses this gap and reveals that LOXL4 is significantly upregulated in Osteosarcoma and associated with poor survival, positioning it as a critical prognostic marker. Beyond Osteosarcoma, our pan-cancer analysis uncovered that LOXL4’s genomic alterations are cancer-type specific. Furthermore, we found that LOXL4 expression correlates with immune infiltration and m6A regulators, suggesting its potential role in shaping the tumor immune microenvironment—a previously unexplored aspect of its function. Many cancers are involved in complex interrelationships with the immune microenvironment [44–47]. Additionally, LOXL4 shows significant associations with m6A RNA methylation regulators, indicating potential epigenetic regulatory mechanisms [48]. These multifaceted findings showed LOXL4 may be a key regulator of the tumor immune microenvironment and a promising therapeutic target across cancers.

Next, we demonstrated that LOXL4 enhances Osteosarcoma cell proliferation, invasion, and EMT through experiments. These functional effects are consistent with the known roles of LOX family enzymes in ECM remodeling and EMT induction via their catalytic activity, which includes the production of hydrogen peroxide—a signaling molecule that can further modulate tumor cell behavior [49–55]. GSEA further supported the involvement of LOXL4 in EMT. Our findings not only corroborate previous reports of LOXL4’s oncogenic role in other malignancies [8, 19, 20], but also significantly extend upon the work of Tan et al. (who identified LOXL4 as a prognostic hub gene [56]) by elucidating its functional mechanistic impact in Osteosarcoma. However, the molecular mechanisms through which LOXL4 exerts its oncogenic effects were not explored. Previous studies reported that the Wnt/β-catenin signaling plays a role in cancer progression, metastasis, and chemotherapy resistance [57–61], and the activation of this pathway can promote EMT through the nuclear accumulation of β-catenin and subsequent transcriptional downregulation of E-cadherin [62]. To explore whether LOXL4 affects the progression of Osteosarcoma through Wnt/β-catenin signaling, we performed pathway enrichment analysis and found that the Wnt/β-catenin pathway may be a key mediator of LOXL4-driven oncogenicity. The subsequent western blotting experiments also confirmed that LOXL4 expression positively correlated with the activation of Wnt/β-catenin signaling. Besides, inhibition of Wnt/β-catenin signaling with XAV-939 attenuated the pro-tumorigenic effects of LOXL4 overexpression, confirming the functional dependency of LOXL4 on this pathway.

Several limitations of our study should be acknowledged. First, although we establish a link between LOXL4 and Wnt/β-catenin activation, the exact molecular intermediates remain to be fully elucidated. LOXL4 may regulate the Wnt/β-catenin signaling pathway either by directly modulating key components within the pathway or by indirectly affecting other molecules that, in turn, influence Wnt/β-catenin activity. This requires further experimental exploration in the future. Second, while we employed extensive bioinformatic (including the use of AlphaFold, an effective AI technology for structural prediction) and functional validations, our study did not incorporate broader AI approaches. This is a significant consideration given that AI-integrated methods are revolutionizing biomedical research by identifying biomarkers and improving precision oncology [63]. For instance, AI-driven analysis of transcriptomic data could in the future be leveraged to uncover complex relationships between LOXL4 and the tumor microenvironment or to predict its prognostic relevance across cancers with higher precision [63]. From a translational medicine perspective, our findings posit LOXL4 as a promising therapeutic target in Osteosarcoma. Targeted inhibition of LOXL4 or its downstream Wnt/β-catenin pathway could suppress metastatic progression and improve patient outcomes. However, to advance this potential toward clinical application, future research must include validation using animal models, as well as assessment of LOXL4 inhibition alone or in combination with standard chemotherapy. Furthermore, the integration of LOXL4 expression into clinical decision-making could be powerfully aided by emerging AI-based tools. AI is not only revolutionizing biomarker discovery but also enhancing therapeutic strategies by optimizing choices, as demonstrated by Espinosa et al. [64]. Therefore, future work should also explore developing AI-powered models to incorporate LOXL4 status with other clinical variables, thereby improving risk stratification and personalizing treatment plans for patients. Beyond these scientific and technical considerations, it is also essential to acknowledge the ethical dimensions associated with biomarker discovery and the development of targeted therapies. As precision oncology increasingly relies on biomarker-driven patient stratification and predictive modeling, issues such as equitable access to novel therapies, data privacy in handling large-scale genomic and transcriptomic datasets, and the interpretability of AI-assisted decision-making tools become critical. Addressing these challenges will be necessary to ensure that the translation of LOXL4-targeted strategies into clinical practice is both responsible and inclusive, ultimately benefiting a broader patient population.

Conclusion

In summary, our study provides comprehensive evidence that LOXL4 promotes Osteosarcoma progression through Wnt/β-catenin-mediated proliferation and EMT. Its widespread overexpression and association with the tumor immune microenvironment further support its role as a multifunctional oncogene. Targeting the LOXL4/Wnt/β-catenin axis may offer a novel therapeutic avenue for Osteosarcoma patients.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1. (137.2MB, docx)
Supplementary Material 2. (9.9KB, xlsx)
Supplementary Material 3. (144.8KB, xlsx)
Supplementary Material 4. (17.3KB, xlsx)
Supplementary Material 5. (51.6KB, xlsx)

Author contributions

Wenjie Chen: Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Conceptualization. Fujie Xie: Writing – original draft, Visualization, Validation, Software, Methodology, Investigation. Jie Lv: Writing – review & editing, Resources, Investigation. Dixi Huang: Writing – review & editing, Resources, Investigation. Ronghao Zhong: Writing – review & editing, Resources, Investigation, Data curation. Zhijia Wen: Writing – review & editing, Resources, Investigation. Jiangsen Sun: Writing – review & editing, Resources, Investigation. Shaowei Zheng: Writing – review & editing, Resources, Investigation. Weile Liu: Writing – review & editing, Resources, Investigation. Haobo Zhong: Writing – review & editing, Resources, Investigation. Shoubin Huang: Writing – review & editing, Resources, Investigation.

Funding

This work was supported by the Foundation of Guangdong Basic and Applied Basic Research Foundation (2023A1515140045 & 2023A1515140129 & 2023A1515140183 & 2023A1515140034).

Data availability

The datasets supporting this study are publicly available in the following repositories: UCSC (https://xenabrowser.net/) database provides the Pan-cancer dataset (https://xenabrowser.net/datapages/?cohort=TCGA%20TARGET%20GTEx&removeHub=https%3 A%2 F%2Fxena.treehouse.gi.ucsc.edu%3A443), GTEx dataset (https://xenabrowser.net/datapages/?cohort=GTEX&removeHub=https%3 A%2 F%2Fxena.treehouse.gi.ucsc.edu%3A443), and osteosarcoma dataset (https://xenabrowser.net/datapages/?cohort=GDC%20TARGET-OS&removeHub=https%3 A%2 F%2Fxena.treehouse.gi.ucsc.edu%3A443).Gene Expression Omnibus (GEO) datasets GSE152048 were accessed via the NCBI GEO database: https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE152048.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Wenjie Chen, Fujie Xie, and Jie Lv have contributed equally to this work and share first authorship.

Change history

11/10/2025

This article has been updated to correct affiliation 1.

Contributor Information

Haobo Zhong, Email: zhonghaobohz78@163.com.

Shoubin Huang, Email: ShoubinHuang81@163.com.

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Associated Data

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

Supplementary Materials

Supplementary Material 1. (137.2MB, docx)
Supplementary Material 2. (9.9KB, xlsx)
Supplementary Material 3. (144.8KB, xlsx)
Supplementary Material 4. (17.3KB, xlsx)
Supplementary Material 5. (51.6KB, xlsx)

Data Availability Statement

The datasets supporting this study are publicly available in the following repositories: UCSC (https://xenabrowser.net/) database provides the Pan-cancer dataset (https://xenabrowser.net/datapages/?cohort=TCGA%20TARGET%20GTEx&removeHub=https%3 A%2 F%2Fxena.treehouse.gi.ucsc.edu%3A443), GTEx dataset (https://xenabrowser.net/datapages/?cohort=GTEX&removeHub=https%3 A%2 F%2Fxena.treehouse.gi.ucsc.edu%3A443), and osteosarcoma dataset (https://xenabrowser.net/datapages/?cohort=GDC%20TARGET-OS&removeHub=https%3 A%2 F%2Fxena.treehouse.gi.ucsc.edu%3A443).Gene Expression Omnibus (GEO) datasets GSE152048 were accessed via the NCBI GEO database: https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE152048.

Articles from Discover Oncology are provided here courtesy of Springer

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