LSCC patients datasets were downloaded from TCGA datasets (https://portal.gdc.cancer.gov/), included 111 tumor and 12 nontumor specimens. The expressing data of mRNAs were analyzed by th the limma package for R software. The R package “survival” was performed to investigate the relationships between LSCC patients and the survival data. All original data are downloaded from TCGA datasets. The experimental study was approved by the Ethics Committee of the Second Affiliated Hospital of Nantong University.

We used Cell Type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) way to qualify and quantify 22 different types of immune cells in tissues (https://cibersort.stanford.edu/). This method mostly depends on a leukocyte gene signature matrix, called LM22.

Tumor Immune Estimation Resource (TIMER) was applied to understand a comprehensive profile of tumor immune cells of pan-cancer (cistrome.dfci.harvard.edu/TIMER/). We evaluated six immune infiltrates (neutrophils, macrophages, dendritic cells, B-cells, CD4+ T-cells and CD8+ T-cells) in HNSCC by utilizing this website server. Several modules were mainly provided on this website, containing Gene, Survival, Mutation, SCNA, Diff Exp, Correlation, and Estimation. We investigated correlation between immune cell infiltration and survival, EPHX4 copy numbers, EPHX4 mRNA expression levels, and individual immune cell infiltration.

The relationship between immunomodulators and EPHX4 expression were explored from a web portal TISIDB (http://cis.hku.hk/TISIDB/) to explore tumor-immune system interactions. We analyzed immunostimulators and immunoinhibitors that were significantly associated with EPHX4 expression (Spearman correlation test, p < 0.05). The result of protein network was then subjected to GO annotation and KEGG pathway enrichment analysis utilizing web-based tools (https://string-db.org/) and WEB-based Gene SeT Analysis Toolkit (http://www.webgestalt.org/).

One human laryngeal cancer cell line-HN6, were obtained from Typical Culture Cell Bank, Chinese Academy of Sciences (Shanghai, China), and cultured in DMEM supplement with 10% FBS (Thermo Fisher Scientific) and 1% penicillin-streptomycin.

Cells were lysed with ice-cold RIPA buffer supplemented containing 125 mM Tris-HCl (Ph 6.8), 4% SDS, 20% glycerol, and 0.004% bromphenol blue supplemented with complete protease cocktail. Protein concentrations were measured with a BCA protein assay kit (Pierce). Proteins in the lysates were separated by SDS-PAGE and immunoblotted.

Total RNA was isolated with TRIzol reagent (Takara), and cDNA was then produced with a RevertAid First Strand cDNA Synthesis (Roche) and served as template for real-time PCR using an SYBR Green-based qRT-PCR system according to the manufacturer’s protocol. Elative RNA levels of indicated genes were determined using the comparative 2−ΔΔCT method and normalized to GAPDH.

RT-PCR primers used:

EPHX4 (forward: 5’--3’: TACGTGCGGATCAAGGATTCA, reverse: 5’--3’: GGTAACGCCAAGAATACCAGAA)

GADPH (forward: 5’--3’: GGAGCGAGATCCCTCCAAAAT, reverse: 5’--3’: GGCTGTTGTCATACTTCTCATGG).

1 × 103 cells were plated into a new 96-well plate. The cell viability was detected by CCK-8 assay kit (Pierce) following the manufacturer’s protocols at indicated timepoints after seeding. The results were analyzed by GraphPad Prism 8.0 tool.

HN6 or stable cell lines were obtained with 0.25% trypsin, counted, and seeded into a new 6-well dish at 2 × 103 cells/dish. After 10 days, the colonies were calculated after fixed with paraformaldehyde (4%), followed by staining with 1% crystal violet and imaging.

HN6 or stable cell lines were obtained and seeded into new 6-well plates, The next day, cells were mechanically scratched. We got images at the same wounded region at the indicated timepoints after wounding, and the percentage of wound closure was determined by Image J software.

We conducted IHC assays based on the standard protocol previously.14 Cancer samples from LSCC patients were sectioned into 4-μm thick slides for the detection into of EPHX4 expression. Staining was conducted following to standard protocol.

An immunoreactive intensity score was calculated by multiplying the staining value with the percentage category value, and finally the average score was calculated from scores of the three independent pathologists.

The proper statistical analyses that were conducted using SPSS and Graph Prime 8.0 software. Unpaired Student t tests was performed to compare between two groups, statistical significance was determined as indicated in the figure legend. p < 0.05 was considered significant; *p < 0.05; **p < 0.01; ***p < 0.001; Kaplan-Meier plots with log-rank test were used to analyze overall survival data.

To explore the possible function of EPHX4 in LSCC, we found that the mRNA expression level of EPHX4 was significantly upregulated in LSCC compared with normal tissues (Fig. 1A). Survival analysis based on TCGA datasets revealed that LSCC patients with low EPHX4 displayed a longer overall survival than those with high EPHX4 expression (p = 0.003) (Fig. 1B).

Figure 1.

The expression of EPHX4 in laryngeal cancer and its clinical significance. (A) The significant upregulation of EPHX4 was observed in laryngeal cancer. (B) Survival assays based on TCGA datasets. A poor prognosis was observed in laryngeal cancer by TCGA datasets with high EPHX4 expression.

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To delve into the association of EPHX4 levels with the pattern of immune cells, we extracted and processed the signature gene profile by the use of CIBERSORT method. After removing the samples with p ≥ 0.05, the landscape of the infiltrating immune cells in cancerous and nontumor specimens for TCGA LSCC cohorts is showed in Fig. 2. When compared to normal tissues, the proportions of T cells CD4 naive and macrophages M1 were significantly increased, while B-cells naive, T-cells CD4 memory resting, monocytes, mast cells resting and eosinophils in LSCC decreased. In comparison to normal specimens, different patterns of the infiltrating immune cells in TCGA LSCC cohorts are shown in Fig. 3A. Moreover, in TCGA LSCC cohorts, different correlations pattern among the immune cells were found (Fig. 3B). Our results demonstrated that EPHX4 could regulate the immune activity of LSCC microenvironment.

Figure 2.

Evaluation of the proportions of 22 types of immune cell infiltration in TCGA cohorts.

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Figure 3.

Associations between EPHX4 gene and immune cell infiltration levels. (A) Heatmaps and violin plots showed the differences in the immune cell distribution between malignant (red) and normal (blue) tissue in laryngeal cancer cohorts. (B) The bar chart summarized the percentage of 22 infiltrated immune cells from laryngeal cancer and normal tissues.

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We further conducted correlation analysis and found 59 genes significantly related to the expression of EPHX4. Moreover, we conducted GO assays, and Fig. 4A lists the top 10 of each aspect. The enriched BP (Biological Process) was involved in cell chemotaxis, response topologically incorrect protein, positive regulation of regulated secretory pathway, positive regulation of exocytosis, ER-associated misfolded protein catabolic process, endoplasmic reticulum mannose trimming, retinal ganglion cell axon guidance, protein demannosylation protein, alpha-1,2-demannosylation, cellular response to misfolded protein. The enriched CC (Cellular Components) involved secretory granule membrane, ficolin-1-rich granule membrane, tertiary granule, endoplasmic reticulum quality control compartment, immunological synapse, cornified envelope, intrinsic component of synaptic vesicle membrane, tertiary granule membrane, specific granule membrane. The enriched MF (Molecular Function) involved unfolded protein binding, protein tyrosine kinase activity, UDP-glucosyltransferase activity, transmembrane-ephrin receptor activity, glucosyltransferase activity, ephrin receptor activity, ephrin receptor binding, transmembrane receptor protein tyrosine kinase activity, amyloid-beta binding, transmembrane receptor protein kinase activity. We also found five KEGG pathways related to the dysregulation of EPHX4 mRNA expression, which comprised with protein processing in endoplasmic reticulum, amoebiasis, viral protein interaction with cytokine and cytokine receptor, lysosome and natural killer cell mediated cytotoxicity (Fig. 4B).

Figure 4.

Significant (A) GO and KEGG pathway (B) analysis of the EPHX4 coexpression genes.

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Next, we evaluated the correlation between the expression levels of EPHX4 and immune cell infiltration using TISIDB (http://cis.hku.hk/TISIDB/) database, which showed the relationship between EPHX4 expression and abundance of 28 TILs in different types of cancer. Moreover, we found that the expression of EPHX4 is more closely related to the abundance of Mast, CD56bright, CD56dim, Tem_CD8, Mem_B, Neutrophil, NKT, pDC, NK, Th1, Act_CD_8, Macrophage, Treg, Act_DC in HNSCC (Fig. 5).

Figure 5.

Correlation between EPHX4 expression levels and immune cell subsets. The red and black asterisks in the correlation heatmap indicated various immune cell types significantly associated with EPHX4 expression levels in laryngeal cancer cohorts. The dot plots showed the correlations between EPHX4 expression levels and immune cell subsets in laryngeal cancer cohorts.

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To further investigate the association EPHX4 expression with immunomodulators, we first studied the relationship between EPHX4 expression and immunoinhibitors among various human cancer types. The results showed that EPHX4 expression was obviously associated with immunoinhibitors in different kinds of cancer. The significant association between EPHX4 and immunoinhibitors in HNSCC was also found. Then, we analyzed the association between EPHX4 expression and immunostimulators and MHC molecule. The results showed that EPHX4 expression correlated obviously with parts of immunostimulators and MHC molecule in HNSCC (Fig. 6).

Figure 6.

Correlation between EPHX4 expression levels and immunomodulators. The red and black asterisks in the correlation heatmap indicated different immunomodulators significantly associated with EPHX4 expression levels in laryngeal cancer cohorts. The dot plots showed the correlations between EPHX4 expression levels and immunomodulators in laryngeal cancer cohorts.

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We also explored the signaling molecular pathways by which EPHX4 might modulate the immune response in HNSCC. We identified immunostimulators (VTCN1, TGFB1, CDC244, LAG3, PDCD1, TIGIT, IDO1, CD274, ADORA2A, KDR) and immunoinhibitors (TNFSF13, TNFSF4, KLRC1, TNFRSF25, IL2RA, CXCR4, ENTPD1, ULBP1, RAET1E, KLRK1, CD276, TMIGD2, IL6, CXCL12, C10ORF54, NT5E, TNFRSF4, TNFRSF13C, CD40, TMEM173) significantly associated with EPHX4 in HNSCC. we analyzed protein-protein network of 30-associated immunomodulators produced by the STRING online server (Fig. 7A). GO was used to annotate these 30 genes and KEGG pathway enrichment analysis of these gene showed that cytokine-cytokine receptor interaction, intestinal immune network for IgA production, cell adhesion molecules, natural killer cell mediated cytotoxicity, rheumatoid arthritis, Malaria signaling pathway were associated with EPHX4-mediated tumor-immune microenvironment (Fig. 7B,C).

Figure 7.

Analysis of immunomodulators associated with EPHX4 expression levels. (A) Protein-protein network of 30-associated immunomodulators in laryngeal cancer, produced by the STRING online server. (B) Gene Ontology annotation of 30-associated immunomodulators in laryngeal cancer. (C) Kyoto Encyclopedia of Genes and Genomes pathway analysis of the above those 30 genes.

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To specify the role of EPHX4 gene in LSCC, we then constructed a shRNA-EPHX4 and EPHX4 vector to knockdown and overexpression of EPHX4 respectively in laryngeal cell line (HN6 cell line). Their knockdown and overexpression of efficacy was evaluated by western blotting (Fig. 8A). As determined by CCK8 experiments, EPHX4 knockdown inhibited cell proliferation, while re-expresssion-EPHX4 reversed effects (Fig. 8B). EPHX4 knockdown suppressed colony formation ability, while EPHX4 re-expresssion-EPHX4 also rescued effects (Fig. 8C).

Figure 8.

EPHX4 promoted the proliferation, migration of laryngeal cancer cell line. (A) Immunoblotting of normal control (NC) versus EPHX4 knowdown with sh#1, sh#2, and vector versus EPHX4-overexpressing (EPHX4) efficiencies in HN6 cell line. (B) CCK-8 assay was performed in NC and EPHX4 knockdown and overexpression in HN6 cell line. (C) Clonogenic assays of NC and EPHX4 knockdown and overexpression HN6 cell line were recorded (left) and quantitatively analyzed (right). (D) Wound healing assays of EPHX4 knockdown and overexpression in HN6 cell line were recorded. (E) Left: Representative IHC images of EPHX4 protein levels in LSCC tumors and in paired normal tissues.Scale bar:100 uM. Right: EPHX4 protein levels were significantly lower in ESCC tumors than in paired normal tissues (n = 12).

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Cancer cell migration plays a key role in cancer progression. To explore the role of EPHX4 in migration, a cell scratch assay was conducted to investigate the horizontal migration ability. Our results showed that migration was inhibited in EPHX4 knockdown cell line, while EPHX4 re-expresssion-EPHX4 rescued. horizontal migration ability of HN6 cell line (Fig. 8D). To explore the expression pattern of EPHX4, we performed an IHC staining to analyze its expression in LSCC tumor tissues, EPHX4 protein was expressed at significantly higher levels in LSCC tumor tissues than in paired normal tissues (Fig. 8E).