STRIP2 promotes hepatocellular carcinoma progression and immune evasion: a potential prognostic biomarker and therapeutic target

Introduction

Liver cancer is one of the most common and lethal malignancies worldwide (1). It ranks sixth in global incidence, with approximately 900,000 new cases diagnosed annually (2). Hepatocellular carcinoma (HCC) constitutes over 90% of primary liver cancers. HCC is highly heterogeneous and aggressive, leading to poor treatment outcomes and a 5-year survival rate of only around 21% (3,4). Therefore, elucidating the mechanisms of HCC development, identifying sensitive prognostic biomarkers, and exploring potential therapeutic targets are crucial for improving patient survival.

Among the various biological processes (BP) implicated in tumor progression, cytoskeletal remodeling has emerged as a critical driver of cancer cell invasion, metastasis, and treatment resistance (5). The cytoskeleton is a dynamic intracellular network that not only maintains cell morphology, stability, and movement, but also plays a pivotal role in signal transduction and cell-microenvironment interactions (6,7). Dysregulation of cytoskeletal-associated proteins has been increasingly recognized in HCC pathogenesis, offering potential prognostic and therapeutic value (8). STRIP2 is a key regulator of cytoskeletal stability, contributing to the maintenance of cell morphology and cytoskeletal dynamics (9). Increasing evidence suggests that STRIP2 plays an oncogenic role, with recurrent mutations and dysregulated expression identified across a variety of malignancies (10). Recent studies have demonstrated that STRIP2 is aberrantly overexpressed in breast, lung, and gastric cancers, where its elevated expression correlates with poor clinical outcomes and more aggressive tumor behavior (11-13). These findings highlight the potential of STRIP2 as a prognostic biomarker and therapeutic target in solid tumors. However, its expression patterns and biological functions in HCC remain largely unexplored.

This study employs bioinformatics analyses to assess STRIP2 expression in HCC and its correlation with clinical prognosis. We further validate STRIP2’s prognostic value using immunohistochemistry (IHC) in a clinical patient cohort. We also develop a nomogram incorporating STRIP2 expression for precise prognostic stratification of HCC patients. Finally, we conduct STRIP2 silencing experiments in HCC cell lines to examine its role in tumor proliferation, migration, and invasion. These findings will offer new insights into personalized treatment and prognostic management of HCC. They will also help establish a theoretical foundation for STRIP2 as a potential therapeutic target. We present this article in accordance with the TRIPOD reporting checklist (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-250/rc).

Methods

Data acquisition and differential expression analysis

RNA sequencing (RNA-seq) data and corresponding clinical information for HCC and adjacent normal tissues were obtained from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/). The RNA-seq data were normalized to transcripts per million (TPM) for pan-cancer differential expression analysis. mRNA expression data from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/) were also included. The inclusion criteria for dataset selection were as follows: (I) the dataset contained both HCC tumor and adjacent non-tumor tissue samples; (II) the total number of samples exceeded 100; and (III) STRIP2 expression data were available. Based on these criteria, the following datasets were ultimately included: GSE22058, GSE25097, GSE36376, GSE46444, GSE54236, GSE63898, GSE64041, and GSE76427. To further validate STRIP2 differential expression, we included the ICGC-LIRI-JP dataset from the International Cancer Genome Consortium (ICGC).

Differentially expressed genes (DEGs) analysis

HCC patients were categorized into high and low STRIP2 expression groups based on the median STRIP2 expression level. DEGs were identified using the DESeq2 package, with a significance threshold of p.adjust <0.05 and |log₂fold change (FC)| >2. Results were visualized with a volcano plot highlighting the top five upregulated and downregulated DEGs.

Enrichment analysis

Functional enrichment analysis was conducted using the clusterProfiler package to assess Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment for the identified DEGs. Gene Set Enrichment Analysis (GSEA) was performed with the c2.cp.all.v2022.1.Hs dataset. Positively and negatively enriched pathways were ranked by adjusted p-value, and the top five in each category were visualized.

STRIP2 methylation and immune infiltration analysis

STRIP2 promoter methylation levels in HCC were assessed using the UALCAN platform, and the MethSurv tool was used to examine the correlation between STRIP2 methylation and patient prognosis. Immune infiltration analysis was performed using CIBERSORTx (https://cibersortx.stanford.edu/) to quantify 22 immune cell subtypes in HCC (14). Differences between high and low STRIP2 expression groups were evaluated by the Wilcoxon test, and Spearman correlation analysis was used to assess associations between STRIP2 expression and immune cells infiltration. Visualizations were generated with the ggClusterNet package.

To further explore STRIP2’s interaction with the immune microenvironment, immune-related genes were identified using the TISIDB database (http://cis.hku.hk/TISIDB/) (15). Co-expression analysis of STRIP2 with five categories of immune genes (chemokines, immune receptors, major histocompatibility complexs, immunoinhibitors, and immunostimulators) was performed, and results were presented in a heatmap.

Protein-protein interaction (PPI) network analysis

The GeneMANIA database (http://genemania.org) was used to predict potential STRIP2-interacting proteins (16). We then explored STRIP2’s functional network in HCC by generating correlation heatmaps for STRIP2 and its interacting proteins.

Drug sensitivity analysis and immunotherapy response evaluation

The half-maximal inhibitory concentration (IC50) values for HCC therapeutic drugs were obtained from the Genomics of Drug Sensitivity in Cancer (GDSC) database. The oncoPredict package was used to evaluate differences in drug sensitivity between high and low STRIP2 expression groups. The pRRophetic package was employed to estimate IC50 values for HCC treatments. Immunotherapy response was assessed using the Tumor Immune Dysfunction and Exclusion (TIDE) platform (http://tide.dfci.harvard.edu/) to determine the relationship between STRIP2 expression and patient response to immune checkpoint inhibitor (ICI) therapy.

Screening of potential therapeutic compounds

The Connectivity Map (cMap) database was utilized to identify potential therapeutic compounds for HCC. Based on the DEGs between high and low STRIP2 expression groups, the top 30 upregulated and top 30 downregulated genes (ranked by |log₂FC|) were identified. The L1000 platform was then applied to these gene sets to predict candidate compounds, and the three compounds with the lowest enrichment scores were selected as potential STRIP2-related therapeutic candidates.

Clinical sample collection

A retrospective analysis was conducted on HCC patients who underwent curative resection at The Fourth Hospital of Hebei Medical University between January 2017 and December 2018. Paraffin-embedded tumor specimens and corresponding clinicopathological data were collected, with survival follow-up completed by November 20, 2023. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of The Fourth Hospital of Hebei Medical University (No. 2023KS181), and all patients provided written informed consent.

Cell culture

Human HCC cell lines MHCC-97H and Mahlavu were obtained from the Chinese Academy of Sciences (Shanghai). Cells were maintained in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and penicillin-streptomycin in a humidified incubator at 37 °C with 5% CO₂.

Cell transfection

HCC cells were plated and grown for 24 hours before transfection. Short hairpin RNA (shRNA) sequences targeting STRIP2 were designed and cloned into lentiviral plasmids (Hanbio, Shanghai, China) to construct STRIP2-silencing vectors. shSTRIP2 1: 5'-GGAACAAGTTCATCGGATT-3'; shSTRIP2 2: 5'-GCCGGAGCTTACTACTGAA-3'; negative control (NC): 5'-TTCTCCGAACGTGTCACGT-3'. shRNA-STRIP2 and NC was transfected into cells using Lipofectamine 3000 (Invitrogen, USA). After 48 hours, cells were treated with puromycin to select for stably transfected cell lines. The efficiency of STRIP2 knockdown was verified by Western blotting.

Western blot

Total protein was extracted from cells, separated by SDS-PAGE, and transferred onto a PVDF membrane. The membrane was incubated overnight at 4 °C with a primary antibody against STRIP2 (Invitrogen, PA5-25058, 1:1,000), followed by incubation with a secondary antibody at room temperature for 1 hour. Protein bands were detected using enhanced chemiluminescence and quantified by the grayscale intensity ratio of STRIP2 to an internal reference protein.

IHC staining and scoring

Formalin-fixed, paraffin-embedded HCC tumor samples were dewaxed, rehydrated, subjected to antigen retrieval, and blocked to quench endogenous peroxidase activity. Sections were incubated overnight at 4 °C with an anti-STRIP2 primary antibody (Invitrogen PA5-25058, 1:200), followed by incubation with a secondary antibody at room temperature. Diaminobenzidine was used as the chromogen for color development. After counterstaining, dehydration, and clearing, slides were sealed and examined under a light microscope.

IHC scoring was performed independently by two pathologists, evaluating the percentage of positive cells and staining intensity. The positive cell proportion was scored as 0 (0%), 1 (1–25%), 2 (26–50%), 3 (51–75%), or 4 (76–100%). Staining intensity was scored as 0 (no staining), 1 (light yellow), 2 (brownish-yellow), or 3 (dark brown). The final IHC score was calculated by multiplying the positive cell proportion score by the staining intensity score. STRIP2 expression was defined as high when the IHC score ≥8.

Survival analysis and Cox regression analysis

Kaplan-Meier survival analysis was performed to compare outcomes between high and low STRIP2 expression groups, using the log-rank test for significance. Survival curves were plotted using the survminer and survival packages. Univariate and multivariate Cox regression analyses were conducted to evaluate the prognostic impact of STRIP2 expression and other clinical variables. Variables with P<0.1 in univariate analysis were included in the multivariate model. Logistic regression analysis was also performed to assess the association between STRIP2 expression and clinicopathological parameters. Statistical significance was set at P<0.05.

Least absolute shrinkage and selection operator (LASSO) regression

LASSO regression was performed using the glmnet package. A 10-fold cross validation was used to determine the optimal λ value for feature selection. This optimal λ was then used to construct a STRIP2-associated LASSO Cox model.

Construction and validation of prognostic nomogram

A prognostic nomogram was developed using the rms package to predict 1-year, 3-year, and 5-year disease-free survival (DFS) and overall survival (OS) in HCC patients. Calibration curves were plotted to assess the agreement between predicted and observed outcomes, while time-dependent receiver operating characteristic (ROC) curves and the corresponding area under the curve (AUC) values were used to evaluate the model’s predictive performance.

Immunofluorescence

HCC cells were fixed with 4% paraformaldehyde, permeabilized with 0.5% Triton X-100, and blocked with 5% bovine serum albumin. Cells were then incubated overnight at 4 °C with an anti-STRIP2 primary antibody (Invitrogen PA5-25058, 1:50), followed by a fluorophore-conjugated secondary antibody at room temperature. After washing with phosphate-buffered saline (PBS), nuclei were counterstained with 4',6-diamidino-2-phenylindole (DAPI). Fluorescence microscopy was performed to examine the subcellular localization of STRIP2.

Cell Counting Kit-8 (CCK-8) assay

HCC cells (1,000 cells per well) were seeded into 96-well plates, and CCK-8 reagent (Dojindo, Japan) was added to each well. After 2 hours of incubation at 37 °C, the absorbance was measured at 450 nm to determine cell viability. All experiments were performed in triplicate.

Colony formation assay

HCC cells (800 cells per well) were seeded into six-well plates and cultured for 14 days. Cell colonies were fixed with 4% paraformaldehyde, stained with crystal violet, and counted under a microscope.

Wound healing assay

HCC cells were grown to confluence in six-well plates. A sterile pipette tip was used to create a wound in the cell monolayer, and detached cells were removed by washing with PBS. Cell migration into the wound area was observed at 0 and 48 hours. The wound area was measured using ImageJ software, and the wound closure rate was calculated to quantify cell migration.

Transwell assay

Cell migration and invasion were assessed using 24-well Transwell chambers (Corning, NY, USA). For invasion assays, the upper chambers were pre-coated with Matrigel. HCC cells suspended in serum-free medium were added to the upper chamber, while medium containing 10% FBS was placed in the lower chamber as a chemoattractant. After 24 hours of incubation, non-migrated cells on the upper membrane surface were removed with a cotton swab. Cells that migrated or invaded to the lower surface of the membrane were fixed, stained with crystal violet, and counted under a light microscope.

Statistical analysis

All data were analyzed using R software (version 4.1.3) and GraphPad Prism (version 9.0). Results are presented as the mean ± standard deviation (SD) of three independent experiments. A Student’s t-test was used to compare two groups, and one-way analysis of variance (ANOVA) was used for multiple group comparisons. Statistical significance was defined as P<0.05 (*), P<0.01 (**), and P<0.001 (***).

Results

STRIP2 expression and clinical relevance in public databases

Analysis of the TCGA database revealed that STRIP2 expression was significantly upregulated in HCC tissues (Figure 1A,1B). This upregulation was consistently observed across GEO and ICGC datasets (Figure 1C). Paired-sample analysis further confirmed higher STRIP2 expression in HCC tumors compared to adjacent non-tumor tissues (Figure 1D). Clinical correlation analysis indicated that high STRIP2 expression was significantly associated with advanced clinical stage, residual tumor status, and worse T stage in HCC (P<0.05 for each; Figure 2A-2D).

Figure 1 STRIP2 expression across various tumor types and HCC. (A) Pan-cancer analysis of STRIP2 expression utilizing TCGA database. (B) Comparison of STRIP2 expression between HCC tissues and unpaired normal tissues in the TCGA database. (C) Analysis of STRIP2 expression in HCC and unpaired normal tissues across public datasets. (D) Comparison of STRIP2 expression between HCC tissues and paired normal tissues in the TCGA database. *, P<0.05; **, P<0.01; ***, P<0.001. HCC, hepatocellular carcinoma; TCGA, The Cancer Genome Atlas; TPM, transcripts per million.

Figure 2 Association between STRIP2 expression and clinicopathological features in TCGA. (A) Tumor status. (B) Histologic grade. (C) Pathologic T stage. (D) Pathological stage. (E) Kaplan-Meier survival curves for DFS based on STRIP2 expression. (F) Time-dependent ROC curves for DFS. (G) Kaplan-Meier survival curves for OS based on STRIP2 expression. (H) Time-dependent ROC curves for OS. ns, P≥0.05; *, P<0.05. AUC, area under the curve; CI, confidence interval; DFS, disease-free survival; FPR, false positive rate; HR, hazard ratio; OS, overall survival; ROC, receiver operating characteristic; TCGA, The Cancer Genome Atlas; TPM, transcripts per million; TPR, true positive rate.

Prognostic significance of STRIP2 in HCC patients

We next evaluated the prognostic value of STRIP2 in HCC patients using TCGA-LIHC datasets. Kaplan-Meier survival analysis demonstrated that high STRIP2 expression was associated with significantly shorter DFS (Figure 2E). ROC curve analysis showed that the AUC values for predicting 1-year, 3-year, and 5-year DFS were 0.599, 0.599, and 0.701, respectively (Figure 2F). Similarly, patients with high STRIP2 expression had significantly shorter OS (Figure 2G), with AUC values of 0.620, 0.599, and 0.614 for 1-year, 3-year, and 5-year OS (Figure 2H).

Survival analysis revealed that the adverse prognostic impact of high STRIP2 expression was evident in patients with early tumor grade (G1/G2), lower T stage (T1/T2), and early clinical stage (I/II) (P<0.05, Figure S1). Univariate and multivariate Cox regression analyses confirmed that STRIP2 was an independent risk factor for DFS in HCC (Table S1). Although STRIP2 was significantly associated with OS in univariate analysis, it did not remain significant in the multivariate analysis for OS (Table S2).

Using the TCGA cohort, we constructed a nomogram that integrated STRIP2 expression and American Joint Committee on Cancer (AJCC) stage. The nomogram demonstrated strong predictive performance for both DFS and OS, as evidenced by calibration plots and ROC curves (Figure S2).

Identification of DEGs and enrichment analysis

A total of 222 DEGs were identified between the high and low expression groups, comprising 199 upregulated genes and 23 downregulated genes. The top five upregulated genes were PAGE2, TUBA3C, SPOCK3, SSX3, and SPATA31D1, while the top five downregulated genes were CDH22, REG3A, BHLHA9, TMEM252, and HAMP (Figure 3A).

GO analysis indicated that the DEGs were mainly involved in BP of cell-cell adhesion, were enriched in the catenin complex at the cellular component (CC), and affected molecular functions (MF) related to transmitter-gated ion channel activity. KEGG pathway analysis showed neuroactive ligand-receptor interaction and pancreatic secretion were significant enrichment pathways (Figure 3B; Table S3).

Figure 3 DEGs and enrichment analysis of STRIP2 in HCC. (A) Volcano plot illustrating DEGs between high and low STRIP2 expression groups. (B) Circular diagram depicting GO and KEGG terms associated with the DEGs. GSEA results comparing high (C) and low (D) STRIP2 expression groups. BP, biological processes; CC, cellular component; DEGs, differentially expressed genes; FC, fold change; GO, Gene Ontology; GSEA, Gene Set Enrichment Analysis; HCC, hepatocellular carcinoma; KEGG, Kyoto Encyclopedia of Genes and Genomes; MF, molecular functions.

GSEA revealed that high STRIP2 expression was associated with pathways related to the neuronal system, resolution of sister chromatid cohesion, mitotic prometaphase, neuroactive ligand receptor interaction, whereas low STRIP2 expression was linked to pathways involving cytoplasmic ribosomal proteins, eukaryotic translation initiation, eukaryotic translation elongation, ribosome, translation (Figure 3C,3D; the supplementary table is available online: https://cdn.amegroups.cn/static/public/jgo-2025-250-1.xlsx).

Immune infiltration and immune checkpoints analysis

Given the critical role of immune cells in tumor progression, we evaluated immune cell infiltration in the TCGA cohort using the CIBERSORT algorithm (Figure 4A). High STRIP2 expression was associated with an increase in Th2 cells and T helper cells, while the low STRIP2 expression group showed a significant increase in Th17 cells, cytotoxic T cells, eosinophils, B cells, and CD8⁺ T cells (Figure 4B-4E). Scatter plot analysis confirmed these correlations between STRIP2 expression and specific immune cell populations (Figure 4F-4I).

Figure 4 Correlation between STRIP2 expression and immune infiltration in HCC. (A) Bar chart showing the proportions of 22 immune cell types in HCC samples. (B) Bubble plot illustrating the correlation between STRIP2 expression and various immune cells. (C,D) Analysis of immune infiltration levels of different immune cells in high and low STRIP2 expression groups. (E) Correlation between STRIP2 expression and tumor immune cell infiltration in HCC. Scatter plots showing correlations between STRIP2 expression levels and (F) Th17 cells, (G) Th2 cells, (H) T helper cells, (I) cytotoxic cells. ns, P≥0.05; *, P<0.05; **, P<0.01; ***, P<0.001. HCC, hepatocellular carcinoma; TPM, transcripts per million.

STRIP2 expression also correlated strongly with multiple immune-regulatory genes (Figure S3). Notably, STRIP2 expression was positively correlated with several immune checkpoint molecules, including CD274 (PD-L1), PDCD1 (PD-1), TIGIT, and LAG3. This suggests that STRIP2 may contribute to an immunosuppressive microenvironment and could serve as a potential target for immunotherapy in HCC.

STRIP2 methylation analysis

DNA methylation is a crucial epigenetic mechanism in HCC development (17). Analysis of the UALCAN database showed that STRIP2 methylation levels were significantly lower in HCC tissues than in normal liver tissues (P<0.05, Figure 5A). MethSurv analysis further confirmed that multiple CpG sites within the STRIP2 gene were hypomethylated in HCC (Figure 5B). Notably, three specific CpG sites (cg15433470, cg26434548, and cg19243691) had methylation levels significantly associated with poorer prognosis in HCC patients (Table S4, Figure 5C-5E).

Figure 5 DNA methylation levels of STRIP2 and their impact on the prognosis of HCC. (A) STRIP2 methylation levels in HCC from the UALCAN database. (B) The STRIP2 methylation level from the MethSurv database. Kaplan-Meier survival curve of STRIP2 in (C) cg19243691, (D) cg15433470, (E) cg26434548. ***, P<0.001. BMI, body mass index; HCC, hepatocellular carcinoma; HR, hazard ratio.

PPI network analysis

To investigate STRIP2’s functional interactions, we constructed a PPI network using GeneMANIA. This analysis revealed that STRIP2 interacts with several key proteins (Figure 6A). Co-expression analysis further validated the correlations between STRIP2 and many of these interacting proteins, and the results were visualized in a heatmap (Figure 6B, Figure S3F).

Figure 6 Differences in drug sensitivity and TIDE scores between high- and low-STRIP2 expression groups. (A) Gene interaction network of STRIP2 analyzed using GeneMANIA. (B) Correlation between STRIP2 expression and associated genes. (C) Variations in drug sensitivity between high- and low-STRIP2 expression groups. (D) Differences in immunotherapy responses between high- and low-STRIP2 expression groups predicted by TIDE. (E) Sankey diagram illustrating immune therapy response distributions in high- and low-STRIP2 expression groups based on TIDE prediction. (F) List of screened compounds with the highest and lowest enrichment scores. (G,H) Molecular structures of U-0126 and Morphothebaine. ns, P≥0.05; *, P<0.05. IC50, half-maximal inhibitory concentration; TIDE, tumor immune dysfunction and exclusion.

Drug sensitivity, immunotherapy response, and STRIP2-targeted therapy screening

We assessed whether STRIP2 expression influences sensitivity to common HCC chemotherapeutics. Patients with low STRIP2 expression had greater sensitivity to 5-fluorouracil (5-FU) and oxaliplatin, evidenced by significantly lower IC50 values in the low STRIP2 group (P<0.05, Figure 6C).

Using the TIDE algorithm, we evaluated the impact of STRIP2 on immunotherapy outcomes. High STRIP2 expression was associated with significantly higher TIDE scores than low STRIP2 expression (P<0.05, Figure 6D). The immune response rate was also lower in the high STRIP2 group (Figure 6E), suggesting that STRIP2 may suppress anti-tumor immunity and reduce the efficacy of ICI therapy.

We performed cMap analysis based on the DEGs between high and low STRIP2 groups to identify potential STRIP2-targeted drugs. The three compounds with the most extreme enrichment scores (most negative or most positive) were selected as candidate therapeutic agents (Figure 6F). Among these candidates, MEK inhibitors and adrenergic receptor antagonists emerged as potential efficacy for treating HCC with high STRIP2 expression (Figure 6G,6H).

Immunohistochemical validation in clinical cohorts

We validated STRIP2 expression and its prognostic value in our clinical cohort of 120 HCC patients using IHC. Baseline clinicopathological characteristics are summarized in Table 1. IHC staining revealed that STRIP2 protein was localized in the cytoplasm of HCC cells (Figure 7A). Consistent with our bioinformatics findings, patients with high STRIP2 expression in their tumors had significantly shorter OS and DFS compared to those with low STRIP2 expression (Figure 7B,7C). Multivariate Cox regression analysis confirmed that STRIP2 was an independent risk factor for both DFS and OS in this cohort (Tables 2,3).

Figure 7 Association of STRIP2 expression with clinicopathological parameters in clinical cohort. (A) Representative IHC staining images of STRIP2 in HCC tissues (magnification, ×200; scale bar, 100 μm). (B) Differences in DFS between STRIP2 high and low expression groups. (C) Differences in OS between STRIP2 high- and low-expression groups. (D,E) Clinicopathologic factors influencing DFS identified via LASSO regression, with the corresponding lambda plot. (F,G) Clinicopathologic factors influencing OS identified via LASSO regression, with the corresponding lambda plot. (H) Nomogram predicting DFS in HCC patients. (I,J) Calibration plot and ROC curve validating the DFS nomogram model. (K) Nomogram predicting OS in HCC patients. (L,M) Calibration plot and ROC curve validating the OS nomogram. AJCC, American Joint Committee on Cancer; AST, aspartate aminotransferase; AUC, area under the curve; CI, confidence interval; DFS, disease-free survival; FPR, false positive rate; HCC, hepatocellular carcinoma; HR, hazard ratio; IHC, immunohistochemistry; LASSO, least absolute shrinkage and selection operator; MVI, microvascular invasion; OS, overall survival; PT, prothrombin time; ROC, receiver operating characteristic; TPR, true positive rate.

LASSO regression analysis was performed on 33 clinical variables to refine our prognostic model (Figure 7D-7G). STRIP2 expression, Child-Pugh class, prothrombin time (PT), aspartate aminotransferase (AST), albumin, microvascular invasion (MVI), and AJCC stage were identified as key predictors for DFS (Figure 7H). We constructed a DFS nomogram incorporating these factors, which achieved AUC values of 0.721, 0.841, and 0.822 for 1-year, 3-year, and 5-year DFS predictions (Figure 7I,7J). Similarly, an OS nomogram integrating STRIP2 expression, AST, and AJCC stage yielded AUC values of 0.770, 0.826, and 0.829 for 1-year, 3-year, and 5-year OS, respectively (Figure 7K-7M).

In vitro functional analysis of STRIP2 in HCC

To investigate the biological role of STRIP2 in HCC, we first analyzed its protein features using GeneCards (Figure S4A). This analysis suggested that STRIP2 is predominantly a cytoplasmic protein (Figure S4B). IF in Mahlavu and MHCC-97H cells confirmed that STRIP2 is mainly localized in the cytoplasm (Figure S4C). To evaluate STRIP2’s function, we established HCC cell lines with STRIP2 silencing, which was verified by Western blot (Figure 8A). CCK-8 proliferation assays demonstrated that STRIP2 knockdown significantly suppressed cell proliferation in both Mahlavu and MHCC-97H cells (Figure 8B,8C). Wound healing and Transwell assays showed that STRIP2 knockdown markedly reduced HCC cell migration (Figure 8D,8E) and invasion (Figure 8F-8H). Additionally, colony formation assays confirmed that STRIP2 knockdown substantially impaired the clonogenic capacity of HCC cells (Figure 8I,8J).

Figure 8 In vitro cell experiment of STRIP2 in HCC. (A) Western blot analysis confirming STRIP2 knockdown efficiency in Mahlavu and MHCC-97H cells. (B,C) CCK-8 assays performed on STRIP2 knockdown Mahlavu and MHCC-97H cells. (D,E) Wound healing assays assessing cell migration in STRIP2 knockdown Mahlavu and MHCC-97H cells (magnification, ×50). (F-H) Transwell migration and invasion assays evaluating the impact of STRIP2 knockdown in Mahlavu and MHCC-97H cells (1% crystal violet staining; magnification, ×200). (I,J) Colony formation assay showing the effects of STRIP2 knockdown in Mahlavu and MHCC-97H cells (1% crystal violet staining). **, P<0.01; ***, P<0.001. CCK-8, Cell Counting Kit-8; HCC, hepatocellular carcinoma; NC, negative control.

Discussion

HCC is a highly heterogeneous malignancy. Despite advancements in surgery, immunotherapy, and targeted therapy, the overall efficacy of current treatments remains limited, resulting in poor prognosis (2,18). Therefore, understanding the underlying biological mechanisms, identifying novel therapeutic targets, and discovering sensitive prognostic markers are essential for improving HCC treatment outcomes.

STRIP2, which contains two conserved domains (N1221 and DUF340), is a key regulator of cell morphology and cytoskeletal organization (9). It has been implicated in tumor cell proliferation and migration in multiple malignancies. In lung cancer, STRIP2 is highly expressed and promotes tumor progression via the Akt/mTOR signaling pathway and by facilitating epithelial-mesenchymal transition, correlating with poor survival (12). Similarly, STRIP2 is targeted by microRNA-30c-2-3p, and its knockdown significantly suppresses tumor aggressiveness in gastric cancer (13). Our pan-cancer analysis revealed that STRIP2 is upregulated in 16 malignancies, including HCC as well as breast, gastric, lung, and pancreatic cancers, consistent with previous studies. TCGA analysis further confirmed that STRIP2 expression is significantly higher in HCC tumor tissues compared to adjacent normal tissues, a finding validated across multiple datasets. These results suggest that STRIP2 may be a key driver of HCC development and progression.

Survival analysis demonstrated that high STRIP2 expression correlates with significantly shorter OS and DFS, indicating it may be an important determinant of HCC prognosis. We subsequently developed a prognostic prediction model integrating STRIP2 expression and AJCC stage, which exhibited strong predictive performance. In our clinical cohort, STRIP2’s prognostic significance was further validated. The STRIP2-based nomogram, incorporating clinical parameters, showed high accuracy and consistency in predicting outcomes. These findings provide the first clinical evidence that STRIP2 is an independent prognostic biomarker and can facilitate precision risk stratification in HCC patients.

DNA methylation is a crucial epigenetic regulatory mechanism in HCC pathogenesis and progression (19). Our study revealed that global STRIP2 methylation levels were significantly lower in HCC tissues. However, site-specific methylation analysis identified hypermethylation at specific CpG sites (cg15433470, cg26434548, cg19243691) that was significantly associated with poor prognosis, which suggested that aberrant STRIP2 methylation may play a role in HCC progression through epigenetic regulation.

GO and KEGG enrichment analyses indicated that STRIP2 is primarily involved in cell adhesion, neuroactive ligand-receptor interaction, and transmitter-gated ion channel activity, while GSEA linked high STRIP2 expression to neurotransmitter receptor function. These BP are interconnected and play critical roles in tumor invasion and metastasis. For example, ion channel activation regulates cell volume and morphology, influencing cell adhesion and invasive potential in colorectal and breast cancers (20,21). Similarly, neuroactive ligand-receptors can modulate cytoskeletal dynamics, thereby promoting the migratory and invasive behavior of tumor cells during HCC progression (22). Based on these findings, we hypothesize that STRIP2 might regulate ion channel activity to mediate cytoskeletal remodeling and cell adhesion, thereby promoting HCC cell migration and invasion. Conversely, low STRIP2 expression was associated with enhanced ribosomal function and translation initiation/elongation pathways, suggesting that STRIP2 may also influence HCC progression by modulating protein synthesis. Tumor cells often increase translational activity to support growth and invasiveness, and inhibitors of translation have shown promise as anticancer strategies (23). Further investigation into STRIP2’s role in translational regulation could provide novel insights for HCC therapy.

We performed in vitro experiments to validate the function of STRIP2. STRIP2 knockdown significantly suppressed HCC cell proliferation, migration, and invasion, effectively inhibiting the malignant phenotype of HCC cells. These findings underscore STRIP2’s potential as a therapeutic target for HCC, providing a new direction for future treatment strategies.

The tumor microenvironment (TME) plays a pivotal role in HCC initiation and progression. Immune profiling revealed significant differences in immune cell composition between high and low STRIP2 expression groups. High STRIP2 expression was associated with a significant increase in Th2 cells and T helper cells, while low STRIP2 expression correlated with increased CD8⁺ T cells and DCs. The Th1/Th2 balance is usually maintained in normal tissues, however, in many cancers, elevated Th2 cytokine secretion skews this balance toward a Th2-dominant response, which promotes tumor growth and immune evasion (24). Th2 cells secrete IL-4, IL-5, and IL-13, cytokines that suppress cytotoxic T lymphocyte activity and weaken anti-tumor immune responses (25). In contrast, CD8⁺ T cells recognize tumor antigens via the T-cell receptor and release perforin and granzyme to directly kill tumor cells (26). DCs are essential for antigen presentation and T-cell activation (27). They prime CD8⁺ T cells through cross-presentation and enhance anti-tumor immunity (28). These observations suggest that in HCC patients with high STRIP2 expression, an increase in Th2 cells may suppress the activity of CD8⁺ T cells and dendritic cells (DCs), creating an immunosuppressive state that facilitates tumor progression and immune escape. In addition, TIDE analysis confirmed that high STRIP2 expression was associated with significantly higher TIDE scores, indicating greater immune evasion and a reduced response to ICIs. Taken together, these results suggest that STRIP2 is a key regulator of immune escape in HCC and may serve as a useful biomarker for predicting immunotherapy response.

Oxaliplatin and 5-FU are widely used in HCC treatment. Oxaliplatin is known to induce immunogenic cell death and can enhance anti-tumor immunity when combined with ICIs (29). In hepatic arterial infusion chemotherapy, a combination of fluoropyrimidine and oxaliplatin has shown promising efficacy in HCC patients with extrahepatic metastases (30). Our study revealed that HCC patients with high STRIP2 expression exhibited significantly reduced sensitivity to 5-FU and oxaliplatin, as reflected by higher IC50 values, suggesting that STRIP2 may contribute to chemoresistance. Thus, STRIP2 could serve as both a predictive biomarker for chemotherapy response and a potential therapeutic target to overcome chemoresistance in HCC.

In our compound screening, MEK inhibitors and adrenergic receptor antagonists emerged as potential therapeutic agents for STRIP2-high HCC patients. Both drug classes act as inhibitors of the MAPK pathway, which is critically involved in HCC progression, recurrence, and drug resistance (31). MEK inhibitors block the MAPK/ERK pathway, reducing HCC cell proliferation, migration, and chemoresistance (32). Adrenergic receptor antagonists also suppress ERK signaling, inhibiting HCC cell proliferation and migration while promoting apoptosis (33). These findings suggest that MEK inhibitors and adrenergic receptor antagonists may represent promising treatment options for HCC patients with high STRIP2 expression.

Distinct from previous reports, this study provides a comprehensive evaluation of STRIP2 as a clinically relevant biomarker in HCC, combining evidence from bioinformatics analysis, clinical immunohistochemical validation, and in vitro functional experiments. STRIP2 expression showed consistent prognostic value for both OS and DFS following curative resection. In addition, its expression was also closely associated with sensitivity to chemotherapy and response to immunotherapy. Mechanistically, STRIP2 may contribute to HCC progression by regulating ribosomal activity and protein synthesis. Furthermore, STRIP2 appears to act as a key immunoregulatory factor, influencing the Th1/Th2 balance and thereby promoting tumor growth and immune evasion. These findings suggest that STRIP2 holds promise not only as a prognostic indicator but also as a therapeutic target in HCC.

Despite the comprehensive analyses performed, this study has several limitations. First, it relied primarily on bioinformatics analyses, clinical cohort validation, and in vitro experiments. The precise regulatory mechanisms of STRIP2 and its interactions with specific signaling pathways in HCC remain to be fully elucidated. Second, although validation was performed using public databases and a clinical IHC cohort, the sample size was relatively small and from a single center. Future multi-center, large-scale studies are needed to further verify the clinical utility of STRIP2. Lastly, although MHCC-97H and Mahlavu cell lines were utilized to model highly invasive HCC phenotypes, the expression and functional role of STRIP2 have not yet been validated in more commonly used HCC cell lines such as HepG2. We plan to conduct additional experiments in these classic models in future studies to further enhance the robustness and generalizability of our findings.

Conclusions

In summary, this study identifies STRIP2 as a key oncogenic driver in HCC, promoting tumor progression, immune evasion, and chemotherapy resistance. High STRIP2 expression correlates with poor prognosis and an immunosuppressive microenvironment, characterized by increased Th2 infiltration and reduced CD8⁺ T-cell activity. Functional experiments confirm that STRIP2 inhibition significantly suppresses HCC malignancy. These findings establish STRIP2 as a promising prognostic biomarker and therapeutic target, providing new insights for precision medicine and prognosis stratification in HCC.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-250/rc

Data Sharing Statement: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-250/dss

Peer Review File: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-250/prf

Funding: The study was supported by the Medical Science Research Project of Hebei (No. 20230117) and the Natural Science Foundation of Hebei Province (No. H2022206335).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-250/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. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This research was approved by the Ethics Committee of the Fourth Hospital of Hebei Medical University (No. 2023KS181). Informed consent was obtained from all participants.

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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