Research ArticleArticles
Open Access

Phosphoglycerate-kinase-1 Is a Potential Prognostic Biomarker in HNSCC and Correlates With Immune Cell Infiltration

PING WANG, YUE-YUE WANG, YANG-LONG XU, CHUN-YU ZHANG, KUN WANG and QIAN WANG
Cancer Genomics & Proteomics December 2023, 20 (6suppl) 723-734; DOI: https://doi.org/10.21873/cgp.20419

Abstract

Background/Aim: Head and neck squamous cell carcinoma (HNSCC) is the sixth leading cancer worldwide, with a high recurrence rate and a low cure rate. Phosphoglycerate kinase 1 (PGK1), an essential enzyme in the aerobic glycolysis pathway, is a prognostic marker for a variety of cancers. However, it remains unclear whether a PGK1-based immune signature can be used as a prognostic biomarker in HNSCC patients. Materials and methods: We explored the potential oncogenic mechanisms of PGK1 by multiple bioinformatics analyses combined with multiple databases, including the correlation between PGK1 and prognosis, and the infiltration of immune cells in HNSCC. Functional enrichment analyses were further performed to investigate the potential role of PGK1 in HNSCC. Results: The expression of PGK1 was significantly higher in HNSCC tissues compared to normal tissues. High expression of PGK1 was associated with poor prognosis in HNSCC, and multivariate cox regression analysis showed that PGK1 could be an independent prognostic factor in HNSCC. Pathway analysis revealed that PGK1 may regulate the pathogenesis of HNSCC through the immune signaling pathway. Moreover, PGK1 expression significantly correlated with the infiltration level of 16 types of immune cells. Conclusion: The current study reports that PGK1 expression was increased in HNSCC and that high PGK1 expression was closely associated with poor prognosis and immune cell infiltration, which could serve as a promising independent prognostic biomarker and potential immunotherapeutic target for HNSCC.

Key Words:

Head and neck squamous cell carcinoma (HNSCC) is the sixth leading cancer worldwide, arising from the oral cavity, pharynx, larynx, and hypopharynx (1). The global mortality rate of HNSCC is ranked 12th, with approximately 840,000 cases in 2020, which is expected to rise to 1 million by 2030 (2). Despite technological advancements in the treatment of HNSCC, its overall survival and prognosis remain poor, with a five-year overall survival rate of 50% (3, 4); a large number of patients die of metastasis, immune suppression and recurrence (5). Therefore, it is urgent to discover more sensitive and specific molecular markers to improve the early diagnosis and targeted treatment of HNSCC.

Notably, cancer cells show extensive enhancement of aerobic glycolysis, including overexpression of glycolytic enzyme and an increase in lactate production (6, 7). Phosphoglycerate kinase 1 (PGK1) is an essential enzyme of aerobic glycolysis (8). It catalyzes the reversible transfer of a phosphate group from 1,3-bisphosphoglycerate to adenosine diphosphate (ADP) and produces 3-phosphoglycerate and adenosine triphosphate (ATP) (9). Previous studies have confirmed that PGK1 was associated with poor prognosis of several types of cancer; it also has a higher expression in tumors than that in the normal tissues (8, 10). In cellular experiments, overexpression of PGK1 promotes tumor cell proliferation, migration and invasion (11, 12). In the nucleus, PGK1 preferentially drives cell metastasis via mitochondrial oxidative phosphorylation induction, whereas cytoplasmic PGK1 preferentially supports proliferation by functioning as a glycolytic enzyme in SMAD4-negative pancreatic ductal adenocarcinoma (12). Concerning its molecular mechanism, the overexpression of PGK1 was correlated with mutations of the tumor suppressor genes tumor suppressor p53 (TP53) and E-cadherin (CDH1), while the acetylation at the K323 site of PGK1 was found to be a key regulatory mechanism for promoting its enzymatic activity and cancer cell metabolism in breast and liver cancer (13, 14). Similarly, the depletion of PGK1 dramatically reduced cancer cell proliferation and tumorigenesis (8, 14). Recently, it has been documented that PGK1 was associated with hypoxia and immune regulation, resulting in the promotion of glycolysis, enhancing stem cell-like properties and epithelial-mesenchymal transition (EMT) by activating protein kinase B (AKT) signaling in oral squamous cell carcinoma (OSCC) (15-17). However, whether the PGK1-based immune signature could serve as a prognostic biomarker for patients with HNSCC remains unclear.

This study identified the expression levels of PGK1 in HNSCC and its correlation with the prognosis of HNSCC patients by applying multiple bioinformatics analyses. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses and Gene set enrichment analyses (GSEA) were applied to investigate the potential function of PGK1 in HNSCC. In addition, we investigated the correlation of PGK1 with different levels of immune cell infiltration and further evaluated the predictive performance of PGK1 for high infiltration levels of HNSCC.

Materials and Methods

Datasets and data preprocessing. We explored the differential expression of PGK1 in HNSCC and normal tissues based on The Cancer Genome Atlas (TCGA) dataset using the Tumor immune estimation resource, version 2.0 (TIMER2.0) online tool (http://timer.cistrome.org/) (18), and Gene expression profiling interactive analysis, version 2.0 (GEPIA2.0) online tool (http://gepia2.cancer-pku.cn) (19). The expression levels of total protein of PGK1 between primary tumor and normal tissues were investigated using the clinical proteomic tumor analysis consortium (CPTAC) dataset in the UALCAN tool (http://ualcan.path.uab.edu/index.html) (20).

Human protein atlas (HPA). Images of immunohistochemistry staining for HNSCC (https://www.proteinatlas.org/ENSG00000102144-PGK1/pathology/head+and+neck+cancer#img) and normal salivary gland tissues (https://www.proteinatlas.org/ENSG00000102144-PGK1/tissue/salivary+gland#img) were collected from the human protein atlas (HPA, HPA045385). HPA applies transcriptome and proteomics to provide different protein atlases, including tissue, cell, and pathology atlases (21).

Correlations between PGK1 and survival in HNSCC. We extracted survival information for each sample in the TCGA database and then selected overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) data for PGK1 in HNSCC to investigate the association between PGK1 expression and the prognosis of HNSCC patients. Kaplan-Meier (KM) and log-rank tests were performed for survival analysis of HNSCC (p<0.05), and survival curves were analyzed using the R packages “survminer v0.4.9” (https://CRAN.R-project.org/package=survminer) and “survival v3.5-7” (https://cran.r-project.org/web/packages/survival).

Identification of the prognostic factors for OS in HNSCC. Cox regression analysis was used to evaluate the prognostic factors. Multivariate Cox analysis and univariate Cox regression were adopted to compare the impact of PGK1 expression on survival along with other clinical features. The median PGK1 expression was regarded as the cut-off value. A p-value <0.05 was considered statistically significant. Moreover, receiver operating characteristic (ROC) analysis was performed to assess the effectiveness of the transcriptional expression of PGK1 in distinguishing HNSCC from healthy samples. The computed area under the curve (AUC) value ranging from 0.5 to 1.0 indicated an excellent predictive value.

PGK1-related gene enrichment analysis. We analyzed the 570 significant differential genes related to the PGK1 gene in HNSCC by Gene Ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. GO functional analysis comprising cellular component (CC), molecular function (MF), and biological process (BP), as well as KEGG pathway analysis, were implemented using the ClusterProfiler v3.6.0 package (22) in R. GSEA was used to enrich 10 significant KEGG pathways. The gene set was permutated 1,000 times for each analysis. An adjusted p-value <0.05 was considered to be statistically significant.

Relationship between PGK1 and immune cell infiltration. We used TCGA data to quantify the relative abundance of tumor-infiltrating immune cells in HNSCC patients using the single-sample gene set enrichment analysis (ssGSEA) algorithm (23) in the R package GSVA (24), containing 24 gene sets labeling different tumor-infiltrating immune cell types [aDC (activated dendritic cells), B cells, CD8 T cells, cytotoxic cells, DC (dendritic cells), eosinophils, immature dendritic cells (iDC), macrophages, mast cells, neutrophils, NK CD56bright cells, NK CD56dim cells, NK cells, plasmacytoid dendritic cells (pDC), T cells, T central memory (Tcm), T effector memory (Tem), T follicular helper (TFH), T gamma delta (Tgd), T helper cells, Type 1 T helper (Th1) cells, Type 17 T helper (Th17) cells, Type 2 T helper (Th2) cells, regulatory T cells (Treg)], to comprehensively study the molecular characteristics of tumor-immune interactions in HNSCC. To examine the association between PGK1 expression and the abundance of tumor-infiltrating immune cells, enrichment scores characterized the degree of infiltration of each type of immune cells in the samples, and p-values were calculated using the Wilcoxon rank-sum and Spearman’s rank correlation tests.

Results

Elevated expression of PGK1 in HNSCC and normal tissues. To explore the possible role of PGK1 in carcinogenesis, we first analyzed the expression of PGK1 in 33 human cancers from the TCGA dataset using the TIMER2.0 online tool. As shown in Figure 1A, PGK1 was significantly overexpressed in 16 cancers compared with normal samples, including bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), stomach adenocarcinoma (STAD), and uterine corpus endometrial carcinoma (UCEC). We further compared the expression of PGK1 in 41 cancer tissues with paired normal tissues, and the result illustrated that PGK1 was significantly overexpressed in tumors (Figure 1B). In addition, ROC curves were used to evaluate the feasibility of distinguishing tumors from normal tissues based on the expression level of PGK1, and the results indicated that PGK1 had a certain accuracy (AUC=0.888) in predicting HNSCC (Figure 1C). Moreover, immunohistochemical staining from HPA also revealed that PGK1 protein was upregulated in HNSCC tissue (Figure 1D).

Figure 1.
Figure 1.

Phosphoglycerate kinase 1 (PGK1) was upregulated and had prognostic value in head and neck squamous cell carcinoma (HNSCC). (A) The expression of the PGK1 gene in HNSCC from the cancer genome atlas (TCGA) data. (B) PGK1 expression levels in HNSCC and matched normal tissues. (C) Receiver operating characteristic (ROC) analysis of PGK1 showed promising discrimination power between tumor and normal tissues. (D) The protein levels of PGK1, based on the Human Protein Atlas.

Association between PGK1 expression and cancer patient survival prognosis. We performed univariate and multivariate Cox analysis of overall survival to explore PGK1 as an independent prognostic factor of HNSCC. In the univariate Cox analysis model, N stage [hazard ratio (HR)=1.376, p=0.029], M stage (HR=4.794, p=0.002), and PGK1 expression (HR=1.76, p<0.001) were significantly associated with OS in HNSCC patients. In the multivariate Cox analysis model, N stage (HR=1.362, p=0.040), M stage (HR=4.179, p=0.005), and PGK1 (HR=1.838, p<0.001) were still relevant to worse prognosis (Table I).

View this table:
Table I.

Univariate and multivariate analysis of clinicopathological factors that correlate with overall survival (OS) of head and neck squamous cell carcinoma (HNSCC) patients.

Furthermore, we investigated the relationship between PGK1 expression and overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) in HNSCC patients. According to the KM plot, high PGK1 expression was positively correlated with poor OS [HR=1.76, 95% confidence interval (CI)=1.34-2.32, p<0.001, Figure 2A], poor DSS (HR=2.15, 95% CI=1.49-3.10, p<0.001, Figure 2B), and poor PFI (HR=1.63, 95% CI=1.22-2.18, p=0.001, Figure 2C). The KM Plotter database analysis showed that the PGK1 expression level has a significant impact on OS of patients (HR=2.13, logrank p=0.0049, Figure 2D). These results indicate that PGK1 expression is a risk factor for prognosis in patients with HNSCC.

Figure 2.
Figure 2.

Survival curves show overall survival (OS), disease-specific survival (DSS) and progression-free interval (PFI) rates of head and neck squamous cell carcinoma (HNSCC) patients with low or high phosphoglycerate kinase 1 (PGK1) expression from the cancer genome atlas (TCGA) datasets and KM Plotter database. (A) Survival curves showing OS rates from TCGA datasets. (B) Survival curves showing DSS rates from TCGA datasets. (C) Survival curves showing PFI rates from TCGA datasets. (D) Survival curves showing OS rates from the KM Plotter database.

Identification of differentially expressed genes in HNSCC samples with low and high PGK1 expression. The TCGA database was used to analyze the differences between the PGK1 high- and low-expression groups’ gene expression profiles. We sorted the PGK1 expression of all samples from low to high, with the first 0-50% of samples as the PGK1 low expression group, and the last 50-100% of samples as the PGK1 high expression group. DESeq2 (v1.18.0) was used to identify differentially expressed genes (DEGs) between the PGK1 high- and low-expression groups and a total of 570 DEGs, including 70 up-regulated and 500 down-regulated, were identified as statistically significant (|log fold change (logFC)| >1, p<0.05) (Figure 3A). We explored significant genes associated with PGK1 in HNSCC using TCGA data and identified 10,951 significant co-expressed genes, of which 8,758 were positively related and 2,193 were negatively related to PGK1. The heat map displayed the top five positively related DEGs and top five negatively related DEGs between PGK1 high- and PGK1 low-expressed groups (Figure 3B). We found that the genes most positively associated with PGK1 included P4HA1, LDHA, PDK1, NDRG1, and PFKP, while those negatively associated with PGK1 included TMEM125, ACKR1, COX4I2, CLEC3B, and SPNS3.

Figure 3.
Figure 3.

A total of 570 differentially expressed genes (DEGs) were identified as being statistically significant between phosphoglycerate kinase 1 (PGK1) highly-expressed and lowly-expressed groups. (A) Volcano plot of differentially expressed genes, including 500 up-regulated and 70 down-regulated genes. (B) The heatmap displays the top five positively related DEGs and top five negatively related DEGs between PGK1 high- and PGK1 low-expressed groups.

Functional analysis of PGK1. We analyzed 570 differentially expressed genes between high and low expression of PGK1 in HNSCC. GO and KEGG functional enrichment analysis was performed by the clusterProfiler package. Biological process (BP) of GO enrichment analysis showed that epidermal cell differentiation, keratinocyte differentiation and muscle contraction were significantly enriched (Figure 4A). Cellular components (CC) of the GO enrichment analysis showed that contractile fiber, myofibril and contractile fiber part were mainly enriched (Figure 4B). Molecular function (MF) of GO enrichment analysis showed that receptor ligand activity, peptidase regulator activity and peptidase inhibitor activity were prominently enriched (Figure 4C). KEGG enrichment analysis demonstrated that adrenergic signaling in cardiomyocytes was the most significantly enriched pathway, and primary immunodeficiency was also a crucial pathway (Figure 4D). Next, Gene Set Enrichment Analysis (GSEA) analysis was conducted to gain further insight into the biological pathways involved in HNSCC with different PGK1 expression levels. The results showed that high PGK1 expression was mainly enriched in primary immunodeficiency, graft versus host disease, allograft rejection, asthma, and linoleic acid metabolism-related signaling pathways (Figure 4E). Notably, we found primary immunodeficiency in both the KEGG and GSEA result; thus, we speculated that these genes were associated with immunity.

Figure 4.
Figure 4.

Functional analysis of phosphoglycerate kinase 1 (PGK1). (A) Gene ontology (GO) enrichment analysis in the biological process (BP) category. (B) GO enrichment analysis in the molecular function (MF) category. (C) GO enrichment analysis in the cellular component (CC) category. (D) Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis. (E) Enrichment plots from the gene set enrichment analyses (GSEA).

Relationship between PGK1 expression and immune cell infiltration. The previous enrichment analysis revealed that PGK1 was mainly related to primary immunodeficiency. We surmised that there might be some relationship between PGK1 and immune cells. Thus, we furtherly investigated the association between PGK1 expression and immune infiltration in HNSCC by calculating the correlation between PGK1 and 24 immune-cell subsets via the means of ssGSEA with Spearman’s r (Figure 5A). In this regard, PGK1 was shown to be significantly correlated with 16 immune cell types, including positive correlations with the level of infiltration of Th2 cells (r=0.222, p<0.001, Figure 5B), Tcm (r=0.109, p=0.015, Figure 5C), Tgd (r=0.095, p=0.034, Figure 5D), and negatively correlated with the level of infiltration of NK CD56 bright cells (r=−0.213, p<0.001, Figure 5E), NK cells (r=−0.090, p=0.045, Figure 5F), Th17 cells (r=−0.121, p=0.007, Figure 5G), Treg (r=−0.133, p=0.003, Figure 5H), TFH (r=−0.142, p=0.001, Figure 5I), TEM (r=−0.145, p=0.001, Figure 5J), DC (r=−0.153, p<0.001, Figure 5K), Mast cells (r=−0.178, p<0.001, Figure 5L), T cells (r=−0.185, p<0.001, Figure 5M), cytotoxic cells (r=−0.203, p<0.001, Figure 5N), CD8 T cells (r=−0.211, p<0.001, Figure 5O), B cells (r=−0.258, p<0.001, Figure 5P) and pDC (r=−0.344, p<0.001, Figure 5Q). These results revealed that PGK1 may play an integral role in regulating lymphocyte infiltration in HNSCC, which prompted us to further investigate the association between PGK1 expression levels and immune infiltration. Astonishingly, we found significant differences in the levels of infiltrating immune cells when PGK1 expression was divided into high and low groups, including Th2 cells, NK CD56 bright cells, T cells, cytotoxic cells, CD8 T cells, B cells and pDC (Figure 6A, D, L-P, p<0.001), Th17 cells, Treg, TFH, TEM, DC and mast cells (Figure 6F-K, p<0.01), while no significant differences were found regarding Tcm, Tgd and NK cells (Figure 6B-C, E). To verify this result, we set a decreased cellular content of Th2 cells in the KM Plotter database and found that PGK1 does not have a significant impact on OS in HNSCC patients (HR=4.13, logrank p=0.094, Figure 7), indicating the importance of the effect of Th2 cells on PGK1.

Figure 5.
Figure 5.

Association analysis of phosphoglycerate kinase 1 (PGK1) gene expression and immune infiltration. (A) The association between PGK1 expression and 24 tumor-infiltrating lymphocytes. (B-Q) The correlation of PGK1 expression with immune infiltration level of (B) Type 2 T helper (Th2) cells, (C) T central memory (Tcm), (D) Tgd, (E) NK CD56 bright cells, (F) NK cells, (G) Type 17 T helper (Th17) cells, (H) regulatory T cells (Treg), (I) T follicular helper (TFH), (J) T effector memory (Tem), (K) dendritic cells (DC), (L) mast cells, (M) T cells, (N) cytotoxic cells, (O) CD8 T cells, (P) B cells, (Q) plasmacytoid dendritic cells (pDC).

Figure 6.
Figure 6.

Comparison of immune cells between high- and low-phosphoglycerate kinase 1 (PGK1) expression groups. (A-P) Histogram showing the difference of Type 2 T helper (Th2) cells, T central memory (Tcm), T gamma delta (Tgd), NK CD56 bright cells, NK cells, Type 17 T helper (Th17) cells, regulatory T cells (Treg), T follicular helper (TFH), T effector memory (Tem), dendritic cells (DC), mast cells, T cells, cytotoxic cells, CD8 T cells, B cells and plasmacytoid dendritic cells (pDC) infiltration level between high- and low-PGK1 expression groups.

Figure 7.
Figure 7.

Survival curves show overall survival (OS) of head and neck squamous cell carcinoma (HNSCC) patients with low or high phosphoglycerate kinase 1 (PGK1) expression from the KM Plotter database in the presence of decreased cellular content of Type 2 T helper (Th2) cells.

Discussion

HNSCC is the sixth most common cancer in humans and the prognosis is still rather poor (25). It is necessary to find more accurate biomarkers for detection at an early stage and for better monitoring cancer progression. In recent years, an increasing number of studies has shown that PGK1 is overexpressed in various cancer types, and the inhibition of PGK1 could suppress tumor growth and metastasis (26, 27). Many cancer cells display enhanced glycolysis and suppressed mitochondrial metabolism; PGK1 is an ATP-producing enzyme involved in the first ATP-generating step of the glycolytic pathway. PGK1 promotes tumorigenesis and progression by mediating glycolysis that generates ATP for tumor cells, especially under hypoxic conditions (28, 29). To date, few studies have explored the relationship between PGK1 and HNSCC (30).

In this study, we attempted to explore the potential mechanism of PGK1 in promoting HNSCC and its feasibility as a molecular marker. Bioinformatics analysis was conducted to assess the prognostic value of PGK1 in HNSCC using data from TCGA. PGK1 was more highly expressed in tumor tissues compared to normal samples. In addition, PGK1 expression was an independent prognostic factor for OS in both univariate and multivariate regression analyses; high PGK1 expression was positively correlated with poor OS. High PGK1 expression has been associated with poor prognosis in breast, liver and endometrial cancer, and it may serve as a promising biomarker and target therapy for these tumors (14, 31, 32). Hence, we conjectured that PGK1may also serve as a biomarker for HNSCC.

We found multiple oncogenes and immune-related genes were significantly associated with PGK1. The genes most positively correlated with PGK1 included P4HA1, LDHA, PDK1, NDRG1, and PFKP, while genes negatively correlated with PGK1 included TMEM125, ACKR1, COX4I2, CLEC3B, and SPNS3, some of which have been demonstrated to be related to HNSCC. For example, P4HA1, a single-gene surrogate of hypoxia, could serve as a diagnostic biomarker and independent prognostic factor in HNSCC; significant correlations have been found between the P4HA1 mRNA level and the mRNA level of several EMT and stem cell markers (33). Concerning the NDRG1, LDHA and PDK1 oncogenes, their blockage could significantly inhibit the HNSCC cell proliferation and apoptosis (34-37). To further investigate the biological function of PGK1 in HNSCC, we used TCGA data for GO, KEGG functional enrichment analysis and GSEA. The KEGG analysis showed that PGK1 influences the HNSCC development process through the adrenergic signaling in cardiomyocytes, PAR signaling pathway, dilated cardiomyopathy and primary immunodeficiency. The GSEA results showed that high PGK1 expression was mainly enriched in primary immunodeficiency, graft versus host disease, allograft rejection, asthma, and linoleic acid metabolism-related signaling pathways. There are primary immunodeficiency signaling pathways in both KEGG and GSEA results. Primary immunodeficiencies (PIDs) are genetic disorders that predispose to frequent and severe infections, autoimmunity and cancer, while the type of malignancy depends on the primary immunodeficiency, the age of the patient and possible viral infection (38). We therefore hypothesized that PGK1 was associated with immunity.

Given that tumor-infiltrating immune cells account for an indispensable component of the tumor microenvironment and that enhanced glycolytic activity in cancer is associated with pro-tumor immunity (39, 40), we sought to investigate the relationship between PGK1 and immune infiltration in HNSCC. In this study, we found that several tumor-infiltrating immune cells correlated with the expression of PGK1 in HNSCC. We further revealed a positive correlation between PGK1 expression and Th2 cells, Tcm, and Tgd. Type 2 immunity was illustrated by T helper 2 lymphocytes (Th2) and downstream cytokines (IL-4, IL-13, IL-31) as well as group 2 innate lymphoid cells (ILC2) (41). Immune cells, including T cells, are capable of mounting immune responses against tumors. The ability of the immune system to detect and eliminate neoplastic cells is known as tumor immune surveillance (42, 43). Th2 cells and type 2 immunity are involved in tumor immune surveillance, for example by reducing the size of established tumors (44). T central memory cells (Tcm), included in memory T cells show super persistence and antitumor immunity (41), and Manjarrez-Orduño found the increased frequencies of Tcm cells and enhanced tumor inflammation profiles in melanoma and non-small cell lung cancer (NSCLC) (45). These findings suggest a potential correlation between PGK1 and immune infiltration in HNSCC. However, there are some shortcomings in this study, including the need to further investigate the detailed mechanism of the impact of PGK1 on immune infiltration in HNSCC, by designing further in vivo and in vitro experiments.

Conclusion

To conclude, we demonstrate that PGK1 expression is upregulated in HNSCC and is closely associated with poor prognosis and immune cell infiltration. This study lays the foundation for a detailed study of the relationship between PGK1 and tumor-associated immune cell infiltration. Our findings suggest that PGK1 could serve as a promising independent prognostic biomarker and potential immunotherapeutic target for HNSCC. Further experimental validation is needed to elucidate the biological impact and underlying mechanism of PGK1.

Acknowledgements

This study was supported by the Outstanding Young Talent Project of Zunyi Medical University (17zy-002) And the Construction Project of Rural Revitalization from Guizhou Province Ministry of Education (QJJS[2022]037).

Footnotes

  • Conflicts of Interest

    The Authors declare that there are no conflicts of interest.

  • Authors’ Contributions

    PW: Formal analysis, visualization, writing. QW: Conceptualization, review & editing, funding support. KW: Methodology, review & editing. YYW, YLX, ZCY: Resources, formal analysis, software.

  • Received July 31, 2023.
  • Revision received September 10, 2023.
  • Accepted September 15, 2023.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC-ND) 4.0 international license (https://creativecommons.org/licenses/by-nc-nd/4.0).

References

  1. ↵
    1. Johnson DE,
    2. Burtness B,
    3. Leemans CR,
    4. Lui VWY,
    5. Bauman JE,
    6. Grandis JR
    : Head and neck squamous cell carcinoma. Nat Rev Dis Primers 6(1): 92, 2020. DOI: 10.1038/s41572-020-00224-3
    OpenUrlCrossRefPubMed
  2. ↵
    1. Sung H,
    2. Ferlay J,
    3. Siegel RL,
    4. Laversanne M,
    5. Soerjomataram I,
    6. Jemal A,
    7. Bray F
    : Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71(3): 209-249, 2021. DOI: 10.3322/caac.21660
    OpenUrlCrossRefPubMed
  3. ↵
    1. Citron F,
    2. Segatto I,
    3. Musco L,
    4. Pellarin I,
    5. Rampioni Vinciguerra GL,
    6. Franchin G,
    7. Fanetti G,
    8. Miccichè F,
    9. Giacomarra V,
    10. Lupato V,
    11. Favero A,
    12. Concina I,
    13. Srinivasan S,
    14. Avanzo M,
    15. Castiglioni I,
    16. Barzan L,
    17. Sulfaro S,
    18. Petrone G,
    19. Viale A,
    20. Draetta GF,
    21. Vecchione A,
    22. Belletti B,
    23. Baldassarre G
    : miR-9 modulates and predicts the response to radiotherapy and EGFR inhibition in HNSCC. EMBO Mol Med 13(7): e12872, 2021. DOI: 10.15252/emmm.202012872
    OpenUrlCrossRef
  4. ↵
    1. Ebnoether E,
    2. Muller L
    : Diagnostic and therapeutic applications of exosomes in cancer with a special focus on head and neck squamous cell carcinoma (HNSCC). Int J Mol Sci 21(12): 4344, 2020. DOI: 10.3390/ijms21124344
    OpenUrlCrossRef
  5. ↵
    1. Yang Z,
    2. Yan G,
    3. Zheng L,
    4. Gu W,
    5. Liu F,
    6. Chen W,
    7. Cui X,
    8. Wang Y,
    9. Yang Y,
    10. Chen X,
    11. Fu Y,
    12. Xu X
    : YKT6, as a potential predictor of prognosis and immunotherapy response for oral squamous cell carcinoma, is related to cell invasion, metastasis, and CD8+ T cell infiltration. Oncoimmunology 10(1): 1938890, 2021. DOI: 10.1080/2162402X.2021.1938890
    OpenUrlCrossRef
  6. ↵
    1. Li W,
    2. Xu M,
    3. Li Y,
    4. Huang Z,
    5. Zhou J,
    6. Zhao Q,
    7. Le K,
    8. Dong F,
    9. Wan C,
    10. Yi P
    : Comprehensive analysis of the association between tumor glycolysis and immune/inflammation function in breast cancer. J Transl Med 18(1): 92, 2020. DOI: 10.1186/s12967-020-02267-2
    OpenUrlCrossRef
  7. ↵
    1. Payen VL,
    2. Mina E,
    3. Van Hée VF,
    4. Porporato PE,
    5. Sonveaux P
    : Monocarboxylate transporters in cancer. Mol Metab 33: 48-66, 2020. DOI: 10.1016/j.molmet.2019.07.006
    OpenUrlCrossRef
  8. ↵
    1. He Y,
    2. Luo Y,
    3. Zhang D,
    4. Wang X,
    5. Zhang P,
    6. Li H,
    7. Ejaz S,
    8. Liang S
    : PGK1-mediated cancer progression and drug resistance. Am J Cancer Res 9(11): 2280-2302, 2019.
    OpenUrl
  9. ↵
    1. Duncan L,
    2. Shay C,
    3. Teng Y
    : PGK1: An essential player in modulating tumor metabolism. Methods Mol Biol 2343: 57-70, 2022. DOI: 10.1007/978-1-0716-1558-4_4
    OpenUrlCrossRef
  10. ↵
    1. Yang Y,
    2. Cui H,
    3. Li D,
    4. Gao Y,
    5. Chen L,
    6. Zhou C,
    7. Feng M,
    8. Tu W,
    9. Li S,
    10. Chen X,
    11. Hao B,
    12. Li L,
    13. Cao Y
    : Prognosis and immunological characteristics of PGK1 in lung adenocarcinoma: a systematic analysis. Cancers (Basel) 14(21): 5228, 2022. DOI: 10.3390/cancers14215228
    OpenUrlCrossRef
  11. ↵
    1. Wang F,
    2. Zhang H,
    3. Liu B,
    4. Liu W,
    5. Zhang Z
    : miR-6869-5p inhibits glioma cell proliferation and invasion via targeting PGK1. Mediators Inflamm 2020: 9752372, 2020. DOI: 10.1155/2020/9752372
    OpenUrlCrossRef
  12. ↵
    1. Liang C,
    2. Shi S,
    3. Qin Y,
    4. Meng Q,
    5. Hua J,
    6. Hu Q,
    7. Ji S,
    8. Zhang B,
    9. Xu J,
    10. Yu XJ
    : Localisation of PGK1 determines metabolic phenotype to balance metastasis and proliferation in patients with SMAD4-negative pancreatic cancer. Gut 69(5): 888-900, 2020. DOI: 10.1136/gutjnl-2018-317163
    OpenUrlAbstract/FREE Full Text
  13. ↵
    1. Li Y,
    2. Wang S,
    3. Zhang X,
    4. Yang R,
    5. Wei X,
    6. Yan R,
    7. Jiang Y,
    8. Shen W
    : Expression characteristics and significant prognostic values of PGK1 in breast cancer. Front Mol Biosci 8: 695420, 2021. DOI: 10.3389/fmolb.2021.695420
    OpenUrlCrossRef
  14. ↵
    1. Hu H,
    2. Zhu W,
    3. Qin J,
    4. Chen M,
    5. Gong L,
    6. Li L,
    7. Liu X,
    8. Tao Y,
    9. Yin H,
    10. Zhou H,
    11. Zhou L,
    12. Ye D,
    13. Ye Q,
    14. Gao D
    : Acetylation of PGK1 promotes liver cancer cell proliferation and tumorigenesis. Hepatology 65(2): 515-528, 2017. DOI: 10.1002/hep.28887
    OpenUrlCrossRefPubMed
  15. ↵
    1. Zhang Y,
    2. Cai H,
    3. Liao Y,
    4. Zhu Y,
    5. Wang F,
    6. Hou J
    : Activation of PGK1 under hypoxic conditions promotes glycolysis and increases stem cell like properties and the epithelial mesenchymal transition in oral squamous cell carcinoma cells via the AKT signalling pathway. Int J Oncol 57(3): 743-755, 2020. DOI: 10.3892/ijo.2020.5083
    OpenUrlCrossRef
    1. Zhao C,
    2. Zhou Y,
    3. Ma H,
    4. Wang J,
    5. Guo H,
    6. Liu H
    : A four-hypoxia-genes-based prognostic signature for oral squamous cell carcinoma. BMC Oral Health 21(1): 232, 2021. DOI: 10.1186/s12903-021-01587-z
    OpenUrlCrossRef
  17. ↵
    1. Bajrai LH,
    2. Sohrab SS,
    3. Mobashir M,
    4. Kamal MA,
    5. Rizvi MA,
    6. Azhar EI
    : Understanding the role of potential pathways and its components including hypoxia and immune system in case of oral cancer. Sci Rep 11(1): 19576, 2021. DOI: 10.1038/s41598-021-98031-7
    OpenUrlCrossRef
  18. ↵
    1. Li T,
    2. Fan J,
    3. Wang B,
    4. Traugh N,
    5. Chen Q,
    6. Liu JS,
    7. Li B,
    8. Liu XS
    : TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res 77(21): e108-e110, 2017. DOI: 10.1158/0008-5472.CAN-17-0307
    OpenUrlAbstract/FREE Full Text
  19. ↵
    1. Tang Z,
    2. Kang B,
    3. Li C,
    4. Chen T,
    5. Zhang Z
    : GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res 47(W1): W556-W560, 2019. DOI: 10.1093/nar/gkz430
    OpenUrlCrossRefPubMed
  20. ↵
    1. Chandrashekar DS,
    2. Karthikeyan SK,
    3. Korla PK,
    4. Patel H,
    5. Shovon AR,
    6. Athar M,
    7. Netto GJ,
    8. Qin ZS,
    9. Kumar S,
    10. Manne U,
    11. Creighton CJ,
    12. Varambally S
    : UALCAN: An update to the integrated cancer data analysis platform. Neoplasia 25: 18-27, 2022. DOI: 10.1016/j.neo.2022.01.001
    OpenUrlCrossRefPubMed
  21. ↵
    1. Li Y,
    2. Liu Z,
    3. Zhang Y
    : Expression and prognostic impact of FZDs in pancreatic adenocarcinoma. BMC Gastroenterol 21(1): 79, 2021. DOI: 10.1186/s12876-021-01643-6
    OpenUrlCrossRef
  22. ↵
    1. Yu G,
    2. Wang LG,
    3. Han Y,
    4. He QY
    : clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16(5): 284-287, 2012. DOI: 10.1089/omi.2011.0118
    OpenUrlCrossRefPubMed
  23. ↵
    1. Bindea G,
    2. Mlecnik B,
    3. Tosolini M,
    4. Kirilovsky A,
    5. Waldner M,
    6. Obenauf AC,
    7. Angell H,
    8. Fredriksen T,
    9. Lafontaine L,
    10. Berger A,
    11. Bruneval P,
    12. Fridman WH,
    13. Becker C,
    14. Pagès F,
    15. Speicher MR,
    16. Trajanoski Z,
    17. Galon J
    : Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity 39(4): 782-795, 2013. DOI: 10.1016/j.immuni.2013.10.003
    OpenUrlCrossRefPubMed
  24. ↵
    1. Hänzelmann S,
    2. Castelo R,
    3. Guinney J
    : GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14: 7, 2013. DOI: 10.1186/1471-2105-14-7
    OpenUrlCrossRefPubMed
  25. ↵
    1. Idel C,
    2. Ribbat-Idel J,
    3. Kuppler P,
    4. Krupar R,
    5. Offermann A,
    6. Vogel W,
    7. Rades D,
    8. Kirfel J,
    9. Wollenberg B,
    10. Perner S
    : EVI1 as a marker for lymph node metastasis in HNSCC. Int J Mol Sci 21(3): 854, 2020. DOI: 10.3390/ijms21030854
    OpenUrlCrossRef
  26. ↵
    1. Zhang Y,
    2. Yu G,
    3. Chu H,
    4. Wang X,
    5. Xiong L,
    6. Cai G,
    7. Liu R,
    8. Gao H,
    9. Tao B,
    10. Li W,
    11. Li G,
    12. Liang J,
    13. Yang W
    : Macrophage-associated PGK1 phosphorylation promotes aerobic glycolysis and tumorigenesis. Mol Cell 71(2): 201-215.e7, 2018. DOI: 10.1016/j.molcel.2018.06.023
    OpenUrlCrossRefPubMed
  27. ↵
    1. Chu Z,
    2. Huo N,
    3. Zhu X,
    4. Liu H,
    5. Cong R,
    6. Ma L,
    7. Kang X,
    8. Xue C,
    9. Li J,
    10. Li Q,
    11. You H,
    12. Zhang Q,
    13. Xu X
    : FOXO3A-induced LINC00926 suppresses breast tumor growth and metastasis through inhibition of PGK1-mediated Warburg effect. Mol Ther 29(9): 2737-2753, 2021. DOI: 10.1016/j.ymthe.2021.04.036
    OpenUrlCrossRef
  28. ↵
    1. Nie H,
    2. Ju H,
    3. Fan J,
    4. Shi X,
    5. Cheng Y,
    6. Cang X,
    7. Zheng Z,
    8. Duan X,
    9. Yi W
    : O-GlcNAcylation of PGK1 coordinates glycolysis and TCA cycle to promote tumor growth. Nat Commun 11(1): 36, 2020. DOI: 10.1038/s41467-019-13601-8
    OpenUrlCrossRef
  29. ↵
    1. Qian X,
    2. Li X,
    3. Shi Z,
    4. Xia Y,
    5. Cai Q,
    6. Xu D,
    7. Tan L,
    8. Du L,
    9. Zheng Y,
    10. Zhao D,
    11. Zhang C,
    12. Lorenzi PL,
    13. You Y,
    14. Jiang BH,
    15. Jiang T,
    16. Li H,
    17. Lu Z
    : PTEN suppresses glycolysis by dephosphorylating and inhibiting autophosphorylated PGK1. Mol Cell 76(3): 516-527.e7, 2019. DOI: 10.1016/j.molcel.2019.08.006
    OpenUrlCrossRefPubMed
  30. ↵
    1. Li SR,
    2. Man QW,
    3. Liu B
    : Development and validation of a novel hypoxia-related signature for prognostic and immunogenic evaluation in head and neck squamous cell carcinoma. Front Oncol 12: 943945, 2022. DOI: 10.3389/fonc.2022.943945
    OpenUrlCrossRef
  31. ↵
    1. Fu D,
    2. He C,
    3. Wei J,
    4. Zhang Z,
    5. Luo Y,
    6. Tan H,
    7. Ren C
    : PGK1 is a potential survival biomarker and invasion promoter by regulating the HIF-1α-mediated epithelial-mesenchymal transition process in breast cancer. Cell Physiol Biochem 51(5): 2434-2444, 2018. DOI: 10.1159/000495900
    OpenUrlCrossRef
  32. ↵
    1. Guo S,
    2. Xiao Y,
    3. Li D,
    4. Jiang Q,
    5. Zhu L,
    6. Lin D,
    7. Jiang H,
    8. Chen W,
    9. Wang L,
    10. Liu C,
    11. Fang W,
    12. Lin L
    : PGK1 and GRP78 overexpression correlates with clinical significance and poor prognosis in Chinese endometrial cancer patients. Oncotarget 9(1): 680-690, 2017. DOI: 10.18632/oncotarget.23090
    OpenUrlCrossRef
  33. ↵
    1. Kappler M,
    2. Kotrba J,
    3. Kaune T,
    4. Bache M,
    5. Rot S,
    6. Bethmann D,
    7. Wichmann H,
    8. Güttler A,
    9. Bilkenroth U,
    10. Horter S,
    11. Gallwitz L,
    12. Kessler J,
    13. Greither T,
    14. Taubert H,
    15. Eckert AW,
    16. Vordermark D
    : P4HA1: A single-gene surrogate of hypoxia signatures in oral squamous cell carcinoma patients. Clin Transl Radiat Oncol 5: 6-11, 2017. DOI: 10.1016/j.ctro.2017.05.002
    OpenUrlCrossRefPubMed
  34. ↵
    1. Li Q,
    2. Shen Z,
    3. Wu Z,
    4. Shen Y,
    5. Deng H,
    6. Zhou C,
    7. Liu H
    : High P4HA1 expression is an independent prognostic factor for poor overall survival and recurrent-free survival in head and neck squamous cell carcinoma. J Clin Lab Anal 34(3): e23107, 2020. DOI: 10.1002/jcla.23107
    OpenUrlCrossRefPubMed
    1. Dos Santos M, Head Neck Genome Project/GENCAPO,
    2. da Cunha Mercante AM,
    3. Nunes FD,
    4. Leopoldino AM,
    5. de Carvalho MB,
    6. Gazito D,
    7. López RVM,
    8. Chiappini PBO,
    9. de Carvalho Neto PB,
    10. Fukuyama EE,
    11. Tajara EH,
    12. Louro ID,
    13. da Silva AMÃ
    : Prognostic significance of NDRG1 expression in oral and oropharyngeal squamous cell carcinoma. Mol Biol Rep 39(12): 10157-10165, 2012. DOI: 10.1007/s11033-012-1889-0
    OpenUrlCrossRefPubMed
    1. Chen TY,
    2. Hsieh YT,
    3. Huang JM,
    4. Liu CJ,
    5. Chuang LT,
    6. Huang PC,
    7. Kuo TY,
    8. Chia HY,
    9. Chou CY,
    10. Chang CW,
    11. Chen YF,
    12. Chen HM,
    13. Lo JF,
    14. Li WC
    : Determination of pyruvate metabolic fates modulates head and neck tumorigenesis. Neoplasia 21(7): 641-652, 2019. DOI: 10.1016/j.neo.2019.04.007
    OpenUrlCrossRef
  37. ↵
    1. Bhola NE,
    2. Freilino ML,
    3. Joyce SC,
    4. Sen M,
    5. Thomas SM,
    6. Sahu A,
    7. Cassell A,
    8. Chen CS,
    9. Grandis JR
    : Antitumor mechanisms of targeting the PDK1 pathway in head and neck cancer. Mol Cancer Ther 11(6): 1236-1246, 2012. DOI: 10.1158/1535-7163.MCT-11-0936
    OpenUrlAbstract/FREE Full Text
  38. ↵
    1. Salavoura K,
    2. Kolialexi A,
    3. Tsangaris G,
    4. Mavrou A
    : Development of cancer in patients with primary immunodeficiencies. Anticancer Res 28(2b): 1263-1269, 2008.
    OpenUrlAbstract/FREE Full Text
  39. ↵
    1. Li M,
    2. Zhang A,
    3. Qi X,
    4. Yu R,
    5. Li J
    : A novel inhibitor of PGK1 suppresses the aerobic glycolysis and proliferation of hepatocellular carcinoma. Biomed Pharmacother 158: 114115, 2023. DOI: 10.1016/j.biopha.2022.114115
    OpenUrlCrossRef
  40. ↵
    1. Park HB,
    2. Choi BC,
    3. Baek KH
    : PGK1 modulates balance between pro- and anti-inflammatory cytokines by interacting with ITI-H4. Biomed Pharmacother 161: 114437, 2023. DOI: 10.1016/j.biopha.2023.114437
    OpenUrlCrossRef
  41. ↵
    1. Belmesk L,
    2. Muntyanu A,
    3. Cantin E,
    4. AlHalees Z,
    5. Jack CS,
    6. Le M,
    7. Sasseville D,
    8. Iannattone L,
    9. Ben-Shoshan M,
    10. Litvinov IV,
    11. Netchiporouk E
    : Prominent role of type 2 immunity in skin diseases: Beyond atopic dermatitis. J Cutan Med Surg 26(1): 33-49, 2022. DOI: 10.1177/12034754211027858
    OpenUrlCrossRef
  42. ↵
    1. Crispin JC,
    2. Tsokos GC
    : Cancer immunosurveillance by CD8 T cells. F1000Res 9: 80, 2020. DOI: 10.12688/f1000research.21150.1
    OpenUrlCrossRef
  43. ↵
    1. St Paul M,
    2. Ohashi PS
    : The roles of CD8+ T cell subsets in antitumor immunity. Trends Cell Biol 30(9): 695-704, 2020. DOI: 10.1016/j.tcb.2020.06.003
    OpenUrlCrossRef
  44. ↵
    1. Schreiber S,
    2. Hammers CM,
    3. Kaasch AJ,
    4. Schraven B,
    5. Dudeck A,
    6. Kahlfuss S
    : Metabolic interdependency of Th2 cell-mediated type 2 immunity and the tumor microenvironment. Front Immunol 12: 632581, 2021. DOI: 10.3389/fimmu.2021.632581
    OpenUrlCrossRef
  45. ↵
    1. Manjarrez-Orduño N,
    2. Menard LC,
    3. Kansal S,
    4. Fischer P,
    5. Kakrecha B,
    6. Jiang C,
    7. Cunningham M,
    8. Greenawalt D,
    9. Patel V,
    10. Yang M,
    11. Golhar R,
    12. Carman JA,
    13. Lezhnin S,
    14. Dai H,
    15. Kayne PS,
    16. Suchard SJ,
    17. Bernstein SH,
    18. Nadler SG
    : Circulating T cell subpopulations correlate with immune responses at the tumor site and clinical response to PD1 inhibition in non-small cell lung cancer. Front Immunol 9: 1613, 2018. DOI: 10.3389/fimmu.2018.01613
    OpenUrlCrossRefPubMed
Back to top

In this issue

Print
Download PDF
Article Alerts
Email Article
Citation Tools
Reprints and Permissions
Phosphoglycerate-kinase-1 Is a Potential Prognostic Biomarker in HNSCC and Correlates With Immune Cell Infiltration
PING WANG, YUE-YUE WANG, YANG-LONG XU, CHUN-YU ZHANG, KUN WANG, QIAN WANG
Cancer Genomics & Proteomics Dec 2023, 20 (6suppl) 723-734; DOI: 10.21873/cgp.20419
Twitter logo Facebook logo Mendeley logo

Jump to section

Keywords