Introduction

Hepatocellular carcinoma (HCC) is commonly diagnosed worldwide, especially in eastern Asia1. Increasing HCC incidence and cancer-specific mortality account for the third leading cause of cancer deaths2, imposing a great burden to global health and economy. While surgical resection is recommended for HCC patients in the early stages, the 5-year postoperative recurrence rate ranges from 40 to 70%, resulting in poor clinical outcomes for HCC patients3,4. Immune checkpoint blockade has demonstrated effectiveness in treating advanced-stage HCC, however, there is no validated biomarker for clinical decision-making5. Therefore, there remains a need to identify novel prognostic and therapeutic biomarkers for personalizing treatment strategies.

LIM kinase 1 (LIMK1) is a member of the LIM kinase family and serves as a crucial regulator of the cytoskeleton by phosphorylating and inactivating cofilin. Growing evidence suggests that LIMK1 is a versatile protein involved in various physiological processes, including cell migration, cell cycle, apoptosis, and cell differentiation6,7. Furthermore, LIMK1 has also been demonstrated to play vital roles in tumor growth and metastasis7. Upregulation of LIMK1 has been observed in breast cancer8, gastric cancer9,10, colorectal cancer11,12,13, ovarian cancer14,15, and prostate cancer16,17,18. Dysregulated LIMK1 expression is correlated with lymph node metastasis and biochemical recurrence in prostate cancer17. However, the role of LIMK1 in HCC remains poorly understood, necessitating further research.

Currently, an increasing number of well-developed biological databases and well-designed cohorts with large samples are publicly available, which makes it possible to explore potential prognostic and treatment biomarkers for cancer patients19,20,21,22. The tissue microarray is an effective tool for basic research, prognostic oncology and drug discovery23. Tissue microarray is becoming an indispensable tool for discovering and validating biomarkers for personalized medicine24. In this study, we aimed to assess the clinical significance and underlying biological mechanisms of LIMK1 in HCC by integrating RNA-seq and tissue microarray data. We also investigated the potential of LIMK1 targeted inhibition for the treatment of HCC due to its promising outcomes in preclinical studies25,26.

Material and methods

Data source

The RNA-seq expression profile and clinical data of HCC samples were downloaded from the UCSC Xena database (https://tcga.xenahubs.net/) and the ICGC database (https://dcc.icgc.org/projects/LIRI-JP). We used log2 (transcript per kilobase million + 1) as the normalized gene expression levels. HCC patients without complete clinical and follow-up information were excluded. In the present study, 50 normal samples and 341 HCC cases in the TCGA cohort were included, while 202 normal samples and 240 HCC cases in the ICGC cohort were included. Clinical validation was performed using a tissue microarray of 90 paired HCC and normal tissues (HLivH180Su30), which was purchased from Shanghai Outdo Biotech Company (Shanghai, China). Three HCC samples were excluded due to poor specimen quality, leaving 87 paired samples for validation analysis. Informed consent was obtained from all HCC participants. This study conformed to the Declaration of Helsinki, and ethical approval was granted by the ethics committee of the Hubei No. 3 People’s Hospital of Jianghan University (202212). The included cohorts were displayed in Table S1.

Immunohistochemistry analysis of tissue microarray specimens

For immunohistochemistry (IHC) analysis, anti-LIMK1 antibody (ab119084) was purchased from Abcam. After deparaffinization and rehydration, antigen retrieval was carried out by heat-induced epitope retrieval in citrate buffer (pH 6.0) at 95 °C for 20 min using a microwave oven. The primary antibody was then incubated with the tissues at 4 °C overnight. After washing, the sections are incubated with a biotin-conjugated secondary antibody that binds to the primary antibody. HRP/DAB kit (Maixin Biotech, Fuzhou, China) was used to visualize the staining. The IHC score was assessed by calculating the percentage of positive area and intensity, as described in a previous study27.

Expression level and prognostic value of LIMK1

This study compared the expression of LIMK1 between normal and HCC samples using Wilcoxon rank-sum test. The “pROC” package was employed to construct receiver operating characteristic (ROC) curves to assess the diagnostic accuracy of LIMK1.To evaluate the prognosis value of LIMK1, the Kaplan–Meiers method was utilized by “survminer” package. To adjust for other influencing clinical factors, multivariate Cox regression analyses were conducted, offering a more nuanced understanding of the independent effect of LIMK1 on patient prognosis.

The “rms” package was used to create a multivariate Cox model by integrating LIMK1 and clinicopathological features. To enhance the interpretability of the model and quantify individual feature contributions to risk predictions, we applied SHAP (SHapley Additive exPlanations) analysis using the “kernelshap” R package. Time-dependent ROC curve was calculated to evaluate the prediction accuracy for patient prognosis. Calibration curve was plotted to estimate the model performance, providing insights into potential overfitting or underfitting. Decision curve was also plotted to assess the clinical utility by integrating the costs and benefits of different decision thresholds, helping to determine the value of the model in clinical decision-making.

Functional analyses of LIMK1

The differentially expressed genes (DEGs) in high versus low LIMK1 groups were identified through the “limma” R package. Genes with a fold change of greater than two and a false discovery rate (FDR) of less than 0.05 were classified as DEGs. To gain further insight into the biological significance, gene set enrichment analysis (GSEA) was performed through the “clusterProfiler” and “msigdbr” R packages. The p value of the GSEA results was adjusted using the Benjamini–Hochberg method. GO, KEGG and Hallmark terms with adjusted p values less than 0.05 were considered as significant enrichment28.

Tumor immune microenvironment analysis

The immune landscape was systematically profiled using an integrative approach combining TIMER29, EPIC30, quanTIseq31, ssGSEA32, MCP-counter33, and ESTIMATE34 algorithms by the “IOBR” package. Immunotherapy have been approved for many cancers, therefore, the potential of LIMK1 as an immunotherapy biomarker was explored. This study evaluated the association between LIMK1 gene expression and immune therapy response in HCC patients using the Tumor Immune Dysfunction and Exclusion (TIDE) online tool (http://tide.dfci.harvard.edu)35. Transcriptomic data from HCC patients were analyzed via the TIDE platform to characterize T-cell dysfunction and immune exclusion features within the tumor microenvironment. The Braun202036, GSE10612837, PRJNA48262038 and PRJEB2370939 cohorts were obtained for further validation of LIMK1 as a potential biomarker for immunotherapy response prediction.

Chemotherapy sensitivity analysis

Drug screening datasets have great potential for drug discovery and clinical treatment response prediction. The “OncoPredict” package was utilized to leverage large-scale gene expression and drug screening data. This package employs ridge regression, a type of linear regression that includes regularization to prevent overfitting, to calculate the IC50 values of various chemotherapy agents22. The association between LIMK1 levels and the estimated IC50 values of various drugs was examined.

Results

Expression level and prognostic value of LIMK1

LIMK1 expression in pan-cancer was displayed in Fig. 1A, B. Compared to normal samples, LIMK1 was overexpressed in most cancers. Univariate Cox result of LIMK1 in 33 cancer types was shown in Fig. 1C, and LIMK1 was a risk factor of overall survival in several cancers. In HCC samples, LIMK1 was upregulated (Fig. 2A) and showed good diagnostic performance for HCC (Fig. 2B, C). LIMK1 expression was significantly elevated in advanced-stage (III & IV) versus early-stage (I&II) HCC (Fig. 2D), suggesting its role in tumor progression. HCC patients with high expression of LIMK1 indicated an unfavorable clinical outcome (TCGA log-rank P < 0.001; ICGC log-rank P = 0.009) (Fig. 2E, F). Cox regression analyses identified LIMK1 high expression as a predictor of unfavorable outcome in TCGA cohort (univariate Cox: HR = 2.26, P < 0.001; multivariate Cox: HR = 2.18, P < 0.001) and ICGC cohort (univariate Cox: HR = 2.23, P = 0.011; multivariate Cox: HR = 1.95, P = 0.039) (Table S2, Fig. 2G, H).

Fig. 1
figure 1

Expression level and prognostic value of LIMK1 in pan cancer. (A, B) Expression levels of LIMK1 between normal and tumor samples; (C) Univariate Cox result of LIMK1.

Full size image
Fig. 2
figure 2

Expression level and prognostic value of LIMK1 in HCC. (A) Expression levels of LIMK1 between normal and tumor samples; (B, C) ROC curves for diagnosis of HCC; (D) Expression levels of LIMK1 in different stage; (E, F) Survival curves of LIMK1 in HCC; (G, H) Forest plot of multivariate Cox analyses.

Full size image

For clinical validation of LIMK1 protein expression, IHC was performed on a tissue microarray (HLivH180Su30). IHC analysis revealed distinct cytoplasmic localization of LIMK1 in HCC tumor tissues, exhibiting diffuse granular brown staining (Fig. 3A). In contrast, adjacent normal liver tissues showed weak or negligible staining. Elevated LIMK1protein levels were observed in HCC tissues (Fig. 3B). Patients with high LIMK1 expression exhibited a diminished survival duration and an elevated risk of mortality (Fig. 3C). Cox regression analysis demonstrated that high LIMK1 expression was a reliable indicator of poor prognosis in HCC patients (univariate Cox: HR = 2.09, P = 0.004; multivariate Cox: HR = 2.18, P = 0.006) (Table S2).The multivariate Cox model incorporating LIMK1 and various clinical features is presented in Fig. 4A.The mean SHAP value of each variable in the multivariate Cox model was shown in Fig. 4B. The waterfall plot showed the contribution of each feature to the model’s output (Fig. 4C). The AUC values of 1-year, 2-year, 3-year OS were 0.90, 0.87, and 0.80 respectively, indicating excellent performance in prognostic assessment (Fig. 4D). The calibration curve indicated good agreement between the predicted and actual results (Fig. 4E). The decision curve showed that the model can more effectively guide clinical decision-making (Fig. 4F).

Fig. 3
figure 3

Clinical validation in the tissue microarray cohort. (A) Representative IHC images of LIMK1 expression between HCC and adjacent normal tissues; (B) IHC scores of LIMK1 between HCC and adjacent normal tissues; (C) Survival curve of LIMK1.

Full size image
Fig. 4
figure 4

Model construction and evaluation. (A) Forest plot of multivariate Cox analysis; (B) Mean SHAP value of each variable in the multivariate Cox model; (C) Contribution of each variable to the model; (D) Time-dependent ROC curves of 1-year, 2-year, 3-year OS; (E) Calibration curve of 1-year, 2-year, 3-year OS; (F) Decision curve of the model.

Full size image

Functional analyses

The GO and KEGG enrichment results revealed immune-related pathways, including immune response, adaptive immune response, leukocyte transendothelial migration, and complement and coagulation cascades (Fig. 5A–D). The hallmark enrichment results showed that LIMK1 was implicated in various biological progress, including epithelial-mesenchymal transition (EMT) and angiogenesis (Fig. 5E).

Fig. 5
figure 5

GSEA results of LIMK1. (A, B) GSEA plots of GO terms; (C, D) GSEA plots of KEGG terms (KEGG Copyright Permission was obtained from from the Kanehisa laboratory); (E) Bar plot of hallmark terms.

Full size image

Tumor immune microenvironment analysis

To evaluate the function of LIMK1 in the tumor immune microenvironment of HCC, the association between LIMK1 expression and various immune cells was investigated. Higher immune scores and ESTIMATE scores were noted in LIMK1 high expression group in the TCGA and ICGC datasets (Fig. 6A, B). HCC patients with high LIMK1 expression levels had higher levels of immunosuppressive cells, including myeloid-derived suppressor cells (MDSCs), fibroblasts, regulatory T cells (Tregs), and macrophages M2 (Fig. 6A, B). Additionally, LIMK1 expression showed a positive correlation with numerous immune modulators involved in antigen presentation, cell adhesion, co-inhibitors, co-stimulators, ligands, and receptors (Fig. 7A, B). Importantly, a strong correlation was identified between LIMK1 and key immune checkpoint genes, such as PDCD1, CD274, and CTLA4.

Fig. 6
figure 6

Relationship between LIMK1 and immune cells. (A) Heatmap of the immune infiltrating cells in TCGA dataset; (B) Heatmap of the immune infiltrating cells in ICGC dataset.

Full size image
Fig. 7
figure 7

Relationship between LIMK1 and immune modulators. (A) Heatmap of the immune modulator molecules in TCGA dataset; (B) Heatmap of the immune modulator molecules in ICGC dataset.

Full size image

Immunotherapy response prediction

To further assess LIMK1’s potential for predicting immunotherapeutic response, the relationship between the LIMK1 and TIDE score was investigated. TIDE analysis demonstrated that elevated LIMK1 expression correlated with heightened immune dysfunction and exclusion (Fig. 8A, B). Responders consistently displayed lower LIMK1 expression levels, whereas non-responders exhibited significantly higher LIMK1 expression (Fig. 8C, D), indicating its potential role in diminishing therapeutic efficacy through tumor immune evasion. Notably, patients with high LIMK1 expression showed markedly reduced immunotherapy response rates compared to those with low expression (TCGA cohort: 14% vs. 58%; ICGC cohort: 20% vs. 46%, respectively) (Fig. 8E, F).

Fig. 8
figure 8

Predictive value of LIMK1 in immunotherapy response through TIDE analysis. (A, B) The TIDE score, dysfunction and exclusion score in high versus low LIMK1 groups; (C, D) LIMK1 expression levels between responder and non-responder; (E, F) Response rates in high versus low LIMK1 groups.

Full size image

Moreover, LIMK1 demonstrated robust predictive capacity across multiple immunotherapy cohorts. In the Braun2020 and PRJNA482620 cohorts, elevated LIMK1 expression correlated with significantly poorer prognosis in patients receiving anti-PD-1 immunotherapy (Fig. 9A, B). This trend extended to combination therapies, where high LIMK1 levels predicted reduced survival in the PRJEB23709 cohort treated with dual anti-PD-1/anti-CTLA-4 blockade (Fig. 9C). LIMK1 overexpression showed unfavorable outcomes in the GSE106128 cohort undergoing dendritic cell-targeted immunotherapy (Fig. 9D).

Fig. 9
figure 9

High LIMK1 expression correlates with poor prognosis in patients receiving immunotherapy. (A) Braun2020 cohort; (B) PRJNA482620 cohort; (C) PRJEB23709 cohort; (D) GSE106128 cohort.

Full size image

Chemotherapy sensitivity analysis

LIMK1 expression was negatively correlated with the IC50 values of various chemotherapy agents (Fig. 10A, B). The LIMK1 high expression group had lower IC50 values of various chemotherapeutic drugs, including cisplatin, docetaxel, 5-fluorouracil, paclitaxel, and gemcitabine (Fig. 10C, D). LIMK1 high expression group also had lower IC50 values of anti-angiogenesis treatment drugs, including sorafenib. These results might be useful in guiding clinical treatment for HCC.

Fig. 10
figure 10

Predictive value of LIMK1 in drug sensitivity. (A, B) Correlations of LIMK1 with IC50 values of various drugs; (C, D) IC50 values of various drugs between two LIMK1 groups.

Full size image

Discussion

Recent studies have indicated that LIMK1 significantly enhances tumor growth across various cancers by promoting cell proliferation, migration, and invasion8,9,10,13,15,18. However, the mechanism of LIMK1 in HCC remains poorly understood. We conduct a comprehensive analysis of LIMK1 in HCC utilizing high-throughput RNA-sequencing and tissue microarray data. Our findings reveal that elevated LIMK1 expression correlates with advanced tumor stages, poor survival outcomes, and a unique immunosuppressive tumor microenvironment. Importantly, LIMK1-high patients exhibit enhanced sensitivity to chemotherapy and poor response to immunotherapy, positioning it as a promising candidate for personalized treatment strategies.

Our findings reveal that LIMK1 is significantly overexpressed in HCC tissues compared to adjacent normal tissues across TCGA, ICGC, and in-house cohorts. The expression level of LIMK1 effectively stratified patients into subgroups with opposite survival outcomes, independent of various clinicopathological features. Notably, the multivariate Cox model incorporating LIMK1 achieves robust prognostic accuracy (1-year AUC = 0.90). The calibration curve shows good consistency between our predictive values and the actual values, and decision curve shows great potential for application in clinical practice. While previous studies reported nuclear LIMK1 as a prognostic marker in HCC40, our tissue microarray data identified cytoplasmic LIMK1 as an independent predictor of poor survival. This discrepancy may stem from tumor heterogeneity among HCC patients.

The biological role of LIMK1 in tumor development has attracted increasing interest from researchers. Knockdown of LIMK1 has been reported to impede cell proliferation and invasion of several cancer cells7,9,13,15,18. Growing evidence suggests that LIMK1 plays a role in multiple tumorigenic processes, such as extracellular matrix degradation, EMT, and angiogenesis6,7,41,42. An animal study demonstrated that LIMK1 knockdown reduced angiogenesis41. One recent study found that LIMK1 is upregulated in HCC cells, and its downregulation inhibits cell proliferation, metastasis, and EMT42. LIMK1 was identified as a downstream gene of miR-374c-5p, and the miR-374c-5p/LIMK1 axis was found to suppress EMT through inactivation of the Wnt/β-catenin pathway43. In our study, GSEA results demonstrate that LIMK1 was implicated EMT and angiogenesis, which are consistent with previous reports.

Previous research has shown that overexpression of LIMK1 can facilitate the invasion of drug-resistant osteosarcoma cells44. Knockdown of LIMK1 inhibits cell migration and enhances drug sensitivity to chemotherapy45. Our study reveals LIMK1 as potential biomarker in predicting chemotherapy response. For example, high LIMK1 expression is associated with increased sensitivity to sorafenib, a first-line treatment for HCC patients. The FOLFOX4 protocol, a 5-fluorouracil based chemotherapy regimen, was also approved for unresectable and metastatic HCC patients. High LIMK1 expression is negatively correlated with 5-fluorouracil sensitivity in HCC patients. These findings are valuable for assessing chemotherapy sensitivity and guiding individualized treatment for HCC patients.

Although significant progress has been made in immunotherapy46,47, comprehensive understanding of immune microenvironment in HCC is still needed. GSEA revealed that LIMK1 expression was associated with immune-related biological processes and pathways. HCC patients with high LIMK1 expression exhibit higher levels of immunosuppressive cells, including MDSC, fibroblasts, M2 macrophages, and Tregs, all of which were key regulators of HCC immunity in immunotherapy48,49. Furthermore, TIDE analysis indicated that high LIMK1 levels correlate with higher immune dysfunction and exclusion scores, indicating its potential role in facilitating tumor immune escape mechanisms. Importantly, multi-cohort validation consistently demonstrates that LIMK1 overexpression correlate with significantly poorer prognosis in patients receiving immune checkpoint blockade and dendritic cell-targeted therapies. While our data establish association between LIMK1 and immunosuppression environment in HCC, the mechanistic basis requires careful interpretation. GSEA analysis revealed significant enrichment of LIMK1-high tumors in EMT pathway, consistent with reports that LIMK1 promotes cytoskeletal remodeling to drive EMT in HCC42,43. Mechanistically, EMT transcription factors (e.g., ZEB1/Snail) upregulate immune checkpoints and recruit immunosuppressive cells via chemokine production (e.g., TGF-β/IL-6)50. Therefore, the “LIMK1-EMT-Immune Evasion” axis might be a plausible mechanism linking LIMK1 overexpression to immunosuppression.

This study has several limitations. First, the validation cohort’s small sample size and single-center design may limit generalizability, necessitating multi-center validation with larger cohorts. Second, while our data identified LIMK1 as an indicator of immunosuppression in HCC, the mechanistic link between LIMK1 and immune cell infiltration remains correlative. Although the "LIMK1-EMT-Immune Evasion" axis provides a mechanistic framework for our observed association, future studies should validate whether LIMK1 directly regulates immune cell recruitment via in vitro co-culture models or in vivo knockout experiments. Lastly, the predictive value of LIMK1 for immunotherapy and chemotherapy requires prospective validation in clinical trials.

Conclusion

In conclusion, LIMK1 is found to be helpful in prognosis assessment, immune status estimation, immunotherapy response prediction and chemotherapy drug sensitivity evaluation in patients with HCC. These findings pave the way for future research aimed at leveraging LIMK1 as a prognostic indicator and therapeutic target, potentially leading to more effective strategies for managing HCC and improving outcome. Future research should focus on mechanistic validation and translational applications to fully harness LIMK1’s clinical utility.