Integrated analysis of the prognostic and immunotherapeutic significance of ZEB1-AS1 in hepatocellular carcinoma

Introduction

Hepatocellular carcinoma (HCC) is the sixth most common tumor and the third most frequent cause of cancer-related deaths, placing a heavy burden on healthcare systems worldwide (1,2). Since no evident symptoms appear in the early stage of HCC, most patients are already in the advanced stage when diagnosed, resulting in poor prognosis and high recurrence rate (3). HCC has a complex metabolomic, epigenomic and genomic profile that is challenging for an effective treatment (4). Currently, molecular targeted therapy and immunotherapy including those using multi-tyrosine kinase inhibitors and immune checkpoint blockers have achieved certain results (5,6). However, the monitoring of the prognosis and selection of the treatment options for patients with HCC still need further exploration. Therefore, the exploration of the key cancer-promoting molecular mechanisms of hepatocarcinogenesis might help identifying specific diagnostic and prognostic biomarkers to provide new perspectives for patient stratification and treatment guidance.

Long non-coding RNAs (lncRNAs) are members of the non-coding RNA family and play an essential role in the regulation of specific cellular responses (7,8). Increasing evidence supports that lncRNAs influences tumor progression by working as tumor suppressors or oncogenic drivers in human cancers (9). It has also been shown that lncRNA expression in tumor and immune cells is closely related to the immunotherapy response and tumor immune monitoring (10). Additionally, lncRNAs regulate multiple downstream gene targets and cancer-related pathways, thus guiding the formulation of drugs for an effective cancer therapy (11). Currently, various lncRNAs predict cancer risk and are potential specific prognostic biomarkers and therapeutic targets for cancer (12,13).

LncRNA zinc finger E-box binding homeobox 1 antisense 1 (ZEB1-AS1), from the promoter region of ZEB1, is a non-coding antisense transcript that functions as an oncogenic regulator in a number of neoplasms (14). Its overexpression is correlated with poor prognosis in multiple cancers, such as gastric cancer (15), HCC, colorectal cancer (CRC) (16), and non-small lung cancer (17). In CRC, ZEB1-AS1 may serve as an independent prognostic factor for patients with CRC and is significantly associated with response to immunotherapy (18). However, relatively few studies are available on the association between ZEB1-AS1 and HCC, and no studies reported the relevance of ZEB1-AS1 for the immunotherapy against HCC. Firstly, this work investigated the relation between ZEB1-AS1 expression and survival and clinicopathological features of HCC patients based on public databases. The differential expression of ZEB1-AS1 in normal human hepatocytes (LO2) and human HCC cell lines (SMMC-7721, HepG2, Huh-7) was detected by quantitative real-time polymerase chain reaction (qRT-PCR). Secondly, univariate and multivariate Cox analyses were employed to explore whether ZEB1-AS1 acted as an independent prognostic factor for patients with HCC. And based on the Cox regression analysis results, nomogram survival prediction maps were constructed. Furthermore, we investigated the relation between ZEB1-AS1 expression and tumor microenvironment (TME), immune cell infiltration (ICI), immune checkpoint inhibitors, immunotherapy response and drug sensitivity to elucidate the correlation between ZEB1-AS1 expression and HCC immunity. Finally, we performed a functional enrichment analysis based on the ZEB1-AS1 differentially expressed genes (DEGs) to initially explore the potential mechanism of action of ZEB1-AS1. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-aw-2446/rc).

Methods

Data acquisition

RNA sequences and clinical information of 374 HCC tissue samples and 50 normal tissue samples were obtained from The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer) database. Moreover, the FPKM transcriptome expression of 33 tumors and methylation data of HCC were obtained from UCSC Xena (https://xena.ucsc.edu/, based on TCGA database). Since the data for this study were obtained from databases that are open to the public, no approval from the local ethics committee was required. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Expression of ZEB1-AS1 and clinicopathological analysis

The differential expression of ZEB1-AS1 in different tumor types was evaluated using the “Wilcox.test” method and the results were visualized as box plots using the R-package “ggpubr”. The differential expression of ZEB1-AS1 in HCC was analyzed using the R-package “limma” and “beeswarm”. Then, HCC tumor and normal tissues were paired and the differential expression of ZEB1-AS1 after pairing was investigated. The correlation between ZEB1-AS1 expression and clinical parameters was illustrated using the R package “ggpubr”.

Cell culture and qRT-PCR

Human HCC cell lines (SMMC-7721, HepG2, Huh-7), normal human liver cells (LO2) were obtained from the American Type Culture Collection (ATCC, Virginia, USA). HepG2 and Huh-7 cells were grown in DMEM (Gibco, New York, USA), while SMMC-7721 and LO2 cells were cultured in RPMI-1640 (Gibco). All media were supplemented with 10% fetal bovine serum (FBS, Gibco) and 1% penicillin-streptomycin (Gibco). All four cell lines were cultured in an incubator at 37 °C and 5% CO₂. Rapid RNA extraction kit (Mei5 Biotechnology, Beijing, China) was used to extract total RNA from cells, followed by concentration determination with NanoDrop 2000 spectrophotometer (Thermo, Massachusetts, USA). The extracted RNA was converted to complementary DNA (cDNA) by reverse transcription kit (Mei5 Biotechnology), and then qRT-PCR was performed using 2X M5 HiPer SYBR Premix EsTaq (Mei5 Biotechnology) and StepOnePlus PCR System (Applied Biosystems, California, USA). Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used to normalize the results. PCR amplification primer sequences were as follows: ZEB1-AS1-fw: 5'-TCCCTGCTAAGCTTCCT TCAGTGT-3'; ZEB1-AS1-rev: 5'-GACAGTGATCACTTTCATATCC-3'; GAPDH-fw: 5'-GGAAGCTTGTCATCAATGGAAATC-3'; GAPDH-rev: 5'-TGATGACC CTTTTGGCTCCC-3'. The relative expression of the genes was calculated by the 2^−ΔΔCt method.

Correlation analysis between ZEB1-AS1 expression and survival prognosis

GEPIA2 [http://gepia2.cancer-pku.cn/, based on TCGA and Genotype-Tissue Expression (GTEx) databases] was employed to verify the expression of ZEB1-AS1 in tumor tissues and normal tissues (cutoff, P<0.05). Subsequently, the correlation of ZEB1-AS1 expression with the overall survival (OS) and disease-free survival (DFS) was plotted as Kaplan-Meier (KM) curve. The association of ZEB1-AS1 expression with OS and progression-free survival (PFS) was explored by the R packages “survival” and “survminer” based on independent TCGA data. Additionally, univariate and multivariate Cox regression analyses were used to explore whether ZEB1-AS1 was an independent prognostic factor for patients with HCC.

Cell transfection

Small interfering RNAs (siRNAs) targeting ZEB1-AS1 and a corresponding negative control were designed and synthesized by GenePharma (Shanghai, China). The sequence of si-ZEB1-AS1 was 5'-GCTGAAGTCTGATGATTTA-3'. HCC cells were seeded into 6-well plates and allowed to adhere overnight. Subsequently, cells were transfected using Lipofectamine 3000 (Invitrogen, USA) according to the manufacturer’s protocol. Total RNA was extracted 24–48 h post-transfection for verification.

Cell Counting Kit-8 (CCK-8) assay

Cell proliferation was assessed by seeding cells into 96-well plates at 3×10³ cells per well. Following incubation for 0, 24, 48, and 72 h, 10 µL of CCK-8 reagent (ApexBio, Texas, USA) was added to each well and incubated for another 2 h. The optical density (OD) at 450 nm was determined using a microplate reader (Thermo Fisher Scientific, Inc., Massachusetts, USA).

Transwell assay

Cell migration and invasion were evaluated using 8 µm pore Transwell inserts (Corning Costar, New York, USA). For the invasion study, membranes were coated with Matrigel (BD Biosciences, New Jersey, USA), while migration assays utilized uncoated membranes. A suspension of 3×10⁴ cells in serum-free medium was loaded into the upper compartment, with 10% FBS-containing medium placed in the bottom chamber. After 24 h, cells remaining on the upper membrane surface were wiped away. The migrated or invasive cells were then fixed with 4% paraformaldehyde and stained with crystal violet. Quantification was performed by counting cells in randomly selected areas across all wells under an Olympus optical microscope at 20× objective magnification.

Wound healing assay

For the scratch assay, transfected cells were grown to 90% confluency in 6-well plates (1×10⁵ cells per well). A 10 µL pipette tip was employed to generate a straight scratch across the cell monolayer. To eliminate detached cells, the wells were rinsed three times with phosphate buffered saline (PBS) before the medium was replaced with serum-free variants. Cell migration into the denuded area was visualized under an inverted microscope at the initial (0 h) and terminal (48 h) time points.

Correlation analysis between ZEB1-AS1 expression and immunity

The R package “estimate” was used to count immune and stromal scores and then the relationship of ZEB1-AS1 expression with the TME was examined. The findings were visualized using the R package “reshape2” and “ggpubr”. Furthermore, the amount of immune cells in each HCC sample was calculated by the CIBERSORT method to explore the correlation of ZEB1-AS1 expression with ICI and then the correlation analysis was performed using the R package “limma” and “reshape2”. The association of ZEB1-AS1 expression with immune checkpoint inhibitor was also explored using the TCGA database. The heatmap was used to visualize the results using the R-package “reshape2” and “RColorBrewer” options. The immunophenoscore (IPS) of HCC patients was downloaded from The Cancer Immunome Atlas (TCIA; https://tcia.at/) database to predict the correlation of ZEB1-AS1 expression with the immunotherapeutic response (19). Then, IPS was compared between the ZEB1-A1 high and low expression groups.

Correlation analysis between ZEB1-AS1 expression and drug sensitivity

The half-maximal inhibitory concentration (IC₅₀) of the inhibitors targeting ZEB1-AS1 in each HCC patient was calculated using the R package “pRRophetic” to predict the chemotherapeutic response and drug sensitivity (20), and the discrepancy between the ZEB1-AS1 high and low expression groups was compared.

Correlation analysis of gene expression

The analysis of ZEB1-AS1 co-expressed genes was performed using the data from the HCC patients in the TCGA database. The filter of the correlation coefficient was 0.6, and the P value was 0.001. The genes with the most significant correlation with ZEB1-AS1 expression were selected and visualized by the R package “circlize” and “corrplot”.

ZEB1-AS1-related gene enrichment analysis

ZEB1-AS1-related DEGs were screened using a cutoff value of |log₂ fold change (FC)| >1, false discovery rate (FDR) <0.05. Then, Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed by the R package “clusterProfiler” using all DEGs to explore the potentially enriched functions and pathways of ZEB1-AS1.

Statistical analysis

Statistical analysis was performed using the R software (version 4.0.5). ZEB1-AS1 expression in various cancers was detected by Wilcox test. Analysis of the interaction of ZEB1-AS1 expression with prognosis in HCC patients was performed by KM plotter and log-rank test. A value of P<0.05 was considered statistically significant.

Results

Expression of ZEB1-AS1 in HCC and normal tissues

ZEB1-AS1 expression in patients with various tumors was evaluated based on the RNA sequence data from 33 TCGA tumors. The findings indicated that ZEB1-AS1 was significantly overexpressed in HCC tissues when compared to normal liver tissues (P<0.001) (Figure 1A,1B). In addition, the variance analysis showed that ZEB1-AS1 expression was remarkably high in stomach adenocarcinoma (STAD), prostate adenocarcinoma (PRAD), pheochromocytoma and paraganglioma (PCPG), lung squamous cell carcinoma (LUSC), lung adenocarcinoma (LUAD), kidney renal papillary cell carcinoma (KIRP), head and neck squamous cell carcinoma (HNSC), glioblastoma multiforme (GBM), esophageal carcinoma (ESCA), colon adenocarcinoma (COAD), cholangiocarcinoma (CHOL), while its expression was significantly low in uterine corpus endometrial carcinoma (UCEC), thyroid carcinoma (THCA), kidney chromophobe (KICH), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), and breast invasive carcinoma (BRCA) (Figure 1A). In addition, 50 pairs of cancerous and normal tissue samples were screened for paired analysis, revealing that ZEB1-AS1 was still significantly overexpressed in HCC samples (P<0.001) (Figure 1C). For further validation of ZEB1-AS1 expression in HCC, we examined the expression level of ZEB1-AS1 in HCC cell lines (SMMC-7721, HepG2, Huh-7) and normal human liver cells (LO2) by qRT-PCR. The results showed that ZEB1-AS1 expression was significantly upregulated in HCC cell lines compared to normal liver cells (Figure 1D).

Figure 1 Expression, clinicopathological parameters, and correlation of ZEB1-AS1 genes in HCC. (A) ZEB1-AS1 expression levels in pan-cancer from TCGA data. (B) The expression of ZEB1-AS1 in HCC. (C) Comparing the ZEB1-AS1 expression in paired normal and tumor. (D) ZEB1-AS1 expression in HCC cell lines. (E) Violin plot of clinical stage of ZEB1-AS1 expression in HCC. (F) Association between ZEB1-AS1 expression and clinicopathological parameters in HCC. *, P<0.05; **, P<0.01; ***, P<0.001. ACC, adrenocortical carcinoma; BLCA, bladder urothelial carcinoma; BRCA, breast invasive carcinoma; CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL, cholangiocarcinoma; COAD, colon adenocarcinoma; DLBC, lymphoid neoplasm diffuse large B-cell lymphoma; ESCA, esophageal carcinoma; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; GBM, glioblastoma multiforme; HCC, hepatocellular carcinoma; HNSC, head and neck squamous cell carcinoma; KICH, kidney chromophobe; KIRC, kidney renal clear cell carcinoma; KIRP, kidney renal papillary cell carcinoma; LAML, acute myeloid leukemia; LGG, brain lower grade glioma; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; MESO, mesothelioma; OV, ovarian serous cystadenocarcinoma; PAAD, pancreatic adenocarcinoma; PCPG, pheochromocytoma and paraganglioma; PRAD, prostate adenocarcinoma; READ, rectum adenocarcinoma; SARC, sarcoma; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; TCGA, The Cancer Genome Atlas; TGCT, testicular germ cell tumors; THCA, thyroid carcinoma; THYM, thymoma; UCEC, uterine corpus endometrial carcinoma; UCS, uterine carcinosarcoma; UVM, uveal melanoma.

Correlation of ZEB1-AS1 expression with clinicopathological parameters

The “Pathological Stage Map” module of GEPIA2 was used to explore the association of ZEB1-AS1 expression with the pathological stage of HCC. The results showed that ZEB1-AS1 overexpression was remarkably correlated with an advanced tumor stage (Figure 1E). Furthermore, the clinical data of HCC in TCGA were combined, and the grouped analysis revealed that ZEB1-AS1 overexpression was remarkably interrelated with a poor tumor differentiation, advanced tumor stage, large tumor size, and positive lymph node metastasis (LNM) in HCC patients (Figure 1F; Table 1).

Survival analysis

Firstly, we used the GEPIA2 database to analyze the relevance of ZEB1-AS1 expression to the survival of HCC patients. The findings found that ZEB1-AS1 overexpression was remarkably correlated with worse OS (P=0.002) (Figure 2A) and DFS (P=0.002) (Figure 2B). Secondly, we explored the relationship of ZEB1-AS1 expression with HCC survival by KM method using the independent TCGA data. As well, the results found that ZEB1-AS1 overexpression was remarkably interrelated with worse OS (P=0.003) (Figure 2C) and PFS (P<0.001) (Figure 2D). Additionally, we employed univariate and multivariate Cox analyses to indicate that ZEB1-AS1 expression and tumor stage were independent prognostic factors for OS in HCC patients (Figure 2E,2F). Furthermore, the constructed nomograms based on these prognostic indicators to estimate the individual survival probabilities at 1-, 3-, and 5-year revealed that these probabilities were 85.3%, 72.0%, and 62.5%, respectively, in HCC patients (Figure 2G). The calibration curves showed a good association of the predicted OS with the observed OS at 1-, 3-, and 5-year (Figure 2H).

Figure 2 Correlation of ZEB1-AS1 expression with survival in HCC patients. (A) OS plot of ZEB1-AS1 in GEPIA2 database. (B) DFS plot of ZEB1-AS1 in GEPIA2 database. (C) OS plot of ZEB1-AS1 based on TCGA database. (D) PFS plot of ZEB1-AS1 based on TCGA database. (E) Univariate survival-related analysis. (F) Multivariate survival-related analysis. (G) A nomogram for predicting OS in HCC patients at 1-, 3-, and 5-year. (H) Nomogram calibration curves for OS prediction at 1-, 3-, and 5-year. **, P<0.01; CI, confidence interval; DFS, disease-free survival; HCC, hepatocellular carcinoma; HR, hazard ratio; OS, overall survival; PFS, progression-free survival; TCGA, The Cancer Genome Atlas.

Effects of knocking down ZEB1-AS1 expression on proliferation, migration and invasion of HCC cells

To investigate whether ZEB1-AS1 modulates HCC progression, we silenced its expression in Huh-7 cells via siRNA transfection (Figure 3A). Functional assays showed that ZEB1-AS1 deficiency suppressed cell growth, as evidenced by CCK-8 results (Figure 3B). Additionally, both migration and invasion were notably impaired following ZEB1-AS1 knockdown, according to transwell and wound healing assays findings (Figure 3C,3D). These data suggest that ZEB1-AS1 is essential for maintaining the aggressive phenotype of HCC cells.

Figure 3 Knockdown of ZEB1-AS1 expression affects the proliferation, migration, and invasion of HCC cells. (A) Validation of transfection efficiency in Huh-7 cells after transfection using qRT-PCR. (B) Proliferation of Huh-7 cells was assessed by CCK-8 assay. (C) Effects of ZEB1-AS1 knockdown on the migration and invasion of Huh-7 cells were evaluated by transwell assays [Crystal violet aqueous solution (0.1%) stained; magnification ×100]. (D) Migration of Huh-7 cells was examined by wound healing assay (magnification ×40). **, P<0.01; ***, P<0.001. si-NC: control group. si-ZEB1-AS1: experimental group.CCK-8, Cell Counting Kit-8; HCC, hepatocellular carcinoma; OD, optical density; qRT-PCR, quantitative real-time polymerase chain reaction.

Correlation of ZEB1-AS1 expression with TME

TME has an important role in the development, progression, and treatment of cancer (21,22). The findings of our study indicated that ZEB1-AS1 was an independent prognostic factor for OS in HCC patients, and further exploration of the interaction of TME with ZEB1-AS1 expression was necessary. The findings demonstrated that ZEB1-AS1 expression was negatively and significantly associated with stromal score, immune score and total score, suggesting that an increase in stromal or immune cells was associated with low ZEB1-AS1 expression (Figure 4A).

Figure 4 Correlation between ZEB1-AS1 expression and immunity. (A) The correlation between ZEB1-AS1 expression and TME. (B-D) Correlation between ZEB1-AS1 and infiltrating levels of immune cells in HCC. (E,F) Correlation analysis between the expression of ZEB1-AS1 and immune checkpoint inhibitors. *, P<0.05; **, P<0.01; ***, P<0.001. HCC, hepatocellular carcinoma; TME, tumor microenvironment.

Correlation of ZEB1-AS1 expression with ICI and immune checkpoints

Several studies showed that the level of ICI is strongly linked to the activity of tumor cells (23,24). It is known that ZEB1-AS1 expression is correlated with the prognosis of HCC patients, but it is not clear whether ZEB1-AS1 aberrant expression affects ICI. The results revealed that ZEB1-AS1 expression was significantly negatively related to activated NK cells, regulatory T cells (Tregs), CD8 T cells, resting mast cells, and significantly positively related to follicular helper T cells and M0 macrophages in patients with HCC (Figure 4B-4D). Additionally, the relationship of ZEB1-AS1 with immune checkpoint inhibitors revealed that ZEB1-AS1 was significantly positively associated with multiple immune checkpoints in HCC, including NRP1, CD276, CD80, VTCN1, HHLA2, TNFSF9, TNFSF15, CD274, CD44, and TNFRSF9. In contrast, ZEB1-AS1 was significantly negatively associated with TMIGD2 (Figure 4E,4F).

Correlation of ZEB1-AS1 expression with the immunotherapeutic response

To further evaluate the association of ZEB1-AS1 expression with the immunotherapeutic response in HCC patients, we analyzed the relation of ZEB1-AS1 expression with IPS using the TCIA database, a TCGA-based comprehensive immunogenomic analysis database. The findings demonstrated that the ZEB1-AS1 high expression group had significantly lower total IPS, IPS for cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) blocker, programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1)/programmed death-ligand 2 (PD-L2) blocker, and CTLA-4 plus PD-1/PD-L1/PD-L2 blocker than the ZEB1-AS1 low expression group (Figure 5A). This predicted a poorer response to CTLA-4 blocker, PD-1/PD-L1/PD-L2 blocker and CTLA-4 plus PD-1/PD-L1/PD-L2 blocker immunotherapy in HCC patients with high ZEB1-AS1 expression.

Figure 5 Correlation of ZEB1-AS1 expression with immunotherapy response and drug sensitivity. (A) The relationship between IPS and ZEB1-AS1 expression in HCC patients. (a) IPS. (b) IPS-CTLA-4. (c) PD-1/PD-L1/PD-L2. (d) CTLA-4 plus PD-1/PD-L1/PD-L2. (B) Correlation of ZEB1-AS1 expression with drug sensitivity. (a) Sorafenib. (b) Gemcitabine. (c) Sunitinib. (d) AKT inhibitor VIII. (e) Paclitaxel. (f) Cyclopamine. (g) AP-24534. (h) Tipifarnib. (i) Etoposide. (j) Vinorelbine. AKT, protein kinase B; CTLA-4, cytotoxic T-lymphocyte-associated protein 4; HCC, hepatocellular carcinoma; IPS, immunophenoscore; PD-1, programmed cell death protein 1; PD-L1, programmed death-ligand 1; PD-L2, programmed death-ligand 2.

Correlation of ZEB1-AS1 expression with drug sensitivity

The R package “pRRophetic” was employed to explore whether ZEB1-AS1 expression has predictive value for drug sensitivity in patients with HCC in TCGA data. We chose sorafenib, gemcitabine, sunitinib, protein kinase B (AKT) inhibitor VIII, paclitaxel, cyclopamine, AP-24534, tipifarnib, etoposide, and vinorelbine to assess chemotherapy response in the high and low ZEB1-AS1 expression groups. The IC₅₀ of all the above chemotherapeutic drugs was found to be remarkably lower in the ZEB1-AS1 high expression group than in the ZEB1-AS1 low expression group, suggesting that the application of these drugs was more beneficial to HCC patients with high ZEB1-AS1 expression (Figure 5B).

Correlation analysis of gene expression

The ZEB1-AS1 co-expression analysis revealed that the top 5 co-expressed genes significantly positively associated with ZEB1-AS1 were COL5A1, ANTXR1, MRC2, COL1A2, THBS2; the top 5 co-expressed genes significantly negatively associated with ZEB1-AS1 were DCXR, DCXR-DT, PCYT2, DHRS4L2, and PEBP1. A complex interaction network was found among ZEB1-AS1 co-expressed genes (Figure 6A).

Figure 6 Gene expression correlation analysis and gene enrichment analysis. (A) Interactions of ZEB1-AS1-coexpressed genes. Red represents a significant positive correlation, and green represents a significant negative correlation. (B) The top 50 DEGs associated with ZEB1-AS1. (C) Histogram presentation of GO enrichment of the DEGs in MF, BP and CC of biology. (D) Bubble chart of GO enrichment of the DEGs in the aspects of MF, BP, CC. (E) Histogram presentation of top 20 significant pathways related to the DEGs by the KEGG database analysis. (F) Bubble chart of top 20 significant pathways. BP, biological process; CC, cellular component; DEGs, differentially expressed genes; FC, fold change; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; MF, molecular function.

ZEB1-AS1-related gene enrichment analysis

To further explore the potential mechanism of ZEB1-AS1 acting HCC, we performed a functional analysis. At first, we identified 1,313 ZEB1-AS1-related DEGs using the TCGA database HCC RNA sequences data (top 50 DEGs are shown in Figure 6B). Then, we performed GO and KEGG analysis using DEGs. The GO enrichment analysis showed that ZEB1-AS1 was mainly involved in neurotransmitter transport, synaptic membrane, and channel activity (Figure 6C,6D). By KEGG pathway analysis, we found that ZEB1-AS1 was remarkably interrelated with the regulation of cAMP signaling pathway, Wnt signaling pathway, and neuroactive ligand-receptor interaction (Figure 6E,6F).

Discussion

The emergence of microarray analysis and second-generation whole genome sequencing technologies, reveals the critical role of lncRNAs in tumors (25). Several lncRNAs can act as oncogenic or tumor suppressor factors and regulate tumorigenesis and progression (26,27). The differential expression of specific lncRNAs serves as a critical biomarker for cancer progression (28,29). Therefore, the exploration of these molecules as cancer biomarkers might provide a reference for cancer diagnosis and treatment. Previous studies revealed that ZEB1-AS1 is overexpressed in a variety of cancers and is an oncogenic lncRNA (14,30,31). It has effected on tumor proliferation, migration, and invasion by affecting the cell cycle, regulating signaling pathways, and triggering epithelial-mesenchymal transition (32-34). Several studies demonstrated that ZEB1-AS1 may be a potential prognostic biomarker for human cancers (35,36). However, it is still unclear whether ZEB1-AS1 can be used as a therapeutic target and guide the immunotherapy. Therefore, this study focused on the prognostic value and immunological role of ZEB1-AS1 in HCC for the diagnosis and treatment of HCC.

The expression of ZEB1-AS1 in various tumor tissues and normal tissues was evaluated as first, revealing that the expression of ZEB1-AS1 was high in STAD, PRAD, PCPG, LUSC, LUAD, KIRP, HNSC, GBM, ESCA, COAD, CHOL, and low in UCEC, THCA, KICH, CESC, and BRCA. Then, the differential expression of ZEB1-AS1 in HCC and the correlation with clinicopathological parameters were analyzed, showing that ZEB1-AS1 was overexpressed in HCC, and its overexpression was correlated with poor differentiation grade, advanced tumor stage, large tumor size, and positive LNM. Our findings showed no significant correlation of ZEB1-AS1 expression with distant metastasis (DM), which might be related to the low number of HCC patients with DM in the included sample. Mu et al. demonstrated a remarkable positive correlation between high ZEB1-AS1 expression and DM in patients with HCC (37). In addition, we found that ZEB1-AS1 was significantly highly expressed in HCC cell lines by qRT-PCR. As regards the survival, high ZEB1-AS1 expression was correlated with worse OS, DFS and PFS in HCC patients. Additionally, high ZEB1-AS1 expression and advanced tumor stage were independent risk factors for poor prognosis in patients with HCC. All these findings indicated that ZEB1-AS1 is an oncogene associated with poor prognosis in patients with HCC. Meanwhile, our functional experiments revealed that the genetic ablation of ZEB1-AS1 significantly hindered Huh-7 cells proliferation and compromised their migratory and invasive capabilities. These observations align with the characteristic features of aggressive tumor behavior, suggesting that ZEB1-AS1 is a critical driver of HCC progression.

A study pointed out that 90–95% of cancer cases are attributed to lifestyle and environment (38). Cancer occurrence and progression are the result of the interaction between tumor cells and the TME (22). TME has an essential role in tumor progression and therapeutic response (39). Therefore, the correlation of ZEB1-AS1 expression with TME was investigated, and the results showed a remarkable negative correlation of ZEB1-AS1 expression with stromal score and immune score. This result implied that high ZEB1-AS1 expression was linked to fewer stromal cells and immune cells, partly explaining the poorer survival of HCC patients with high ZEB1-AS1 expression. Furthermore, ICI often has a regulatory role in the proliferation, migration and metastasis of tumor cells (40,41). Thus, the correlation of 22 immune cells with ZEB1-AS1 expression in HCC was explored and the findings revealed that high ZEB1-AS1 expression was associated with fewer activated NK cells, regulatory T cells (Tregs), CD8 T cells, resting mast cells, and with more follicular helper T cells and M0 macrophages. Therefore, it would be meaningful to further investigate the specific mechanisms used by ZEB1-AS1 to regulate these tumor-ICIs in HCC according to these results indicating a significant correlation of ZEB1-AS1 expression with HCC-ICI. Immune checkpoint pathways with immunosuppressive functions can be activated by cancer cells, thereby affecting tumor progression and treatment response (42). Currently, PD-1/PD-L1 and CTLA-4 inhibitors have shown favorable efficacy in tumor treatment (43). Our findings showed that ZEB1-AS1 expression had a significant positive correlation with NRP1, CD276, CD80, VTCN1, HHLA2, TNFSF9, TNFSF15, CD274, CD44, and TNFRSF9 immune checkpoints, as well as a significant negative correlation with TMIGD2. The relationship between these immune checkpoints and HCC cells deserves further exploration. Alternatively, our results showed a correlation between ZEB1-AS1 expression and the response to immunotherapy with CTLA-4 and PD1/PD-L1/PD-L2 blockers. ZEB1-AS1 overexpression might predict a poorer response to immunotherapy. These findings indicated that ZEB1-AS1 expression had a strong relationship with HCC immune response.

Regarding the potential mechanism of action of ZEB1-AS1, one study found that ZEB1-AS1 promotes HCC brain metastasis by targeting miR-302b to activate the PI3K-AKT pathway and increase EGFR expression (44). Mu et al. found that ZEB1-AS1 targets miR-299-3p/E2F1 axis to promote HCC cell proliferation and metastasis (37). By co-expression analysis, we identified COL5A1, ANTXR1, MRC2, COL1A2, THBS2, DCXR, DCXR-DT, PCYT2, DHRS4L2, and PEBP1 as co-expressed genes of ZEB1-AS1. Most of these genes are correlated with the prognosis of HCC patients. For example, Li et al. found that patients with high COL1A2 expression have poor DFS and OS (45); high MRC2 expression is associated with the poor prognosis of HCC after hepatectomy (46); and DCXR low expression predicts the poor prognosis in patients with HCC (47). Moreover, ZEB1-AS1 is implicated in the regulation of tumor-associated signaling pathways, such as the Wnt signaling pathway and cAMP signaling pathway, by KEGG pathway analysis. Zhan et al. suggested that Wnt signaling plays a key role in the regulation of developmental and stemness cascades and is closely related to cancer (48). The immune dysfunction and cellular metabolism disorders caused by some cancers are linked to the dysregulation of cAMP signaling system, and cAMP is one of the important cancer-related pathways (49).

Although the prognostic and immunological value of ZEB1-AS1 in HCC was demonstrated from various aspects, some limitations remain. Firstly, it should be noted that while our bioinformatic analysis and in vitro experiments provide compelling evidence, these findings remain preliminary and somewhat speculative. The clinical significance of ZEB1-AS1 as a prognostic marker in HCC warrants further validation in a larger cohort of clinical specimens, particularly comparing HCC tissues with their respective peritumoral counterparts. Secondly, additional proofs are necessary to demonstrated that ZEB1-AS1 influences the progression of HCC through the immune pathway, although our results showed that ZEB1-AS1 expression was associated with HCC immunity. Additionally, the relationship of ZEB1-AS1 with the immunotherapeutic response and chemotherapeutic response was based on database exploration, and more independent immunotherapy cohorts are necessary to confirm these results.

Conclusions

In conclusion, our study provides a novel direction and a theoretical framework for understanding the role of ZEB1-AS1 in HCC. While ZEB1-AS1 represents a potential theoretical target, its practical clinical application requires long-term investigation and validation in substantial patient cohorts.

Acknowledgments

The authors thank the Key Laboratory of Molecular Diagnostics and Precision Medicine for Surgical Oncology in Gansu Province for their help.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-aw-2446/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-aw-2446/prf

Funding: This study was supported by grants from Gansu Province Health Industry Project (No. GSWSKY2022-36); Lanzhou Science and Technology Planning Project (No. 2022-ZD-44); Natural Science Foundation of Gansu Province (No. 23JRRA1313); and Natural Science Foundation for Young Scientists and the Science & Technology Planning Project of Gansu Province (No. 18JR3RA058); Health Industry Scientific Research Program of Gansu Province (No. GSWSKY2020-06).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-aw-2446/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.

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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