Identification and functional validation of SPON2 as a novel biomarker for the diagnosis and prognosis in hepatocellular carcinoma

Introduction

Hepatocellular carcinoma (HCC) constitutes nearly 90% primary hepatic malignancies (1) and about 6% of all human malignancies (2,3), making it the second-leading cause of cancer-related death and a major global health burden (4). Although advances in therapeutic strategies have improved overall survival (OS) and event-free survival in selected patients (5), the long-term survival remain poor, primarily because most of cases are diagnosed at advanced stages (6). Therefore, the discovery and validation of robust biomarkers enabling early diagnosis and accurate prediction of the prognosis remains an urgent clinical need (7,8).

HCC exhibits profound heterogeneity at genomic, transcriptomic, and epigenetic levels (9,10). While genomic alterations can inform therapeutic decision-making (11), their low frequency and complexity often limit their diagnostic applicability (12). Increasing evidence suggests that HCC development and malignant behavior are modulated by dysregulated molecular networks and signaling pathways (13,14), underscoring the potential value of transcriptional biomarkers for patient stratification and disease characterization.

Conventional transcriptomic analyses, however, are based on bulk tissue sequencing, which represents averaged gene expression across heterogeneous cell populations and lacks single-cell resolution. Unlike bulk methods, single-cell RNA sequencing (scRNA-seq) resolves the tumor ecosystem at single-cell resolution, revealing the molecular diversity and cellular specificity within HCC tissues (15-18).

SPON2 (spondin-2; also known as Mindin or DIL-1) is a secreted extracellular matrix (ECM) protein belonging to the F-spondin family (19). It functions as a regulator of innate immunity and serves as a unique pattern-recognition molecule within the ECM for microbial pathogens (20). In the context of cancer, elevated SPON2 expression has been associated with enhanced tumorigenicity and aggressive clinicopathological features in gastric cancer and triple-negative breast cancer, correlating with poorer patient outcomes and suggesting a role in malignant progression and metastasis (21,22). Mechanistically, SPON2 modulates the Notch signaling pathway in gastric cancer and influences the PI3K-AKT axis in breast cancer, highlighting its involvement in key oncogenic networks (21,22). Furthermore, in prostate cancer, SPON2 has been shown to promote osteogenic responses via activation of the PI3K-AKT-mTOR pathway, supporting its functional relevance in tumor microenvironment signaling (23). Although these studies highlight pro-malignant functions of SPON2 in several solid tumors, its specific role and associated biological processes in HCC remain poorly understood.

In this study, we integrated bulk transcriptomic data with scRNA-seq profiles to identify SPON2 as a consistently upregulated gene specifically within HCC tumor cells. Through functional experiments, we demonstrated that SPON2 promotes malignant phenotypes, including proliferation, migration, and invasion, primarily through activation of the WNT/β-catenin signaling pathway. Furthermore, high SPON2 expression correlated with advanced disease stage and poorer OS, supporting its utility as both a diagnostic and prognostic biomarker. Collectively, our findings establish SPON2 as a key regulator of HCC aggressiveness and provide new mechanistic insights into its pro-tumorigenic role, offering a potential target for future therapeutic strategies. We present this article in accordance with the REMARK and MDAR reporting checklists (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-1-1003/rc).

Methods

Dataset acquisition and preprocessing

Normalized RNA sequencing data, corresponding clinical features, and genomic variants of HCC and adjacent normal liver tissues from The Cancer Genome Atlas (TCGA)-liver hepatocellular carcinoma (LIHC) cohort were obtained from the UCSC Xena database (https://xenabrowser.net/). Transcriptional expression profiles from multiple Gene Expression Omnibus (GEO) datasets, including GSE124535 (24), GSE14520 (25), and GSE144269 (26), were also downloaded. For datasets containing OS information, only patients with OS time greater than zero days were included in subsequent analyses.

scRNA-seq analysis

ScRNA-seq datasets from six HCC patients [GSE146115 (27) and GSE166635 (28)] were retrieved for analysis utilizing the Seurat R package (29).

Cells of suboptimal quality were filtered out according to the following criteria: mitochondrial gene content exceeding 10%, fewer than 200 or more than 5,000 genes detected. After filtering, 16,360 cells were retained for the following analysis.

Batch effects across donors and runs were removed with RunHarmony (harmony R package) (30). After selecting the 4,000 most variable genes, principal component analysis (PCA) was performed (31), and the first 30 principal components retained for t-distributed stochastic neighbor embedding (t-SNE) visualization (32). Cells were clustered with a shared nearest neighbor (SNN) modularity optimization algorithm (33) (resolution =1.0) and annotated using established canonical markers (34).

We employed Monocle2 (35) to reconstruct the differentiation trajectory of tumor cells from six HCC patients using the top 500 variable features. Additionally, the single-sample enrichment analysis (ssSEA) algorithm was applied to calculate proliferation and invasion/migration scores based on gene signatures derived from previous studies (36).

Functional enrichment analysis

To explore the biological mechanisms underlying HCC, we performed differential expression analysis with “limma” (37). HCC-related genes were identified based on an adjusted P value <0.05 and a fold change (FC) ≥1.5. Functional annotation of these genes was conducted using the “clusterProfiler” (38) against the Gene Ontology (GO) (39) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (40) databases, with an adjusted P value <0.05. Next, gene set enrichment analysis (GSEA) was carried out (38), incorporating both the “HALLMARK” gene sets and previously published pathways (41).

To uncover the biological processes linked to SPON2 expression, HCC patients from the TCGA-LIHC cohort were stratified into SPON2-high and SPON2-low groups using the median expression of SPON2. DE analysis (adjusted P value <0.05, FC ≥1.5) identified SPON2-high-related genes. Subsequently, GSEA was performed, focusing on the WNT signaling pathways.

Identification of SPON2 as a diagnostic and prognostic biomarker

To identify HCC-specific biomarkers, DE analysis was performed between HCC tumors and adjacent normal liver tissues using samples from the TCGA-LIHC and GSE124535 cohorts. Genes with an adjusted P value <0.05 and an FC ≥1.5 were considered HCC-related genes. For scRNA-seq data from patients with HCC, the “FindAllMarkers” function was adopted to identify differentially expressed genes (DEGs) between tumor cells and non-tumor cells. In this analysis, percentage of cells in group 1 (pct.1) denotes the fraction of tumor cells expressing a given gene, while percentage of cells in group 2 (pct.2) represents its expression fraction in non-tumor cells (e.g., stromal and immune cells). Transcripts meeting an adjusted P value <0.05, FC ≥1.5, pct.1 ≥0.65, and pct.2 <0.1 were designated tumor-specific. The final HCC biomarkers were determined as the overlapping genes between the upregulated HCC-related DEGs from the TCGA-LIHC and GSE124535 cohorts and the tumor-specific genes identified at the single-cell level.

Estimation of proliferation and invasion-related scores

Proliferation and invasion-related scores (migration, invasion, and extravasation) for tumor cells and HCC samples were calculated using the GSVA R package and the gsva function, based on previously defined gene signatures (42,43).

Cell culture

HepG2, Huh7, SUN-387, LX-2, and Li-7 cells were obtained from the Chinese Academy of Sciences (Guangzhou, China) and maintained in RPMI-1640 medium supplemented with fetal bovine serum (FBS; 10%) at the temperature of 37 ℃ with 5% CO₂ according to standard culture protocols.

Cell transfection

The siRNA-1 (5'-UAUCUCGGUCACCGUGUCCUGdTdT-3') and siRNA-2 (5'-UCUACAAUCUCAUUGUCCCUGdTdT-3') targeting SPON2 and siRNA-NC (5'-ACGUGACACGUUCGGAGAAdTdT-3') were obtained from KeyGEN (Nanjing, China). Cells were seeded into 6-well plates and cultured until they reached 80–90% confluence, after which they were transfected using Lipofectamine^TM 2000 Transfection Reagent (Cat. No. 11668019, Invitrogen, Carlsbad, CA, USA).

Western blot analysis

Cell or tissue samples were lysed on ice, and an appropriate amount of RIPA lysis buffer was added for sample lysis. Following 10 minutes of centrifugation (10,000 rpm, 4 ℃), the supernatant was restored for quantification and downstream sample preparation. Following gel electrophoresis and membrane transfer, the target band was cut out and sealed with blocking solution at room temperature (RT) for 1 hour. The primary antibody was then added at a dilution of 1:500 (v/v) and incubated overnight at 4 ℃. After the membrane was washed, it was incubated with the appropriate secondary antibody [1:5,000 (v/v)] at RT for 2 hours. The membrane was washed again, and enhanced chemiluminescence solution was applied. The reaction was carried out in the dark for 5 minutes, after which fluorescence results were collected.

Clone formation assay

Cells were transfected with the indicated reagents and subsequently maintained in culture for 24–48 hours according to experimental requirements. After incubation, cells were digested with trypsin and resuspended in complete culture medium to adjust the cell concentration. A total of 500 µL of cells from each treatment group was inoculated into a 6-well plate at approximately 1×10⁴ cells per well. Next, the cells were cultured in a culture incubator for 14 days, with the culture medium replaced every 2–3 days. At the endpoint, the culture medium was removed. Cells were then fixed and stained with 0.1% crystal violet. Finally, colonies were imaged with a digital camera and counted.

Cell counting kit-8 (CCK-8) assay

Cells were seeded in 6-well plates and transfected with different transfection reagents according to the experimental requirements. After 24–48 hours, the cells were trypsinized, collected, and resuspended in complete medium to the desired density. A total of 100 µL of cells from each treatment group was inoculated into a 96-well plate at 1×10⁴ cells per well. Six parallel wells were seeded for each treatment group, and the remaining wells were filled with an equal amount of phosphate-buffered saline (PBS) buffer after adding the samples. Following 24 h incubation, the CCK-8 solution (10 µL) was dispensed into each well in the dark and cells were returned to the incubator for an additional 4 hours. The absorbance at a 450 nm was then read using an enzyme-linked immunosorbent assay (ELISA) reader.

5-ethynyl-2'-deoxyuridine (EdU) assay

Cell proliferation was evaluated with EdU. Transfected cells in the logarithmic growth phase were seeded at 4×10³–1×10⁵ cells per well in 96-well plates. EdU medium was added and cells were incubated for 2 hours. After fixation with methanol, the membrane was permeabilized using 0.5% Triton-X-100 PBS, followed by the Apollo^® Stain assay (Beyotime Biotechnology, Shanghai, China; avoiding light exposure). Cells were incubated at RT on a decolorization shaker, then stained with 4’,6‑diamidino‑2‑phenylindole (DAPI) and observed under a fluorescence microscope.

Wound healing assay

Cells were plated at 1×10⁶ per well in 6-well plates and cultured to ~90 % confluence. A linear wound was introduced with a sterile pipette tip (200 µL). Detached cells were rinsed away and serum-free medium was added. Next, cells were cultured for 48 hours. Wound images were captured at two timepoints: 0 and 48 hours. The remaining wound width was measured to determine the cell migration rate.

Transwell invasion assay

Matrigel was diluted 1:3 with serum-free Dulbecco’s Modified Eagle Medium (DMEM)/F12 and coated onto the lower surface of the Transwell upper chamber. The coated inserts were incubated overnight and exposed to ultraviolet light for 30 minutes. Cells were resuspended in serum-free medium, seeded into the upper chamber, and allowed to invade toward DMEM + 10% FBS in the lower compartment. After 24 hours, invading cells were fixed (4% paraformaldehyde, 30 minutes), stained with crystal violet, and counted under a light microscope. The invasion rate was calculated using the following formula: Invasion rate = (experimental cell count/control cell count) × 100%.

qRT-PCR and western blot for gene expression

qRT-PCR and western blot were performed to evaluate the RNA and protein levels of relevant genes. Subcellular localization of SPON2 was assessed using nuclear-cytoplasmic separation and fluorescence in situ hybridization (FISH) assays.

Statistical analysis

Statistical analysis was performed using R software (version 4.2.2). For comparisons between two groups, the Wilcoxon rank sum test was used, while the Fisher’s exact test was applied for categorical variables. OS differences were assessed using Kaplan-Meier analysis together with the log-rank test. In addition, the diagnostic value of SPON2 for HCC was evaluated using a receiver operating characteristic (ROC) curve. The Pearson’s correlation test was used to examine relationships between variables. Statistical significance was set at P<0.05.

Ethical statement

This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the ethics committee of Guangxi Hospital Division of The First Affiliated Hospital, Sun Yat-sen University-Registry (No. KY-ZXYS-2025-078-01). All participants provided written informed consent prior to participation.

Results

HCC tumors had different transcriptional patterns compared to normal samples

To investigate the transcriptional reprogramming in HCC tumors, gene expression profiles from HCC tumors and adjacent normal samples were obtained from the TCGA-LIHC and GSE124535 cohorts. t-SNE analysis revealed that HCC tumors showed distinctive transcriptional patterns compared to normal samples in both cohorts (Figure 1A and Figure S1A). Subsequently, we calculated the Euclidean distances between HCC tumors and normal samples to explore global shifts in transcriptional gene expression. The relative differences between HCC tumors and normal tissues were significantly larger than the distances observed within HCC or normal samples, indicating a substantial transcriptional divergence between these two groups (Figure 1B and Figure S1B).

Figure 1 The transcriptional patterns between HCC tumors and normal samples. (A) t-SNE visualization of average gene expression in HCC tumors and normal samples from the TCGA-LIHC cohort. Blue dots represent normal samples, while red dots represent HCC tumors. (B) Global differences in transcriptional gene expression between HCC tumors and normal tissues in the TCGA-LIHC cohort, with Euclidean expression distances calculated for tumors and normal tissues (blue), different tumor samples (red), and different normal tissue samples (green). (C) Volcano plot showing DEGs between HCC tumors and normal samples in the TCGA-LIHC cohort, with a fold change ≥1.5 and an adjusted P value <0.05 as the threshold. (D) KEGG enrichment analysis of DEGs upregulated in HCC tumors. (E) GSEA correlating “HALLMARK_DNA_REPAIR”, “HALLMARK_G2M_CHECKPOINT”, and “HALLMARK_E2F_TARGETS” with HCC tumors. (F) HALLMARK analysis of upregulated genes in HCC tumors from the GSE124535 cohort. DEGs, differentially expressed genes; ECM, extracellular matrix; GSEA, gene set enrichment analysis; HCC, hepatocellular carcinoma; KEGG, Kyoto Encyclopedia of Genes and Genomes; LIHC, liver hepatocellular carcinoma; NES, normalized enrichment score; SPON2, spondin-2; TCGA, The Cancer Genome Atlas; t-SNE, t-distributed stochastic neighbor embedding.

To further characterize the biological functions upregulated in HCC tumors, we identified highly expressed genes (Figure 1C) and conducted functional enrichment analysis (Figure 1D). Results showed that compared to normal tissues, HCC tumors significantly activated pathways related to the p53 signaling pathway, ECM-receptor interaction, and several pathways involved in the cell cycle (Figure 1D,1E). These observations were also corroborated in an independent dataset that paired HCC tumors with adjacent normal samples (Figure 1F and Figure S1C-S1E).

Identification of SPON2 as an HCC-specific biomarker

To uncover specific biological signatures of HCC tumors, scRNA-seq datasets from six HCC patients were used (Figure 2A). After quality control, 16,360 single cells were retained and unsupervised clustered identified 23 distinct clusters (Figure 2B). Based on the expression profiles of known cell‑type‑specific markers, the cells were classified into six categories: tumor cells, B cells, T cells, macrophages, fibroblasts, and endothelial cells. The expression patterns of representative marker genes for each cell type are shown (Figure 2C2E). To validate this annotation, we identified genes expressed uniquely in specific cell types (Figure 2F). The tSNE plots with marker gene expression are presented (Figure S2). Subsequently, we compared the upregulated genes in HCC tumors from bulk transcriptomic data with the tumor-specific genes identified in the scRNA-seq dataset. The gene SPON2 emerged as the overlapping gene of interest (Figure 3A). At the single-cell level, tumor cells exhibited significantly higher expression levels and larger fractions of SPON2 expression compared to non-tumor cells (Figure 3B-3D). In both the TCGA-LIHC and GSE124535 cohorts, SPON2 expression showed a remarkable increase in HCC tumors relative to adjacent normal tissues [area under the curve (AUC): 0.788 vs. 0.793], confirming its robust diagnostic accuracy for distinguishing HCC tumors from normal samples (Figure 3E,3F). In addition, SPON2 was also significantly upregulated in HCC tumors in two independent datasets (GSE14520, AUC =0.823; GSE144269, AUC =0.661) (Figure 3G,3H). More importantly, SPON2 expression demonstrated greater accuracy than alpha-fetoprotein (AFP) in distinguishing HCC patients from normal controls across all four cohorts (Figure S3A-S3D). Furthermore, the combined SPON2 + AFP model produced a significantly higher AUC compared with AFP alone in these cohorts, indicating that SPON2 provides substantial complementary diagnostic value to AFP (Figure S3A-S3D). Taken together, these results from both bulk transcriptomic and scRNA-seq datasets highlight SPON2 as a promising HCC-specific biomarker for diagnostic and potentially therapeutic applications.

Figure 2 Unsupervised clustering and cell annotation of scRNA-seq data from six HCC patients. (A) t-SNE visualization of 16,360 single cells that passed quality control, colored by six HCC patients. (B) The unsupervised clustering of the 16,360 cells. (C) t-SNE visualization of cell types annotated by classical gene markers. (D) Bar plot showing the number of cells for each cell type. (E) Bubble plot displaying the expression levels of known markers for specific cell types. (F) Heatmap showing gene expression levels of the top 10 cell-type-specific genes. HCC, hepatocellular carcinoma; scRNA-seq, single-cell RNA sequencing; t-SNE, t-distributed stochastic neighbor embedding.

Figure 3 Identification of SPON2 as the HCC-specific biomarker. (A) Venn diagram showing the intersections between up-regulated genes in HCC tumors and tumor cell-specific genes from the scRNA-seq dataset. Genes with FC ≥1.5 and adjusted P value <0.05 were identified as HCC tumor-upregulated genes. Genes with FC ≥1.5, adjusted P value <0.05, pct.1 ≥0.65, and pct.2 <0.1 were identified as tumor cell-specific genes. (B) SPON2 expression overlaid on the t-SNE representation. (C) Stacked bar chart showing the fraction of tumor and non-tumor cells that either express or do not express SPON2. (D) Comparison of SPON2 expression between tumor and non-tumor cells. (E-H) The expression difference and ROC curves for SPON2 in distinguishing normal and tumor samples in the TCGA-LIHC (E), GSE124535 (F), GSE14520 (G), and GSE144269 (H) cohorts. Data are presented as sensitivity versus 1−specificity. The AUC values are reported with 95% CIs. The Wilcoxon rank-sum test was used to calculate the difference between two groups. AUC, area under the curve; CI, confidence interval; FC, fold change; HCC, hepatocellular carcinoma; LIHC, liver hepatocellular carcinoma; pct.1, percentage of cells in group 1; pct.2, percentage of cells in group 2; ROC, receiver operating characteristic; scRNA-seq, single-cell RNA sequencing; SPON2, spondin-2; TCGA, The Cancer Genome Atlas; t-SNE, t-distributed stochastic neighbor embedding.

SPON2 was associated with poor clinical outcomes and aggressive tumor characteristics

Given the reported oncogenic role of SPON2 in multiple solid cancers (22,44), we next examined its clinical relevance in HCC. As shown in Figure 4A-4C, HCC patients with metastasis and higher pathological grades showed significantly higher SPON2 expression levels. Moreover, high SPON2 expression was strongly associated with markedly shorter OS than low expression (log-rank P=0.02; Figure 4D). In an independent HCC cohort, the SPON2-high group similarly exhibited elevated metastasis scores, shorter OS, and increased relapse rates (Figure 4E-4G), consistent with observations from the TCGA cohort. To determine whether SPON2 serves as an independent prognostic factor, we performed multivariable Cox proportional hazards regression analysis in the TCGA-LIHC cohort, adjusting for key clinicopathological variables including clinical stage, histological grade, gender, and age. The results confirmed that high SPON2 expression remained independently associated with poorer OS [P=0.02; hazard ratio (HR) =1.57; 95% confidence interval (CI): 1.08–2.27; Table 1].

Figure 4 SPON2 was associated with poor clinical outcomes in HCC patients. (A) Correlations between SPON2 expression and clinicopathological features in the TCGA-LIHC cohort. (B,C) Boxplots showing SPON2 expression across the subtypes of each clinicopathological feature. (D) Kaplan-Meier analysis of OS in TCGA HCC patients. Patients were categorized into two groups based on the best cutoff of SPON2 expression. (E) Comparison of metastasis signature scores between SPON2-high and SPON2-low patients in the GSE14520 cohort. (F,G) Kaplan-Meier analysis of OS (F) and RFS (G) in HCC patients from the GSE14520 cohort. Patients were categorized into two groups based on the best cutoff of SPON2 expression. The Wilcoxon rank-sum test was performed to calculate the difference between two groups. HCC, hepatocellular carcinoma; LIHC, liver hepatocellular carcinoma; M, metastasis; N, node; OS, overall survival; RFS, relapse-free survival; SPON2, spondin-2; T, tumor; TCGA, The Cancer Genome Atlas.

We further compared the genomic variant landscapes of TCGA-LIHC tumors stratified into SPON2-high versus SPON2-low expression subsets. Notably, canonical driver mutations associated with hepatocarcinogenesis, including CTNNB1 (45) and TP53 (46), were obviously enriched in patients with high SPON2 expression (Figure 5A,5B). Functional enrichment analyses revealed that the SPON2-high group exhibited activation of oncogenic pathways linked to proliferation and invasiveness, such as the mTORC1 signaling (47) and glycolysis pathways (48) (Figure 5C).

Figure 5 SPON2 was associated with the malignancy of HCC patients. (A,B) Mutational profiles of differentially mutated genes between the SPON2-high and SPON2-low groups in the TCGA-LIHC cohort. (C) Bar plot showing the HALLMARK pathways enriched in the SPON2-high group. (D,E) Comparison of proliferation and invasion/migration scores between SPON2⁺ and SPON2⁻ tumor cells at the single-cell level. The Wilcoxon rank-sum test was performed to calculate the difference between two groups. *, P value ≤0.05; **, P value ≤0.01. CI, confidence interval; HCC, hepatocellular carcinoma; Inf, infinity; LIHC, liver hepatocellular carcinoma; NA, not available; OR, odds ratio; SPON2, spondin-2; TCGA, The Cancer Genome Atlas; TGF, transforming growth factor; TMB, tumor mutational burden; UV, ultraviolet.

We further explored the association between SPON2 expression and functional states within the tumor cell population by performing pseudotime analysis on tumor cells from the HCC scRNA-seq dataset (Figure S4A,S4B). The analysis placed SPON2⁺ cells primarily at the beginning of the trajectory (low pseudotime) (Figure S4C). Consistent with this spatial distribution along a trajectory branch linked to aggressive phenotypes, SPON2⁺ cells demonstrated significantly elevated scores for both proliferation and invasion/migration (both P<0.001, Figure 5D,5E and Figure S4D,S4E). Together, these findings indicate that overexpression of SPON2 may contribute to poor clinical outcomes in HCC by promoting tumor aggressiveness and enhancing malignant cellular phenotypes.

SPON2 enhanced proliferation, migration, and invasion in HCC cells

SPON2 deficiency suppressed malignant phenotypes in HCC cells. To investigate the functional role of SPON2, we knocked down its expression in HepG2 and Li-7 cells using two independent siRNAs (si-SPON2-1 and si-SPON2-2). The knockdown efficiency was confirmed at both the mRNA and protein levels by qRT-PCR and western blot analysis, respectively. SPON2PON2 expression in HCC cell lines was assessed by western blot (Figure 6A,6B) and qRT‑PCR (Figure 6C). Functionally, SPON2 knockdown markedly inhibited cell proliferation, as assessed by CCK-8 assays (Figure 6D,6E). qRT-PCR confirmed a significant reduction in SPON2 mRNA in the siRNA-treated groups compared to the negative control (si-NC) group (both P<0.01; Figure 6F-6H). Additionally, SPON2 knockdown significantly impaired colony-forming ability in both cell lines (Figure 6I-6K). Furthermore, transwell and wound healing assays demonstrated that silencing SPON2 effectively suppressed the migratory (Figure 6L,6M) and invasive (Figure 6O6Q) capacities of HCC cells. In summary, these results indicate that SPON2 knockdown attenuates the proliferative, migratory, and invasive potential of HCC cells in vitro, supporting an oncogenic role for SPON2 in HCC progression.

Figure 6 SPON2 enhanced the proliferation, migration, and invasion of hepatoma carcinoma cells. (C) SPON2 expression in a hepatic stellate cell line (control) and hepatoma carcinoma cell lines was assessed by western blotting (A,B) and qRT-PCR (C). (D-Q) Proliferation (D-H), migration (L-N), and invasion (O-Q) were assessed in hepatoma carcinoma cell lines following SPON2 knockdown or overexpression, along with their respective controls. Quantitative analysis is presented as mean ± SD. Significance was calculated using the Student’s t-test (*, P<0.05; **, P<0.01; n=3 per group). EdU staining (red) and DAPI (blue). Scale bars: (F,I,L) 100 µm. DAPI, 4',6‑diamidino‑2‑phenylindole; EdU, 5-ethynyl-2'-deoxyuridine; NC, negative control; OD, optical density; OE, overexpression; qRT-PCR, quantitative reverse transcription polymerase chain reaction; SD, standard deviation; SPON2, spondin-2.

SPON2 contributed to HCC malignancy by activating the WNT signaling pathways

The WNT signaling pathway has been previously implicated in promoting the migration and invasion of HCC tumors (49,50). We found that SPON2 expression significantly linked to the activation of WNT signaling pathway (Figure 7A-7C, Figure S5A,S5B). SPON2⁺ tumor cells showed markedly higher levels of WNT signaling activation compared to SPON2^- tumor cells (Figure 7D). Further analysis revealed a positive correlation between WNT signaling activation and the invasion/migration scores of tumor cells and HCC patients (Figure 7E-7G), underscoring the potential role of SPON2 in driving tumor progression through WNT pathway activation.

Figure 7 WNT signaling pathway is enriched in SPON2-high groups and associated with invasion/migration. (A-C) The GSEA of the correlation between the WNT signaling pathway and SPON2 expression. (D) Comparison of WNT signaling pathway scores between SPON2+ and SPON2− tumor cells. (E) Correlation between WNT signaling pathway scores and invasion/migration scores in tumor cells from the scRNA-seq data. (F,G) Correlation between WNT signaling pathway scores and invasion/migration scores in the TCGA-LIHC (F) and GSE14520 (G) cohorts. The Wilcoxon rank-sum test was performed to calculate the difference between two groups. GSEA, gene set enrichment analysis; LIHC, liver hepatocellular carcinoma; NES, normalized enrichment score; scRNA-seq, single-cell RNA sequencing; SPON2, spondin-2; TCGA, The Cancer Genome Atlas.

To validate these findings, we performed immunofluorescence co-localization assays in HepG2 and Li-7 HCC cells. Compared with the control group, knockdown of SPON2 in tumor cells resulted in increased E-cadherin expression and decreased vimentin expression, while SPON2 overexpression led to reduced E-cadherin and elevated vimentin levels, supporting the conclusion that SPON2 promotes a shift toward a more mesenchymal state (Figure 8A). Moreover, qRT-PCR analysis confirmed that E-cadherin and vimentin mRNA levels were significantly increased in HepG2 and Li-7 cells (Figure 8F,8G). Western blot analysis confirmed the increased levels in E-cadherin and vimentin in both HepG2 and Li-7 cells (Figure 8B-8E). The above findings suggest that SPON2 was markedly upregulated within HCC. It enhances tumor aggressiveness by activating WNT signaling pathways, which in turn promoting invasion and migration.

Figure 8 SPON2 overexpression upregulates the WNT pathway in hepatoma carcinoma cells. (A) Immunofluorescence analysis of E-cadherin and vimentin expression in hepatoma carcinoma cell lines after SPON2 knockdown or overexpression, along with their corresponding controls. Error bars represent mean ± SD. (B-E) Western blot analysis of E-cadherin and vimentin expression in hepatoma carcinoma cell lines following SPON2 knockdown or overexpression, together with respective controls. (F-G) qRT-PCR analysis of E-cadherin and vimentin expression in hepatoma carcinoma cell lines under SPON2 knockdown or overexpression conditions and their controls. Quantitative data are presented as mean ± SD. Statistical significance was determined by Student’s t-test (**, P<0.01; n=3 per group). DAPI, 4',6‑diamidino‑2‑phenylindole; NC, negative control; OE, overexpression; qRT-PCR, quantitative reverse transcription polymerase chain reaction; SD, standard deviation; SPON2, spondin-2.

To further investigate whether SPON2 enhanced tumor cell malignancy through activation of the WNT pathway, we performed additional functional experiments. The results demonstrated that, compared with the control group, SPON2 knockdown significantly suppressed tumor cell proliferation, migration, and invasion (Figure 9A-9H). Notably, co-treatment with a WNT agonist markedly reversed the inhibitory effects induced by SPON2 knockdown, effectively restoring the malignant phenotypes. These findings provided direct functional evidence that SPON2 promoted HCC aggressiveness via activation of the WNT signaling pathway.

Figure 9 SPON2 enhanced the proliferation, migration, and invasion of hepatoma carcinoma cells via the activation of WNT pathway. (A-H) Proliferation (A-D), migration (G,H), and invasion (E,F) were assessed in SMMC-7721 cell lines under three conditions: control group (shNC), SPON2 knockdown group (sh-SPON2), and SPON2 knockdown combined with WNT agonist treatment group (sh-SPON2 + WNT agonist). Quantitative analysis is presented as mean ± SD. Significance was calculated using the Student’s t-test (**, P<0.01; n=3 per group). NC, negative control; OD, optical density; SD, standard deviation; SPON2, spondin-2.

SPON2 activated the WNT signaling pathway through ECM remodeling

To investigate whether SPON2 activates WNT signaling via ECM remodeling, we evaluated the expression of key WNT pathway components under conditions that modulated ECM and integrin signaling. HCC cells were subjected to the following treatments including control (sh-NC), SPON2 knockdown (sh-SPON2), SPON2 knockdown with Matrigel supplementation (sh-SPON2 + Matrigel), SPON2 overexpression (OE-SPON2), and SPON2 overexpression combined with an integrin inhibitor (OE-SPON2 + integrin-IN). qRT-PCR and western blot analysis revealed that, compared with SPON2 knockdown alone, Matrigel supplementation in SPON2-knockdown cells significantly restored the expression of β-catenin, phosphorylated GSK3β (p-GSK3β), cyclin D1, and c-Myc (Figure 10A-10E). Conversely, compared with SPON2 overexpression alone, treatment with an integrin inhibitor markedly suppressed the expression of these proteins in SPON2-overexpressing cells (Figure 10A-10E). These findings suggested that SPON2 activated the canonical WNT signaling pathway through ECM-mediated mechanisms.

Figure 10 SPON2 activated the WNT signaling pathway through ECM remodeling. (A-F) qRT-PCR was performed to detect the relative mRNA expression of β-catenin (A,B), cyclin D1 (C,D), c-Myc (E,F). (G-K) Western blot analysis was conducted to detect the protein expression levels of β-catenin, p-GSK3β (Ser9), cyclin D1, and c-Myc. (L) Immunofluorescence staining of E‑cadherin (green) and vimentin (red) in HepG2 cells after different treatments. Scale bar: 50 µm. (M) Wound healing assay showing cell migration at 0 and 48 h under indicated conditions. Scale bar: 200 µm. (N) Transwell invasion assay (crystal violet staining) of HCC cells with SPON2 modulation. Scale bar: 100 µm. (O) Quantification of relative invasion rate from three independent experiments. All data are presented as mean ± SD. Significance was calculated using the Student’s t-test [&, P<0.05; &&, P<0.01 vs. sh‑NC group (applied to sh‑SPON2 + matrigel and OE-SPON2 + integrin-IN groups); **, P<0.01 vs. sh‑NC group; n=3 per group]. ECM, extracellular matrix; GAPDH, glyceraldehyde‑3‑phosphate dehydrogenase; NC, negative control; OE, overexpression; qRT-PCR, quantitative reverse transcription polymerase chain reaction; SD, standard deviation; SPON2, spondin-2.

Discussion

Despite extensive research aimed at understanding HCC and developing new therapeutic strategies (51,52). HCC remains a significant global burden. However, its clinical management is hindered by a lack of robust biomarkers to facilitate early detection, evaluate patient prognosis, and predict tumor aggressiveness. Thus, there is an urgent need for comprehensive studies aimed at identifying highly accurate biomarkers for diagnosis, prognosis, and the prediction of metastasis.

With the advancement of sequencing technologies, high-resolution profiling has made it increasingly feasible to identify biomarkers that are upregulated in tumor cells, allowing for accurate differentiation between HCC patients and healthy controls. In this study, by integrating transcriptomic data from HCC patient samples and normal controls, along with scRNA-seq data from six HCC patients, we identified SPON2 as a highly specific biomarker for HCC. Our findings demonstrate that SPON2 can accurately and robustly distinguish HCC patients from normal controls, while also reflecting key aspects of prognosis and the metastatic potential of HCC.

The dysregulation of SPON2 has been demonstrated in various solid cancers, including prostate cancer (53) and gastric cancer (22,54). Elevated SPON2 expression, evident at both the mRNA and protein levels, has been linked to worse clinical outcomes in these cancers (55,56). Consistent with these findings, our study reveals that SPON2 is highly expressed in HCC and demonstrates excellent accuracy in distinguishing HCC patients from healthy controls across four independent cohorts. HCC patients displaying elevated SPON2 expression experienced shorter OS and exhibited a higher metastatic potential, demonstrating the role of SPON2 in HCC carcinogenesis.

Single-cell profiling revealed that SPON2⁺ tumor cells were markedly more proliferative and aggressive compared to SPON2^- tumor cells, suggesting that SPON2 overexpression contributes to the malignancy of HCC cells. To further validate this finding, we conducted overexpression and knockdown experiments in HCC cell lines, confirming that SPON2 overexpression enhanced cell proliferation, migration, and invasion, all of which accelerated tumor development and progression (57). Moreover, our findings revealed that SPON2 expression correlated with WNT signaling pathway activation, a driver of tumor invasion and metastasis. SPON2 knockdown significantly inhibited the expression of key WNT-related proteins, indicating that SPON2 facilitates HCC cell migration, invasion, and metastasis by activating the WNT signaling pathway.

While this study provides compelling in vitro and transcriptomic evidence supporting SPON2 as a functional biomarker in HCC, several limitations should be acknowledged. First, the scRNA-seq analysis, though offering high-resolution insights, was derived from a cohort of six patients. Although integrated with large bulk RNA-seq datasets for validation, future studies with larger, prospectively collected single-cell cohorts are needed to confirm the heterogeneity patterns described here. Second, our integrated analysis relied on publicly available datasets with varying etiologies, disease stages, and treatment histories, and future prospective studies with uniformly annotated clinical data are required to fully validate the clinical associations of SPON2. Third, all functional experiments were performed in vitro; the lack of in vivo validation using animal models limits direct conclusions regarding the role of SPON2 in tumor growth and metastasis within a physiological tumor microenvironment. Finally, although we identified the WNT pathway as a key downstream mechanism, the precise upstream regulators of SPON2 overexpression in HCC and its interactions with specific cell-surface receptors remain to be elucidated and warrant further investigation.

In conclusion, our findings establish SPON2 as a promising multi-faceted biomarker in HCC and reveal its novel function in promoting aggressiveness via WNT pathway activation. These insights not only enhance the molecular understanding of HCC progression but also position SPON2 as a potential candidate for future therapeutic targeting.

Conclusions

In summary, integrated analysis of bulk transcriptomic and scRNA-seq data identified SPON2 as a novel biomarker in HCC. SPON2 expression was significantly upregulated in HCC tissues and was associated with shorter OS. In vitro functional experiments confirmed its elevated expression in HCC cell lines and demonstrated that knockdown of SPON2 significantly inhibited the proliferation, migration, and invasion capacities of tumor cells. Mechanistically, SPON2 was found to promote these malignant phenotypes through activation of the WNT signaling pathway. Collectively, these results suggest that SPON2 is a predictor of adverse clinical outcomes and a key contributor to tumor cell malignancy in HCC.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the REMARK and MDAR reporting checklists. Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-1-1003/rc

Data Sharing Statement: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-1-1003/dss

Peer Review File: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-1-1003/prf

Funding: This work was partially supported by the National Natural Science Foundation of China Project (No. 62472108) and the National Natural Science Foundation of Guangxi (Nos. Guike AB25069095 and 2021GXNSFBA075040).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-1-1003/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the ethics committee of Guangxi Hospital Division of The First Affiliated Hospital, Sun Yat-sen University-Registry (No. KY-ZXYS-2025-078-01). All participants provided written informed consent prior to participation.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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