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02 June 2024: Database Analysis  

ANXA5: A Key Regulator of Immune Cell Infiltration in Hepatocellular Carcinoma

Weichen Wang1A*, Dongquan Liu1B, Jiaming Yao1C, Zhongxu Yuan1E, Liang Yan1F, Baoqiang Cao1AG

DOI: 10.12659/MSM.943523

Med Sci Monit 2024; 30:e943523

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Abstract

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BACKGROUND: Hepatocellular carcinoma (HCC) poses a significant threat to human life and is the most prevalent form of liver cancer. The intricate interplay between apoptosis, a common form of programmed cell death, and its role in immune regulation stands as a crucial mechanism influencing tumor metastasis.

MATERIAL AND METHODS: Utilizing HCC samples from the TCGA database and 61 anoikis-related genes (ARGs) sourced from GeneCards, we analyzed the relationship between ARGs and immune cell infiltration in HCC. Subsequently, we identified long non-coding RNAs (lncRNAs) associated with ARGs, using the least absolute shrinkage and selection operator (LASSO) regression analysis to construct a robust prognostic model. The predictive capabilities of the model were then validated through examination in a single-cell dataset.

RESULTS: Our constructed prognostic model, derived from lncRNAs linked to ARGs, comprised 11 significant lncRNAs: NRAV, MCM3AP-AS1, OTUD6B-AS1, AC026356.1, AC009133.1, DDX11-AS1, AC108463.2, MIR4435-2HG, WARS2-AS1, LINC01094, and HCG18. The risk score assigned to HCC samples demonstrated associations with immune indicators and the infiltration of immune cells. Further, we identified Annexin A5 (ANXA5) as the pivotal gene among ARGs, with it exerting a prominent role in regulating the lncRNA gene signature. Our validation in a single-cell database elucidated the involvement of ANXA5 in immune cell infiltration, specifically in the regulation of mononuclear cells.

CONCLUSIONS: This study delves into the intricate correlation between ARGs and immune cell infiltration in HCC, culminating in the development of a novel prognostic model reliant on 11 ARGs-associated lncRNAs. Furthermore, our findings highlight ANXA5 as a promising target for immune regulation in HCC, offering new perspectives for immune therapy in the context of HCC.

Keywords: Biomarkers, Tumor, tumor microenvironment, biomarkers

Introduction

Hepatocellular carcinoma (HCC) is the most prevalent form of liver cancer and is responsible for the fourth highest number of cancer-related deaths worldwide. Risk factors include chronic hepatitis B and C, alcohol addiction, metabolic liver disease, especially non-alcoholic fatty liver disease, and dietary toxins, such as aflatoxin [1].

Various reprogramming in HCC is one of the important causes of tumor progression [2]. Recent advances in anti-cancer immunotherapy, including inhibition of programmed cell death protein-1 (PD-1)/PD-L1, cytotoxic T-lymphocyte-associated protein-4 (CTLA4), various immune cell therapies, and vaccines, have improved the survival rates of patients with HCC to some extent [3]. The effectiveness of immunotherapy is related to the infiltration of various immune cells in the tumor microenvironment (TME) of HCC [4]. However, the complex regulation of immune cells and various factors in the TME lead to the tumor heterogeneity of HCC, highlighting the need for a deeper understanding of its molecular mechanisms for personalized patient treatment [5].

Immune suppressive cells, such as myeloid-derived suppressor cells, tumor-associated macrophages (TAMs), tumor-associated neutrophils, cancer-associated fibroblasts, and Tregs, are critical components of the TME that promote the growth and invasion of HCC [6]. HBV-specific CD8 T cells, HBV non-specific CD8 cells, CD4 T cells, B cells, natural killer cells, natural killer T cells, Kupffer cells, and hepatic stellate cells all participate in HBV-related HCC occurrence [7]. CCL18+ TAMs are associated with poor prognosis in HCC, as they impair the function of cytotoxic T cells [8]. Fetal-like TAMs can promote HCC progression by interacting with PLVAP+ endothelial cells [9]. Cancer-associated fibroblasts in HCC induce immune suppression phenotypes by secreting cytokines and recruiting monocytes and dendritic cells, thereby promoting immune evasion, tumor growth, and metastasis [10,11]. The infiltration of fibroblasts and macrophages in the TME is associated with immune therapy responses [12]. However, the intricate regulation of immune cells and various factors in the TME contribute to the tumor heterogeneity of HCC, underscoring the necessity for a deeper comprehension of its molecular mechanisms to tailor personalized patient treatments [5].

Anoikis, a common type of programmed cell death, is primarily associated with the interaction between the extracellular matrix and cells [13]. In tumors, anoikis is often disrupted and is a crucial mechanism for tumor metastasis [14]. The abnormal infiltration of immune cells in tumors is closely related to anoikis, and together they regulate tumor metastasis [14]. During tumor progression, detached tumor cells bypass the death signaling pathway and evade immune recognition, which is the primary reason for the development of anoikis resistance [15]. However, a systematic analysis of the interaction between anoikis-related genes (ARGs) and tumor immune cells is still lacking in HCC.

Long non-coding RNAs (lncRNAs) are RNA molecules longer than 200 nt that do not have protein-coding functions [16]. They play a crucial role in homeostasis and tumorigenesis [17] and serve as tumor markers for early screening, diagnosis, prognosis, and predicting responses to medication [18,19]. LncRNA-related models are essential in colon cancer [19], lung adenocarcinoma [20], pancreatic adenocarcinoma [21], and other cancers. LncRNAs regulate the expression, localization, stability, and activity of their binding partners, triggering a series of carcinogenic phenotypes, such as sustained proliferation, metabolic abnormalities, increased stemness, and metastasis, leading to the occurrence and progression of HCC [22]. A novel lncRNA, RP11-386G11.10, reprograms lipid metabolism to promote the progression of HCC [23]. LncRNA HEPFAL accelerates ferroptosis in HCC by regulating the ubiquitination of SLC7A11 [24]. In the study of Liu et al, LINC01234 promoted HCC progression by coordinating aspartate metabolism reprogramming, which means that lncRNAs have an important role in HCC [25].

Therefore, this study focused on analyzing HCC samples from The Cancer Genome Atlas (TCGA) database to investigate the relationship between ARGs and the mutual regulation of immune cells. We developed a novel risk model based on 11 ARG-related lncRNAs and identified new regulatory targets for HCC immune cells. Furthermore, we validated our findings using HCC single-cell sequencing data from the Gene Expression Omnibus (GEO) database. In single-cell data, we observed a close association between the key gene Annexin A5 (ANXA5) and immune cells in ARGs. These findings hold significant implications for studying the TME of HCC and exploring immune-related therapeutic targets, providing theoretical support for subsequent clinical translation. This study provides new molecular targets for the immunotherapy of HCC and new research avenues for the regulation of anoikis and immune cells.

Material and Methods

DATA COLLECTION:

We obtained transcriptome sequencing data and clinical information of HCC from the TCGA database (https://portal.gdc.cancer.gov/) [26], comprising 50 samples of normal liver tissue and 374 samples of HCC. The corresponding clinical characteristics of patients are presented in Table 1. Additionally, we retrieved single-cell sequencing data (GSE125449) of HCC from the GEO database (https://www.ncbi.nlm.nih.gov/geo/) [27]. The single-cell sequencing data included biospecimens from 9 patients with HCC and 10 patients with intrahepatic cholangiocarcinoma. This dataset delineates the single-cell transcriptomic landscape of liver cancer samples from 19 patients, providing valuable insights into the TME of HCC. We extracted a total of 434 ARGs from GeneCards (https://www.genecards.org/) and selected 61 genes with correlation scores greater than 2.

SINGLE-SAMPLE GENE SET ENRICHMENT ANALYSIS:

The single-sample gene set enrichment analysis (ssGSEA) method enables enrichment analysis of samples based on a given gene set, providing enrichment scores. This allowed us to compare the enrichment status of the given gene set between different groups [28]. We used the GSVA (version 1.50.0), limma (version 3.58.1), and GSEABase (version 1.64.0) packages in R to compute the enrichment scores for ARG score, immune cells, and immune function in the HCC transcriptome data. GSVA and GSEABase offer greater capability to detect subtle pathway activity changes within sample populations [29,30]. The limma package is an R/Bioconductor software package that provides an integrated solution for analyzing data from gene expression experiments [31]. Based on the median value of ARG scores, we divided the HCC samples into high-ARG-score and low-ARG-score groups. To compare the differences in immune cells and immune function between the 2 groups, we used the limma, reshape2 (version 1.4.4), and ggpubr (version 0.6.0) packages in R [32]. The reshape2 and ggpubr packages assisted us in visualizing the results. Additionally, we used the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm to evaluate the immune and stromal scores between the low-ARG-score and high-ARG-score groups [33].

IDENTIFICATION OF ARG-RELATED LNCRNAS:

We conducted univariate Cox regression analysis to investigate the relationship between 61 ARGs and the survival of HCC patients, identifying 24 ARGs significantly associated with HCC prognosis (P<0.05). Subsequently, we used a stringent criterion, utilizing the Wilcoxon rank sum test (corFilter=0.6, pvalueFilter=0.001), to screen for lncRNAs highly correlated with ARGs. This rigorous standard helped us attain more relevant genes. Additionally, we analyzed whether the screened lncRNAs exhibited differential expression levels in HCC and were associated with patient prognosis. Ultimately, we identified 34 genes highly correlated with ARGs, differentially expressed in HCC, and associated with patient prognosis.

CONSTRUCTION OF THE RISK MODEL:

To mitigate the risk of overfitting, we used the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model via the glmnet R package (version 4.1–8) [34,35]. In addition to providing the patient’s gene expression, TCGA’s liver cancer data provides the corresponding patient’s survival time and survival status. We standardized the HCC data and extracted the expression levels of 34 lncRNAs for integration with survival data into a unified table, serving as the input for LASSO analysis (Supplementary Table 1). The lncRNAs signature was derived as follows: lncRNAs risk score=∑ (βi×Expi), where βi denotes the LASSO coefficient of the i-th gene, and Expi represents the expression value of the i-th candidate gene. It is well-established that LASSO regression effectively eliminates redundant variables by penalizing regression coefficients based on parameter size. We used the LASSO regression method for feature selection and constructing prediction signatures. LASSO regression attenuates coefficient estimates toward zero, contingent on the regularization parameter λ [36]. To ascertain the optimal λ, we used 10-fold cross-validation and selected λ based on the minimum criterion. Subsequently, patients were stratified into high and low lncRNAs risk groups according to the median lncRNAs score, and their association with overall survival was assessed using Kaplan-Meier analysis. We used the pheatmap package (version 1.0.12) to visually represent the obtained lncRNA signature in the form of a heatmap. Furthermore, we generated a receiver operating characteristic (ROC) curve employing the timeROC package (version 0.4) and evaluated its accuracy using the area under the curve (AUC) model [37].

RISK MODEL AND CLINICAL FEATURES OF HCC:

We evaluated the relationship between the risk score and the clinical characteristics of HCC patients, including age, sex, disease stage, and grade, using univariate and multivariate regression analysis. Additionally, we used the rms package (version 6.7–1) and survival package (version 3.5–7) to draw column diagrams and 1-year, 3-year, and 5-year calibration curves to further analyze the risk model and HCC clinical characteristics.

INFILTRATION OF IMMUNE CELLS:

To quantify the relative proportions of infiltrating immune cells, we used various methods, such as TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, MCPCOUNTER, XCELL, and EPIC [38]. CIBERSORT and CIBERSORT-ABS are versatile computational methods used to quantify the cellular composition of large-scale tissue gene expression profiles, enabling accurate estimation of the immune composition of tumor biopsies [39,40]. QUANTISEQ, XCELL, EPIC, and MCPCOUNTER are computational pipelines for quantifying immune cell fractions from bulk RNA sequencing data obtained from blood or tumor samples [41–43]. These methods are commonly used for quantifying immune cell content in RNA-seq data [40,44]. The TIMER database (https://cistrome.shinyapps.io/timer/), which comprises 10 897 samples across 32 cancer types from TCGA, was used to analyze the level of tumor-associated immune cell infiltration in the TME [45]. Additionally, we used TIMER to investigate the correlation between genes and immune cell infiltration. Based on the data obtained from the Human Protein Atlas (https://www.proteinatlas.org/), the hub gene was considered as a novel biomarker of HCC. We used the pheatmap package (version 1.0.12) to visually represent the quantified immune cells mentioned above in the form of a heatmap. From published literature, we gathered 39 common immune checkpoints for subsequent analysis [46]. Using the ggplot2 package (version 3.4.4) and limma package, we visualized the differences in cell types and immune checkpoints between different groups through boxplots.

SINGLE-CELL DATA PROCESSING AND DIMENSIONALITY REDUCTION:

To process the single cell data, we used the Seurat package (version 5.0.1) within R software [47]. The Seurat package is the most commonly used method for analyzing single-cell data, encompassing functionalities such as data importation, filtering, normalization, feature selection, scaling, dimensionality reduction, clustering, data visualization, and differential gene expression analysis [48–50]. Due to limitations in sequencing depth and inherent gene expression levels, single-cell sequencing data often present a matrix with numerous “0” values, a phenomenon referred to as “drop-out”. These zeros can result from insufficient sequencing depth preventing gene capture or from genes not being expressed. Normal cell gene expression levels typically range from 3000 to 4000. Additionally, cells with fewer detected genes, low count depth, and a high proportion of mitochondrial counts are often deemed low-quality cells, as they can represent dying or ruptured membrane cells [51]. The gene and count features were identified for each sample, and cells with less than 300 or more than 7000 features were filtered out. Additionally, cells with a mitochondrial RNA percentage greater than 10 or ribosome RNA percentage less than 3 were removed. The samples were integrated using the merge function and underwent normalized, scaled, and principal component analysis. To address any potential batch effects within subgroups, we used the harmony package (version 1.2.0) [49]. The FindAllMarkers function was used to identify the marker genes of each cluster. Finally, dimensionality reduction clustering was performed on the resulting dataset using UMAP. Given our primary focus on immune cells, we used the immune cell marker PTPRC to distinguish cells into immune and non-immune categories. Subsequently, immune cells were extracted for separate clustering and cell annotation using SingleR (version 2.4.1). SingleR assigns cellular identities to single-cell transcriptomes by comparing them to reference datasets of pure cell types sequenced via microarray or RNA-seq [52]. We quantified the expression levels of monocytes in HCC samples using the ssGSEA method and analyzed the relationship between monocyte content in samples and patient prognosis using the survival package (version 3.5–7). We quantified the level of monocyte infiltration using the ssGSEA method and analyzed the impact of ANXA5 and monocytes on HCC patient prognosis.

STATISTICS:

R software (version 4.1.3, https://www.r-project.org/) and associated R packages were used to perform all graphical and statistical analyses. The t test was used to compare the differences between 2 groups of samples. Survival analysis was performed using the log-rank test. P<0.05 was considered statistically significant.

Results

RELATIONSHIP BETWEEN ARGS AND IMMUNE INFILTRATION IN HCC:

The research process outline is shown in Figure 1. Following the methods outlined in the section “Single-sample gene set enrichment analysis,” we scored the expression levels of 61 ARGs in HCC samples. We used the ssGSEA method to assess the expression levels of ARGs in HCC samples and categorized them into low- and high-ARG-score groups based on the median. Additionally, using the approach described in the section “Infiltration of immune cells,” we quantified the immune cell populations and functions within HCC samples. To assess the relationship between ARGs and immune cell infiltration, we compared the differences in immune cell composition and function between the 2 sample groups (Figure 2A, 2B). Our findings revealed that the high-ARG-score group had increased immune cell infiltration and immune function scores. Furthermore, correlation analysis indicated a positive correlation between ARG score and immune cell infiltration and function in HCC samples (Figure 2C).

RELATIONSHIP BETWEEN ARGS AND IMMUNE CELLS:

Immune checkpoint blockade therapy targeting immune checkpoints can inhibit negative regulation signal transduction and alleviate T-cell exhaustion [53]. Consequently, immune checkpoint blockade therapy can reverse the immunosuppressive microenvironment and reduce the probability of tumor immune escape, thus improving the prognosis [54]. Given the close relationship between ARG expression level and HCC immune cell infiltration and immune function, we further evaluated the connection between ARGs and immune checkpoints (Figure 3A). We then compared the differences in immune indices, including StromalScore (P<2.22e-16), ImmuneScore (P<1.2e-06), and ESTIMATEScore (P<1e-13) between the high- and low-ARG-score groups (Figure 3B–3D). Our findings indicated that the high-ARG-score group exhibited increased expression levels of immune checkpoints and higher immune indices, which implies improved immune efficacy.

ARG-RELATED LNCRNAS:

To gain a comprehensive understanding of the role of ARGs in HCC, we identified 24 prognosis-related ARGs and selected 33 lncRNAs related to ARGs. The screening criteria for lncRNAs were correlation >0.6, P value <0.001. From these 33 lncRNAs, we selected 11 lncRNAs (NRAV, MCM3AP-AS1, OTUD6B-AS1, AC026356.1, AC009133.1, DDX11-AS1, AC108463.2, MIR4435-2HG, WARS2-AS1, LINC01094, and HCG18) and constructed a COX risk model. The heatmap (Figure 4A) showed the differential expression of these 11 lncRNAs between the high-risk and low-risk groups, with higher expression levels in the high-risk group. The prognostic analysis based on the risk model revealed that patients in the low-risk group had a better survival status (Figure 4B; P<0.001). Furthermore, the area under the ROC curve (AUC) for 1 year, 3 years, and 5 years of overall survival were all greater than 0.65, indicating the model’s reliability (Figure 4C; AUC at 1 year: 0.736; AUC at 3 years: 0.698; AUC at 5 years: 0.695).

PROGNOSTIC MODEL EVALUATION:

We also performed univariate and multivariate COX regression analyses and found that risk score and stage were associated with the prognosis of HCC patients (Figure 5A, 5B; riskScore <0.001). We developed a nomogram for HCC patients that incorporated multiple clinical factors and risk scores and validated its accuracy using a ROC curve (risk, AUC=0.736). The results demonstrated that risk score and stage were important factors for HCC prognosis, and the ROC curve confirmed the model’s accuracy (Figure 5C, 5D).

RISK MODEL AND IMMUNE INFILTRATION:

In terms of the risk model and immune infiltration, we utilized the TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, MCPCOUNTER, XCELL, and EPIC methods to evaluate the relative proportions of infiltrating immune cells. The results showed that the high-risk group had higher levels of immune cell infiltration (Figure 6A). Additionally, we compared the differences in immune cell composition and function between the 2 sample groups (Figure 6B, 6C). The results indicated that the high-risk group had higher levels of immune cell infiltration, immune function scores, and expression of immune checkpoint genes, suggesting a better response to immunotherapy.

ANXA5 AND IMMUNE CELLS IN HCC:

We identified 11 lncRNAs used to construct the risk model, of which 4 showed a high correlation with ANXA5, suggesting its crucial role in HCC (Figure 7A). To investigate the relationship between ANXA5, immune cells, and patient prognosis in HCC, we analyzed single-cell sequencing data. Based on CD45 expression, we classified cells into immune and non-immune cells and extracted immune cells to annotate ARGs (Figure 7B–7D). ANXA5 showed the highest expression level among the 9 ARGs related to the risk model in single cells, primarily in monocytes, as indicated by FCGR3A and CD14 (Figure 8A, 8B).

RELATIONSHIP BETWEEN ANXA5 AND IMMUNE CELL INFILTRATION IN HCC:

We quantified the level of monocyte infiltration using the ssGSEA method and analyzed the impact of ANXA5 and monocytes on HCC patient prognosis. We found that higher levels of monocyte infiltration were associated with better prognosis, while increased ANXA5 expression was associated with worse prognosis (P<0.05; Figure 9A, 9B). ANXA5 and monocyte infiltration may have a synergistic effect on HCC prognosis (P<0.001; Figure 9C). In the TIMER database, ANXA5 was negatively correlated with CD14 (r=−0.152, P=3.35×10−3) but positively correlated with FCGR3A (r=0.493, P=4.29×10−24). ANXA5 was also negatively correlated with tumor purity (r=−0.322, P=8.95×10−10) but positively correlated with monocytes (r=0.548, P=1.93×10−28; Figure 9D).

Discussion

HCC is a prevalent malignancy worldwide with high mortality rates. In recent years, targeted agents, such as sorafenib, lenvatinib, and regorafenib, have shown promising benefits in treating metastatic or unresectable HCC. Despite advancements in therapy, the overall survival of patients with advanced HCC remains unsatisfactory [55]. However, immunotherapy has emerged as a promising treatment option for inhibiting tumor progression and even preventing relapse [56–58]. The identification of reliable and effective biomarkers for HCC prognosis is of the utmost importance. To provide insights for personalized treatment of HCC patients, we conducted a comprehensive investigation of the relationship between ARGs and immune cells in HCC.

Our initial investigation focused on exploring the association between ARGs and immune cell infiltration in HCC. Our findings revealed that samples with high expression of ARGs exhibited increased levels of immune cell infiltration, including T cells, B cells, and macrophages, and expression of immune checkpoint genes, such as PDCD1. TAMs are associated with poor prognosis in head and neck squamous cell carcinoma. TAMs, derived from inflammatory monocytes, play a crucial role in regulating tumor progression. Generally, TAMs promote tumor progression [59]. The coordinated immune response of regulatory T lymphocytes, helper T lymphocytes, and cytotoxic T lymphocytes significantly influences the progression from chronic liver disease to HCC [60–62]. Particularly, cytotoxic T cells play a crucial role in clearing infections or malignant liver cells [63]. This implies that the high expression of ARGs may promote the prognosis of HCC patients receiving immune therapy by enhancing the secretion of TAMs and cytotoxic T cells. Higher expression of the immune checkpoint PD-1 is significantly correlated with shorter overall survival and later tumor staging in patients with cancer [64]. PD-1 leads to T-cell exhaustion, thereby enhancing the growth of HCC [65]. The lower proportion of PD-1+ lymphocytes is associated with better treatment response in HCC [66]. These immune checkpoints have been linked to tumor progression and clinical outcomes of tumor patients treated with immunotherapy. Increasing evidence suggests that immune checkpoint blockade can be a promising option for inoperable HCC cases [67]. Our results indicate that the expression of ARGs can be a significant factor in predicting the prognosis and potential efficacy of immunotherapy in patients with HCC. This implies that we can predict the expression of relevant immune cells and immune checkpoints based on the expression of ARGs in patients with HCC, and thus choose different treatment modalities, achieving personalized treatment for patients.

In this study, we constructed a risk model for the ARG-related lncRNAs NRAV, MCM3AP-AS1, OTUD6B-AS1, AC026356.1, AC009133.1, DDX11-AS1, AC108463.2, MIR4435-2HG, WARS2-AS1, LINC01094, and HCG18 and examined the relationship between the model and the prognosis and immune cells of patients with HCC. Based on the median risk score, we classified the patients into 2 categories: high risk and low risk. We validated the predictive accuracy of the risk model using ROC curves and plotted a nomogram predicting HCC patient prognosis. The outcomes predicted by the model were consistent with those predicted by the nomogram. Clinicopathological analysis and survival analysis demonstrated that the model was highly sensitive to survival prediction. According to Cox regression, the model identified the prognostic factor of HCC. Utilizing this risk model in clinical practice would be beneficial by conducting risk assessment of patients with HCC based on the expression levels of ARG-related lncRNAs, thus enabling personalized treatment. Patients in the high-risk group, characterized by a robust immune response, could benefit from immunotherapy to enhance their prognosis, while those in the low-risk group could opt for alternative treatments, such as radiotherapy, chemotherapy, or targeted therapy [68].

Furthermore, our exploration of differences in the immune microenvironment between patients at low and high risk, examination of immune checkpoints, TME scores, and assessment of immune status using the ssGSEA method revealed a higher level of immune infiltration in the high-risk group. Research suggests that the level of immune infiltration is highly correlated with the efficacy of immunotherapy [69,70].

Based on the correlation between ARGs and lncRNAs, we identified ANXA5 as a potential immune modulatory factor in HCC. Analysis of single-cell data revealed widespread expression of ANXA5 in immune cells in HCC, suggesting it may serve as a new marker for tumor-associated monocytes. This implies that ANXA5 can promote the occurrence and development of HCC by regulating tumor-associated monocytes. ANXA5 is a member of the annexin family of calcium and phospholipid binding proteins, which binds with high affinity to phosphatidylserine (PS) [71]. Its preferential PS binding property has been utilized to detect cells undergoing apoptosis [72]. Moreover, studies have shown that the binding of ANXA5 to PS+ apoptotic cells can modulate the PS-mediated immunosuppressive clearance, thereby increasing the immunogenicity of apoptotic cells, including irradiated, apoptotic tumor cells [73,74]. Studies have shown that knocking out CD8 TEX-related gene ANXA5 in HCC cell lines, among other cancer phenotypes, significantly inhibits cancer cell proliferation and migration [75]. Given that PS contributes to an immunosuppressive TME, ANXA5 can serve as a potential immune checkpoint inhibitor to enhance the immunogenicity of tumor-antigen specific immunotherapies, especially following cytotoxic chemotherapeutics [76]. Research indicates that administering ANXA5 rescues the immune-suppressive state induced by chemotherapy in the TME. Due to ANXA5’s preferential homing to PS-rich tumor cells in the TME, fusing tumor antigen peptides to ANXA5 significantly enhances its immunogenicity and anti-tumor efficacy when administered after chemotherapy. Additionally, the therapeutic anti-tumor effects of ANXA5 peptide fusion can be further enhanced by administering other immune checkpoint inhibitors [77]. This suggests that targeting ANXA5 is likely to inhibit tumor cell progression by enhancing the immune function of HCC.

Currently, cancer research is advancing toward targeted therapies based on precision medicine guided by tumor mutations, thereby deepening our understanding of genomics, disease mechanisms, and drug development in the field [78]. For instance, combining genome sequencing with multi-omics molecular characterization analysis has accelerated the development of personalized interventions, such as CAR-T immunotherapy [79]. Our study comprehensively analyzed the potential roles of ARGs in HCC, particularly highlighting their significant impact on the tumor immune microenvironment. Importantly, we have identified promising candidates, like ANXA5, with ample evidence indicating its inseparable association with patient prognosis and immunity. These findings alleviate the challenges of clinical translation and provide crucial support for subsequent research endeavors.

To the best of our knowledge, this is the first study to investigate the combination of anoikis- and immune-related genes as a prognostic signature for HCC. However, our study has some limitations. First, the results need to be further validated with clinical samples and experimental data. Second, the biological function and mechanism of ANAX5 in HCC progression require further exploration. We plan to implant HCC cell lines with ANXA5 knockdown and normal HCC cell lines separately into mice to observe the potential effects of ANXA5 alteration on tumors in vivo. Third, while the analytical methods we used are reliable, the emergence of new analysis techniques with advancing technology can impose temporal limitations on data analysis.

Conclusions

In this study, we explored the correlation between ARGs and immune cell infiltration in HCC, leading to the development of a novel prognostic model based on 11 ARG-related lncRNAs. Notably, ANXA5, a key ARG, emerged as a significant player influencing patient prognosis and immune cell infiltration in HCC. Acknowledging the hypothesis-generating nature of our work, we believe these findings suggest a promising direction for further research, indicating the potential of ANXA5 as a novel target for HCC treatment.

Figures

The detailed flow chart of this study.Figure 1. The detailed flow chart of this study. Relationship between anoikis-related genes (ARGs) and hepatocellular carcinoma (HCC) immune cells and immune function(A) Boxplot showing the difference in immune cell infiltration level between the high and low ARGs score groups. (B) Boxplot showing the difference in immune function between the high and low ARGs score groups. (C) Correlation between ARGs expression and immune cell infiltration and immune function in HCC samples. (* P<0.05, ** P<0.01, *** P<0.001, ns: no significance).Figure 2. Relationship between anoikis-related genes (ARGs) and hepatocellular carcinoma (HCC) immune cells and immune function(A) Boxplot showing the difference in immune cell infiltration level between the high and low ARGs score groups. (B) Boxplot showing the difference in immune function between the high and low ARGs score groups. (C) Correlation between ARGs expression and immune cell infiltration and immune function in HCC samples. (* P<0.05, ** P<0.01, *** P<0.001, ns: no significance). The relationship between anoikis-related genes (ARGs) score and immune cell infiltration in hepatocellular carcinoma (HCC) samples(A) Box plot of differences in immune checkpoints between high ARGs score group and low ARGs score groups. (B–D) The differences in StromalScore, ImmuneScore, and ESTIMATEScore between the high and low ARGs score groups. (* P<0.05, ** P<0.01, *** P<0.001, ns – no significance).Figure 3. The relationship between anoikis-related genes (ARGs) score and immune cell infiltration in hepatocellular carcinoma (HCC) samples(A) Box plot of differences in immune checkpoints between high ARGs score group and low ARGs score groups. (B–D) The differences in StromalScore, ImmuneScore, and ESTIMATEScore between the high and low ARGs score groups. (* P<0.05, ** P<0.01, *** P<0.001, ns – no significance). Construction of the prognostic model of anoikis-related genes (ARG-related lncRNAs)(A) Heat map of the expression levels of 11 long non-coding RNAs (lncRNAs) in high-risk group and low-risk group samples for constructing the prognosis model. (B) Survival curves of high-risk group samples and low-risk group samples. (C) ROC curve to show the accuracy of the model.Figure 4. Construction of the prognostic model of anoikis-related genes (ARG-related lncRNAs)(A) Heat map of the expression levels of 11 long non-coding RNAs (lncRNAs) in high-risk group and low-risk group samples for constructing the prognosis model. (B) Survival curves of high-risk group samples and low-risk group samples. (C) ROC curve to show the accuracy of the model. Evaluation of the prognostic model(A, B) Univariate and multivariate COX regression analyzes to study the relationship between risk score and patient outcome. (C) A nomogram was drawn to show the relationship between the risk model and patient outcomes. (D) ROC curve to evaluate the accuracy of each indicator in the nomogram model.Figure 5. Evaluation of the prognostic model(A, B) Univariate and multivariate COX regression analyzes to study the relationship between risk score and patient outcome. (C) A nomogram was drawn to show the relationship between the risk model and patient outcomes. (D) ROC curve to evaluate the accuracy of each indicator in the nomogram model. Relationship between risk model and immune infiltration(A) TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, MCPCOUNTER, XCELL, and EPIC methods to evaluate the relative proportions of infiltrating immune cells. (B) Differences in immune function between high-risk and low-risk samples. (C) Differences in expression levels of immune checkpoints between high-risk and low-risk samples. (* P<0.05, ** P<0.01, *** P<0.001, ns – no significance)Figure 6. Relationship between risk model and immune infiltration(A) TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, MCPCOUNTER, XCELL, and EPIC methods to evaluate the relative proportions of infiltrating immune cells. (B) Differences in immune function between high-risk and low-risk samples. (C) Differences in expression levels of immune checkpoints between high-risk and low-risk samples. (* P<0.05, ** P<0.01, *** P<0.001, ns – no significance) ANXA5 and immune cells in hepatocellular carcinoma (HCC)(A) Mulberry plot to show the relationship of the 9 anoikis-related genes (ARGs) with the 11 long non-coding RNAs (lncRNAs) that constitute the risk model. (B–D) Annotated map of immune cell clustering.Figure 7. ANXA5 and immune cells in hepatocellular carcinoma (HCC)(A) Mulberry plot to show the relationship of the 9 anoikis-related genes (ARGs) with the 11 long non-coding RNAs (lncRNAs) that constitute the risk model. (B–D) Annotated map of immune cell clustering. ANXA5 and monocytes(A) Bubble plot of the expression of 9 anoikis-related genes (ARGs) in different classes of immune cells associated with the risk model. (B) Bubble plot of expression levels of ANXA5 and monocyte markers FCGR3A and CD14.Figure 8. ANXA5 and monocytes(A) Bubble plot of the expression of 9 anoikis-related genes (ARGs) in different classes of immune cells associated with the risk model. (B) Bubble plot of expression levels of ANXA5 and monocyte markers FCGR3A and CD14. The relationship between ANXA5 and immune cell infiltration in hepatocellular carcinoma (HCC)(A, B) Survival curves of patient prognosis in monocytes and ANXA5 expression levels in HCC samples. (C) The relationship between mononuclear cell infiltration combined with ANXA5 expression level in HCC samples and patient prognosis. (D) Scatter plot of the correlation between ANXA5 and B cells, CD8 T cells, CD4 T cells, macrophages, neutrophils, and dendritic cells.Figure 9. The relationship between ANXA5 and immune cell infiltration in hepatocellular carcinoma (HCC)(A, B) Survival curves of patient prognosis in monocytes and ANXA5 expression levels in HCC samples. (C) The relationship between mononuclear cell infiltration combined with ANXA5 expression level in HCC samples and patient prognosis. (D) Scatter plot of the correlation between ANXA5 and B cells, CD8 T cells, CD4 T cells, macrophages, neutrophils, and dendritic cells.

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Figures

Figure 1. The detailed flow chart of this study.Figure 2. Relationship between anoikis-related genes (ARGs) and hepatocellular carcinoma (HCC) immune cells and immune function(A) Boxplot showing the difference in immune cell infiltration level between the high and low ARGs score groups. (B) Boxplot showing the difference in immune function between the high and low ARGs score groups. (C) Correlation between ARGs expression and immune cell infiltration and immune function in HCC samples. (* P<0.05, ** P<0.01, *** P<0.001, ns: no significance).Figure 3. The relationship between anoikis-related genes (ARGs) score and immune cell infiltration in hepatocellular carcinoma (HCC) samples(A) Box plot of differences in immune checkpoints between high ARGs score group and low ARGs score groups. (B–D) The differences in StromalScore, ImmuneScore, and ESTIMATEScore between the high and low ARGs score groups. (* P<0.05, ** P<0.01, *** P<0.001, ns – no significance).Figure 4. Construction of the prognostic model of anoikis-related genes (ARG-related lncRNAs)(A) Heat map of the expression levels of 11 long non-coding RNAs (lncRNAs) in high-risk group and low-risk group samples for constructing the prognosis model. (B) Survival curves of high-risk group samples and low-risk group samples. (C) ROC curve to show the accuracy of the model.Figure 5. Evaluation of the prognostic model(A, B) Univariate and multivariate COX regression analyzes to study the relationship between risk score and patient outcome. (C) A nomogram was drawn to show the relationship between the risk model and patient outcomes. (D) ROC curve to evaluate the accuracy of each indicator in the nomogram model.Figure 6. Relationship between risk model and immune infiltration(A) TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, MCPCOUNTER, XCELL, and EPIC methods to evaluate the relative proportions of infiltrating immune cells. (B) Differences in immune function between high-risk and low-risk samples. (C) Differences in expression levels of immune checkpoints between high-risk and low-risk samples. (* P<0.05, ** P<0.01, *** P<0.001, ns – no significance)Figure 7. ANXA5 and immune cells in hepatocellular carcinoma (HCC)(A) Mulberry plot to show the relationship of the 9 anoikis-related genes (ARGs) with the 11 long non-coding RNAs (lncRNAs) that constitute the risk model. (B–D) Annotated map of immune cell clustering.Figure 8. ANXA5 and monocytes(A) Bubble plot of the expression of 9 anoikis-related genes (ARGs) in different classes of immune cells associated with the risk model. (B) Bubble plot of expression levels of ANXA5 and monocyte markers FCGR3A and CD14.Figure 9. The relationship between ANXA5 and immune cell infiltration in hepatocellular carcinoma (HCC)(A, B) Survival curves of patient prognosis in monocytes and ANXA5 expression levels in HCC samples. (C) The relationship between mononuclear cell infiltration combined with ANXA5 expression level in HCC samples and patient prognosis. (D) Scatter plot of the correlation between ANXA5 and B cells, CD8 T cells, CD4 T cells, macrophages, neutrophils, and dendritic cells.

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