Skip to main content

Cellular hierarchy framework based on single-cell/multi-patient sample sequencing reveals metabolic biomarker PYGL as a therapeutic target for HNSCC



A growing body of research has revealed the connection of metabolism reprogramming and tumor progression, yet how metabolism reprogramming affects inter-patient heterogeneity and prognosis in head and neck squamous cell carcinoma (HNSCC) still requires further explorations.


A cellular hierarchy framework based on metabolic properties discrepancy, METArisk, was introduced to re-analyze the cellular composition from bulk transcriptomes of 486 patients through deconvolution utilizing single-cell reference profiles from 25 primary and 8 metastatic HNSCC sample integration of previous studies. Machine learning methods were used to identify the correlations between metabolism-related biomarkers and prognosis. The functions of the genes screened out in tumor progression, metastasis and chemotherapy resistance were validated in vitro by cellular functional experiments and in vivo by xenograft tumor mouse model.


Incorporating the cellular hierarchy composition and clinical properties, the METArisk phenotype divided multi-patient cohort into two classes, wherein poor prognosis of METArisk-high subgroup was associated with a particular cluster of malignant cells with significant activity of metabolism reprogramming enriched in metastatic single-cell samples. Subsequent analysis targeted for phenotype differences between the METArisk subgroups identified PYGL as a key metabolism-related biomarker that enhances malignancy and chemotherapy resistance by GSH/ROS/p53 pathway, leading to poor prognosis of HNSCC.


PYGL was identified as a metabolism-related oncogenic biomarker that promotes HNSCC progression, metastasis and chemotherapy resistance though GSH/ROS/p53 pathway. Our study revealed the cellular hierarchy composition of HNSCC from the cell metabolism reprogramming perspective and may provide new inspirations and therapeutic targets for HNSCC in the future.


Head and neck squamous cell carcinoma (HNSCC) is the most prevalent type of malignancy in the head and neck region with high incidences and adverse 5-year survival rate [1]. Poor clinical outcomes are attributed to extensive inter-patient heterogeneity and chemotherapy resistance, highlighting the inadequacy of standard therapy for curing patients with HNSCC [2]. Therefore, further exploration of cellular mechanisms that drive HNSCC development and identification of new therapeutic targets for overcoming chemotherapy resistance, are of great significance for improving the prognosis of HNSCC patients.

Previous studies have shown that cancer cells often perform epigenetic alteration to tackle challenges from microenvironment and promote adaptability [3]. Cell metabolism reprogramming, one of the crucial tumor epigenetic alterations in response to fluctuating energy needs [4], allows cancer cells to dynamically adjust energy generation and bioenergetics through several metabolic pathways such as glycolysis [5], gluconeogenesis [6], lipid metabolism [7] and arachidonic acid metabolism [8]. Malignant behaviors including proliferation, invasion and metastasis have been proved to correlate with cell metabolism reprogramming [9], wherein biological processes have been increasingly revealed. For instance, cancer cells can stimulate metastasis through targeting fatty acid receptor CD36 via O-GlcNAcylation [10, 11], or affect lipid metabolism through interferon-γ and granzyme B to alter the function of stromal and immune cells from microenvironment, leading to tumor invasion and recurrence [12]. Simultaneously, metabolic changes in cancer cells are also associated with chemotherapy resistance [13]. Activation of glutamine metabolism could significantly up-regulate adipogenesis, leading to sorafenib resistance in tumor [14]. Moreover, resistance of gemcitabine can be induced by increasing glycolysis mediated by hypoxia-inducible factor (HIF)-1α [15]. In allusion to chemotherapy resistance related cell metabolism reprogramming, studies of targeting metabolic changes through the metabolic core pathways in tumor cells has been emerging [16, 17], which may better solve the drug resistance of tumors and improve the prognosis of patients.

Existing studies have confirmed that the changes of metabolic characteristics of HNSCC cells through diverse metabolic pathways inducing the malignant behaviors and chemotherapy resistance [18,19,20], but the relationship between these alterations and patient prognosis has not yet been systematically elucidated. On the other hand, cell metabolism reprogramming may also contribute to the heterogeneity of HNSCC patients [21].Various research about the underlying mechanisms of metabolism reprogramming emerged recent years, indicating it would be a promising direction to break through. Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for dissecting cellular hierarchies [22], and there has been a study utilizing this technique on metabolic characteristics in cellular hierarchies of tumor cells [23]. However, owing to limited number of patients from scRNA-seq datasets, few studies have comprehensively explored the link between metabolism reprogramming reflected in cellular hierarchy composition and inter-patient heterogeneity, which may account for the poor prognosis and chemotherapy resistance in HNSCC. With integration of single-cell and multi-patient sample sequencing, we explored the relationship of specific tumor cell subsets and patient’s prognosis, and further revealed the therapeutic target for more information from the combination of sequencing data.

In this study, the cellular hierarchies of 486 patients with HNSCC were characterized through gene expression deconvolution on bulk HNSCC transcriptomes using single-cell reference profiles of distinct HNSCC cell types including METAactive and METAsilent malignant cells and integrated non-malignant cells from 25 primary and 8 metastatic HNSCC samples. METAactive cells were classified as the particular malignant cluster with high potential of cell metabolism reprogramming and metastasis and METAsilent cells possessed comparatively low metabolic activity and mainly distributed in primary tumor, by which patients were divided into METArisk-high and METArisk-low groups based on the proportion. Later, PYGL was identified as the key metabolism-related biomarker that drives the poor prognosis and drug resistance of patients in METArisk-high group, wherein PYGL/GSH/ROS/p53 was demonstrated as a key pathway (Fig. 1 and Fig.S1). Sequentially, our study may provide reference for further understanding of genetic characteristics of cancer metabolism of HNSCC, and how metabolism reprogramming affects tumor malignancy and chemotherapy resistance in HNSCC patients, thus, hopefully providing a new target in metabolic pathways to improve patient prognosis.

figure 1

Overall design of the study


Data source

The single-cell RNA expression profile of 25 primary and 8 metastatic head and neck squamous carcinoma (HNSCC) samples were downloaded from NCBI Sequence Read Archive (SRA) with an accession #: SRP332116, SRP228811. The mRNA expression profile and clinical information of multi-patient HNSCC samples were downloaded from The Cancer Genome Atlas (TCGA) ( database and The Gene Expression Omnibus (GEO) ( database.

Quality control of single-cell and bulk tissue transcriptomic samples

The inclusion criteria for the single-cell transcriptomic samples includes: (1) From the datasets ≥ 4 samples, at least 2 controls and 2 cases, and availability of raw data or gene expression data; (2) Extraction protocol:Droplet-based single-cell RNA-sequencing on 10X Genomics Chromium platform.

A standard Seurat quality control, transformation, and data integration was then performed: (1) the data with low-quality cells expressing ≤ 200 genes or ≥ 2500 genes or the percent of mitochondrial genes infinite or ≥ 15% were removed and the data with genes expressed in less than 3 cells were removed; (2) For better interpretability, the gene expression matrixes were then transformed using the 'ScaleData' function in Seurat with 3000 highly variable genes, regressions were applied to reduce the effects of mitochondrial genes; (3) 'FindIntegrationAnchors' and 'IntegrateData' function was then used to integrate datasets.

The inclusion criteria for the bulk tissue transcriptomic samples were: (1) Non-formalin soaking samples; (2) With complete clinical metadata; (3) With definite clinical outcome and survival time.

Pre-processing of scRNA-sequencing data

CellRanger (version 4.0.0) was used to obtain the fastq files of the raw data and annotated with the human genome reference sequence (GRCh38). The gene-barcode matrix was then obtained following the Seurat (version 4.0.4) pipeline in R software (version 4.0.5, R-Foundation, Vienna, Austria).

Cell clustering analysis, visualization, and annotation

Cell-clustering and sub-clustering analyses were performed with the FindClusters function of the Seurat package with proper resolutions. For the re-clustering of each type of cell clusters, cells with ribosome gene ratio higher than 35% were filtered. Uniform manifold approximation and projection (UMAP) was used to display identified cell clusters and sub-clusters. The cell clusters were annotated based on highly expressed genes, unique expressed genes, and reported canonical cellular markers.

Clustering and cell type assignment

First, a neighborhood graph was constructed to identify related groups of cells. Next, Leiden clustering was performed on the neighborhood graph. The cluster assignments were then visualized on UMAP plots. Using a combination of top expressed genes in each cluster and a list of known marker genes, cell types were assigned to each cluster.


Pseudobulk was used to detect the Differential expressed genes (DEGs) between primary- and metastatic- specific populations, for it aggregates count values from each sample and cell cluster to create data that can be analyzed using the same methods as bulk RNA-seq data, maintaining the same number of genes but reducing the number of cells to the number of samples in the gene expression matrix [24].

Functional analysis

The DEGs for the malignant group were independently imported into Enrichr, an online bioinformatic website ( for Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The top 10 pathways were selected based on p-value ranking. “META_ACTIVATE” was defined as the gene signature from metabolism pathways selected in KEGG analysis intersected with genes from single cell database. Differential expression rank order was used for subsequent Gene Set Enrichment Analysis (GSEA), performed using the clusterProfiler package in R.


META_ACTIVATE gene signature was set as input data for area under the curve (AUC) value calculation. According to the AUC value, gene-expression rankings were built for each cell. The AUC estimates the proportion of genes in the gene set that are highly expressed in each cell. Cells expressing more genes from the gene set will have higher AUC values than cells expressing fewer genes. Function “AUCell_exploreThresholds” was used to calculate the threshold that could be used to consider the current gene-set active. Then, cell clustering UMAP embedding was colored based on the AUC score of each cell to show which cell clusters were active in the META_ACTIVATE gene signature. Metabolism-related genes (MRGs) for downstream analysis were obtained from the intersection of the META_ACTIVATE gene signature and cell cluster with high AUC score.


Raw gene expression counts from single cell database were used as input for signature matrix generation with CIBERSORTx28. Default settings were used, with the exception of the minimum expression parameter, which was set to 0.25. Deconvolution was performed on normalized Transcripts per million (TPM) bulk RNA-seq data using S-mode batch correction and Absolute mode.

Analysis of the prognostic and therapeutic value of MRGs in HNSCC

“Limma” R package was used to identify DEGs between 486 HNSCC and 44 normal samples and between METArisk-low and METArisk-high group with |log2FC|> 1 and adjusted p-value < 0.05. Then the DEGs were overlapped with MRGs to obtain Differential Expressed Metabolism-Related Genes (DEMRGs) involved in HNSCC. A customization of an open-source the support vector machine recursive feature elimination (SVM-RFE) R script ( based on the R package “e1071” was made to train samples through the training set, sorting the scores of DEMRGs and estimation of tenfold cross-validation errors of gene combinations. The gene combination with the least tenfold cross-validation errors was selected as SVM-RFE generated prognosis-related gene candidates. Multivariate Cox regression analysis was used to identify the independent prognostic factors among the DEMRG candidates and the prognostic value was evaluated by Kaplan–Meier analysis. Images of immunohistochemistry (IHC) staining of the protein products of the genes in HNSCC samples were extracted from the Human Protein Atlas (HPA) database ( Computational Analysis of Resistance (CARE) is a software for identifying genome-scale biomarkers of targeted therapy response using compound screen data. “OncoPredict” is an R package for predicting the drug response, by which the associations of gene expression with the sensitivities to the commonly utilized chemotherapy drugs were investigated.

Metabolic heterogeneity analysis of PYGL-low and PYGL-high groups

To characterize the metabolic features of PYGL-low and PYGL-high groups, the enrichment of 113 metabolism processes in different clusters were quantified by “GSVA” R package and the differential analysis was conducted by “limma” R package. Thereafter, unsupervised non-negative matrix factorization (NMF) clustering was performed using “NMF” R package with the settings “nrun = 50, method = brunet” based on the genes involved in the metabolism process “GSH metabolism”. According to the maximum of cophenetic and dispersion values in rank survey, HNSCC patients were divided into glutathione (GSH) metabolism up-regulated and GSH metabolism down-regulated clusters. To characterize the of different clusters, we analyzed and compared the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression (ESTIMATE) score and tumor purity score calculated by the ESTIMATE algorithm. The survival among two clusters were analyzed by Kaplan–Meier curves using “survminer” R package.

Cell culture and cell transfection

HNSCC cell lines CAL-27 (RRID: CVCL_1107) and HSC-6 (RRID: CVCL_A615) were cultured in Dulbecco's modified Eagle's medium (DMEM, Invitrogen, Grand Island, NY, USA) with 10% fetal bovine serum (FBS, Sigma-Aldrich) at 37℃ in an incubator with 5% CO2. For cell transfection experiments, CAL-27 and HSC-6 cells were seeded in 24-well plates for 24 h with Opti-medium before transfection. The cells were treated with siRNA using lipofectamine (ThermoFisher, China) according to the manufacturer’s instructions after growth to 70–80% puromycin (Sellek, USA) was used to select the stable transfected cells.

Patient samples

Paired tumor and peritumor tissues were collected from HNSCC patients. Peritumor samples were collected 2–5 cm from the edge of tumor tissue. All patients were surgically proven HNSCC, and the follow-up was performed by the Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University. Samples were obtained with informed consent, and the study was approved by the Institutional Ethics Review Board.

Cell proliferation assay

The Cell Counting Kit-8 (CCK-8, Dojindo, China) was used to measure proliferation ability of CAL-27 and HSC-6 cells. The cells were cultured in a 96-well plate after transfection. 100μL Opti-medium and 10ul CCK-8 solution were added to each well at 24, 48 and 72 h time points. After 30 min of incubation, the optical density (OD) value at 450 nm of each well was evaluated by microplate reader machine (BioTek, Vermont, USA). Experiments were repeated at least three times.

Transwell assay

Cell migration and invasion were evaluated by Transwell assay. For cell migration experiments, CAL-27 and HSC-6 cells were seeded in a 24-well plate after transfection. 300ul DMEM with 10% FBS was added to the 24-well plate in prior and 200ul cell suspension was added to the upper chambers containing serum-free medium after 1 h. Photographs of non-overlapping fields were taken after 18 h cell incubation in the present of 5% CO2 at 37℃. For cell migration experiments, 300ul DMEM with 10% FBS was added in the 24-well plate while Matrigel matrix (CORNING, USA) was added to the center of upper chamber at a concentration of 50ul/cm2 growth area. After Matrigel formed into a gel, 200ul cell suspension was added to the upper chambers containing serum-free medium. Photographs of non-overlapping fields were taken after 24 h cell incubation in the present of 5% CO2 at 37℃. Experiments were repeated at least three times.

Flow cytometry assay

CAL-27 and HSC-6 cells were washed with and resuspended in phosphate buffered solution. 7-aminoactinomycin D (7-AAD) (A1310, Life technologies) was used to exclude dead cells. For cell cycle analysis, cells were fixed with 4% paraformaldehyde (MA0192, Meilunbio) for 12 h at 4℃, permeabilized with 0.2% Triton X-100 (T9284, Sigma) in PBS for 15 min at room temperature, and then stained by antibodies to Ki67 (16A8, Biolegend) and further incubated with 0.1 μg μL-1 4’,6-diamidino-2-phenylindole (DAPI, 1306, Thermo Scientific) for 30 min at room temperature. For apoptosis analysis, cells were stained by AnnexinV (640,907, Biolegend) and 7-AAD. For intracellular reactive oxygen species (ROS) levels, cells were stained with antibodies to LSC phenotypic markers, and further stained by 5 μM Dihydroethidium (DHE, KGAF019, KeyGEN) for 30 min at 37 °C. For glucose uptake assay, glucose uptake was measured using the fluorescence-labeled deoxyglucose analog 2-(N-(7-nitrobenz-2-oxa-1,3-diazol-4-yl) amino)-2-deoxyglucose (2-NBDG, Apex Bio, USA). After the indicated treatment, cells were incubated with 2-NBDG for 1 h at 37 °C. Cell sorting and analysis were performed using either a flow cytometer (Attune NxT; Thermo Fisher) or sorter (InFlux Cell or FACSARIA III, BD Biosciences). Data analysis was performed using FlowJo software.

Immunofluorescence assay

Cells were incubated for 3 h with a Histone Rabbit mAb (Cell signal, #7631S). Incubations were performed at room temperature. The slides were collected and manually washed three times with Ventana buffer and three times with distilled water. Finally, the slides were counterstained for 2 min with 1ug/ml of 4,6-diamidino-2-phenylindole (#D1306, Life Technologies, Oregon, USA). γ-H2AX staining were used to indicate double-stranded DNA damage and apoptosis [25]. A proprietary image analysis system was used to quantify the number of positive cells over the total number of cells and the average signal emission/nucleus.

Western blot

Total protein was collected from CAL-27 and HSC-6 cells using RIPA lysis buffer (Beyotime, Shanghai, China), separated by 10% sodium dodecyl sulphate–polyacrylamide gel electrophoresis (SDS-PAGE) gels and then transferred to polyvinylidene fluoride (PVDF, Millipore) membranes. The proteins were incubated with primary antibodies overnight at 4 °C, and then incubated with secondary antibodies for 1 h at room temperature. An Enhanced Chemiluminescence (ECL) Detection Kit (Millipore) was used to visualize the immunoreactive proteins. The information of antibodies: p53 (1:1000); p21 (1:1000). The antibodies against PYGL (Abcam, ab227403), p53 (Cell signaling, #2524S), p21 (Cell Signaling, #2947S) were used for western blotting.

RNA extraction and qRT-PCR

TRIzol (Invitrogen) was used to extract total RNA from HSC-6 and CAL-27 cells and tissue samples according to the manufacturer’s instructions. The concentration and purity of total RNA were examined by UV spectrophotometric analysis at 260 nm. The cDNAs were synthesized using a reverse transcription kit (Promega, Madison, WI, USA) according to the manufacturer's instructions. The mRNA expression levels were measured using the SYBR Green PCR Master Mix (Applied Biosystems, Waltham, MA, USA) and the Applied Biosystems 7900HT sequence detection system (Applied Biosystems). And mRNA relative expression level was measured using the 2 − ΔΔCt method and normalized to glyceraldehyde 3-phosphate dehydrogenase (GAPDH).

PAS staining and glycogen assay

Glycogen level was measured using standardized periodic acid Schiff (PAS) staining technique by AB-PAS staining kit (Bioss). Cells were fixed with 4% paraformaldehyde for 15 min, incubated in 1% periodic acid for 5 min, rinsed in water, and placed in Schiff’s reagent for 10 min. Finally, cells were washed with water. Amylase was used in a set of experiments to verify that staining was specific for glycogen.

Detection of ROS

ROS levels were determined using MitoSOX Red (Applygen, China) as an indicator of mitochondrial superoxide. CAL-27 and HSC6 cells were treated with MitoSOX Red according to the instruction by manufacturer after transfection for 24 h. Cells were washed using PBS and analyzed by FACSCantoTM (BD Biosciences). Data analysis was performed using FlowJo software (Tree Star, Ashland, OR). Experiments were repeated at least three times.

Detection of GSH

GSH levels were determined using a colorimetric GSH assay kit according to the manufacturer’s instruction. Briefly, cells (10 µg) were mixed with 30 µl 5% metaphosphoric acid, and then frozen and thawed twice using liquid nitrogen and 37 °C water. The samples were centrifuged, and the supernatant was subjected to a GSH assay based on a kinetic enzymatic recycling method that detects the oxidation of GSH by 5,5′-dithiobis-2-nitrobenzoic acid (DTNB) and GSH reductase to measure the GSH content in cells74. The absorbance was measured at 412 nm with the Microplate Readers.

Xenograft tumor model

Female BALB/c nude mice (RRID: IMSR_JCL:JCL:mID-0001, 4-weeks-old, purchased from Gempharmatech-GD, Guangdong, China) were maintained in a specific pathogen-free room in the sterile animal facility. PYGL knock-down and control cells were cultured in 100 mm culture dishes and collected by trypsin. The cells were injected subcutaneously into the submandibular region of BALB/c nude mice at a density of 106 in 100 μl medium. The mice were randomly divided into four groups: for the PYGL KD group, the mice were injected with PYGL KD tumor cells. For the cisplatin chemotherapy group, mice were administered injection of cisplatin (0.2 ml/20 g/d) and NaCl (0.4 ml/20 g/d), The control mice were administered the same volume of NaCl (0.6 ml/20 g/d). Tumor volumes were measured twice a day and calculated according to the following formula: 0.5 × length × width2. The lung tissue was harvested for sectioning and slice culture to evaluate tumor metastases. All the section images were evaluated by two qualified pathologists in the Department of Pathology of the Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University. When the results reported by the two pathologists were found to be consistent, they were considered as the verified outcomes. However, in case of any inconsistencies, a third pathologist from the Department of Pathology would reevaluate the results. All the pathologists had obtained the Certificate of Licensed Practicing Physician (Medical examination and Pathology). Mice were euthanized after 60 days of observation. Our experimental procedures were approved by the Institutional Ethics Review Board.

Statistical analyses

Statistical analyses were performed with R (version 4.2.1) and GraphPad Prism 9.2. For comparisons between two groups, statistical significance was estimated using Wilcoxon test. For comparisons among three groups, Kruskal–Wallis test was applied. Pearson correlation analysis was used to determine the correlation between two variables. Survival analysis was performed by using a log-rank test. A p-value < 0.05 was considered statistically significant unless specified.


Heterogeneity among HNSCC driven malignant metastasis was related to cell metabolism reprogramming

As a first step to uncover the organization of cellular hierarchies in HNSCC, we re-analyzed the scRNA-seq profiles covering 25 primary and 8 metastatic HNSCC samples, and notably classified malignant cells, endothelial cells, fibroblast cells, monocytic cells, mast cells, T cells, and B/plasma cells (Fig. 2A, S2A and B). With a focus on the malignant population mainly triggering invasion and pathological state, malignant cells were extracted and re-clustered, while sample source was calculated for cluster identification. 5 clusters with high ratio of cells from primary cancer were named primary C1-C5, 2 clusters with abundant proportion of cells from metastatic tissue were named metastatic C1-C2, and the remain 2 clusters with hybrid sample origin were categorized as Mixed C1-C2 (Fig. 2B, S3A and B). Markers of different clusters were shown in Fig.S3C. The identification of cell clusters with different constitution revealed the existence of distinct cellular distribution in malignant cells, which provided a molecular basis for the known heterogeneity found within the malignant cells compartment [26]. Because metastatic clusters have more proportion of cells from metastatic tissue while primary clusters possess high ratio of cells from primary cancer, studying the heterogeneity between these two populations would provide cellular traits of tumor metastasis, which was considered as the behavior of later tumor period and associated with worse prognosis. Therefore, mixed clusters were discarded while characteristics of primary- and metastatic-specific populations were compared in the subsequent analysis. Using pseudobulk, a method for DEGs detection that avoids the interfere of several over-expressed genes and therefore reduces the false positives [24], we obtained the DEGs between two specific populations (Fig.S4).

figure 2

Cellular populations of HNSCC correlated with metabolic properties and survival. A Diffusion map of cells from all sc-RNA samples, colored by cell-type annotation. B Diffusion map of malignant cells colored by cell proportion annotation, including 9 clusters. C KEGG pathway enrichment analysis of DEGs between primary- and metastatic-specific populations. D GSEA analysis of “META_ACTIVATE” gene signatures between primary- and metastatic-specific populations, which were at the right and left side respectively. E AUC score of all cells from HNSCC samples. The threshold was marked by red bar and the cells colored by darked blue exceeded the threshold value. F Diffusion map based on the AUC score with cells exceeded the threshold value marked. G Relative abundance of different cell populations by gene expression deconvolution in each patient from METArisk-high and METArisk-low groups. Each bar represents an individual patient, and the distribution of colors throughout each bar represents the distribution of cells. H Kaplan–Meier analysis for survival probability of the two METArisk groups

Functional analysis based on pathway information from KEGG database illustrated that DEGs of metastasis-specific population were particularly enriched in several metabolism-related pathways including nucleotide metabolism, oxidative phosphorylation, purine metabolism, carbon metabolism and fatty acid metabolism (Fig. 2C), which indicated that cell metabolism reprogramming was supposed to be a magnitude explanation of HNSCC metastasis. Consequently, we intersected genes from KEGG metabolism-related pathways and genes expressed in HNSCC scRNA-seq profiles generated a set of genes named “META_ACTIVATE”. To investigate whether metabolism reprogramming was dynamic in metastasis population, GSEA was applied and the result showed that activity of most genes was inclined to activate in the metastasis-specific population (P.adjust = 0.038) (Fig. 2D). To quantify the metabolism activity in each cell, AUCell analysis, an efficient approach to identify cells with the META_ACTIVATE gene signature by evaluated threshold, was conducted, and we defined the cell with AUCell score exceeded than the threshold calculated as “META- active” cells, while other cells were defined as “META-silent” cells (Fig. 2E). The distribution of AUCell score was markedly hoisted and surpassed the threshold in metastatic C1-C2 among all of the malignant clusters, while AUCell score was inferior in other clusters (2F and S5A). The result represented that META-active cell significantly associated with metastasis, indicating that specific cell population could virtually influence the development of HNSCC. Additionally, 858 MRGs were screened out form the intersection between top marker genes of the “META-active” cells and META_ACTIVATE gene signature, which might serve as the potential metabolic biomarkers associated with tumor progression.

Meanwhile, expression profile of non-malignant cells from HNSCC microenvironment were integrated according to method depicted in the previous research on metabolic landscape of tumor microenvironment [23] for the downstream analyses.

Deconvolution of cell populations in HNSCC revealed active metabolic properties leading to poor clinical outcomes

We next sought to figure out how these defined HNSCC cell populations and the hierarchies which they are organized into relate to functional, biological and clinical properties of HNSCC. Single-cell RNA sequencing analysis described the biological pattern provided the expression profile of META-active cell, META-silent cell and non-malignance cell, which enabled us to perform CIBERSORTx [27], a scRNA-seq-based gene expression deconvolution method to infer the cellular hierarchy composition in bulk HNSCC transcriptomes as 486 patient samples from TCGA database to examine how the metabolic properties vary across patient samples and how they relate to molecular and clinical features of HNSCC at scale in large clinical datasets (Fig. 2G). Obviously, we revealed the significant heterogeneity of cellular hierarchy composition among patients. Termed as “METArisk”, the proportion of META-active cells in each sample was calculated to describe the heterogeneity of cellular hierarchy composition of each patient and two subgroups were identified according to the segmentation value generated by optimal cutoff analysis: the “METArisk-high” group with higher METArisk and the “METArisk-low” group with lower METArisk (Fig.S5B). Kaplan–Meier analysis showed remarkable difference in prognosis between METArisk-high and METArisk-low group, wherein METArisk-high was associated with worse prognosis and METArisk-low was associated with better prognosis (Fig. 2H), suggesting that exploring the heterogeneity of METArisk group would contribute to finding specific biochemical characteristic lead to poor prognosis.

PYGL was identified as a significant metabolic biomarker provoking high METArisk and poor prognosis.

To further explore the underlying mechanisms for the observed association of METArisk and clinical outcomes of HNSCC, 56 candidate biomarkers were screened out after the intersection was taken using 858 MRGs of the META_ACTIVATE gene signature, DEGs between the two METArisk groups and those between tumor and normal samples (Fig. 3A). In consideration of the link between prognosis and gene expression, machine learning was employed to select biomarker that was closely correlated with the patient survival from the 56 candidates. To select combination of machine learning methods with the best prediction ability, we counted C-index and finally chose SVM-RFE and Mutivariate Cox PH regression analysis (Fig.S6). SVM-RFE first screened 16 genes related with overall survival (Fig. 3B), and multivariate Cox PH regression analyses identified four genes independently associated with clinical risk evaluated by both the outcomes and the survival time, including EPHX3, FDCSP, FAM3B and PYGL (P < 0.05, and IDO1 was abandoned for P > 0.05) (Fig. 3C). To choose the metabolic biomarkers for intensive study, several analyses were conducted subsequently. Kaplan–Meier analysis showed that high expression of these genes led to better prognosis but for PYGL, which worsened the prognosis (Fig. 3D). Calculating the expression of four genes, PYGL was highly expressed in METArisk-high and tumor groups, while the others were highly expressed in METArisk-low and normal groups (Fig. 3E and S7). The different protein expression level of the four genes was detected by obtaining IHC staining image from the HPA database, which demonstrated that PYGL was highly expressed in HNSCC at translation level while the other was almost not expressed (Fig. 3F). RT-PCR experiment was conducted to verify the different expression of the four genes, which also exhibited the same variation (Fig. 3G). Our research findings along with the biological functions of the four metabolic biomarkers were summarized in Fig. 3H. Collectively, PYGL, the key element of the glycogen degradation process, was finally chose as the core biomarker of our research, for it was connected with high METArisk, led to poor prognosis, and was increasingly expressed in tumor at both transcriptional and translational level of HNSCC samples, which might sever as the driving factor for tumor-promoting cell metabolism reprogramming.

figure 3

Identification of PYGL as a significant metabolic biomarker. A Venn diagram among METArisk-related DEGs, tumor-related DEGs and MRGs. B SVM-RFE used to select features having best discriminative capability for patient’s prognosis of HNSCC. C Multivariate Cox regression analyses discovered DEGs that can serve as independent predictors of clinical outcomes. D Kaplan–Meier survival curves of the four genes selected above. E Boxplot depicted the expression level of the four genes in METArisk-high and METArisk-low groups. F Representative IHC images of the four genes from the HPA database. G qRT-PCR analysis of the four genes. Relative mRNA expression level of tumor tissue compared with paracancerous tissure were exhibited in bar chart (n = 3). H The summary image of the four genes with research findings along with the biological functions

PYGL was validated to stimulate HNSCC malignant behaviors and chemotherapy resistance.

To further explore the role of PYGL in HNSCC occurrence and development, we knocked down PYGL in HNSCC cell lines (HSC6 and CAL27) and verified the efficiency of transfection (Fig. 4A and B), and subsequently conducted a series of cell functional experiments. In cell proliferation assay, PYGL knock-down significantly suppressed HNSCC cell lines’ trend growth rate (Fig. 4C). In Transwell assay, migration (Fig. 4D) and invasion (Fig. 4E) of HNSCC cell lines was distinctly inhibited after PYGL knock-down. Immunofluorescence assay revealed that DNA damage occurred more frequently in PYGL knock-down group (Fig. 4F), and cell apoptosis analysis exposed that PYGL knock-down increased cell apoptosis rate (Fig. 4G). As the cellular alterations demonstrated in vitro, the expression of PYGL functionally promotes the malignant behavior of HNSCC, including promoting its growth, metastasis, invasion, and maintaining DNA integrity.

figure 4

HNSCC malignant behaviors suppressed after PYGL knock-down. A and B qRT-PCR (A) and WB (B) analysis of the transfaction efficiency (n = 3 for independent experiments). C CCK-8 assay were used to detect cell proliferation capacities (n = 3). D and E Transwell assay were performed to examine cell potential of migration (D) and invasion (E). F Immunofluorescence staining showed the DNA damage (stained by γ-H2AX, red) in cells. Nuclei were counterstained with DAPI (blue) (n = 3). G Cell apoptosis was measured by flow cytometry assay. Cell was stained by AnnexinV and SSA-C and apoptotic cell was emphasized (n = 4)

In addition to malignant behaviors, we also investigated the relationship between PYGL and chemotherapy resistance, another risk factor leading to poor prognosis in HNSCC. In accordance with ASCO Clinical Practice Guideline [28] and EHNS-ESMO-ESTRO Clinical Practice Guidelines [29], cisplatin was the first choice of chemotherapy to HNSCC, and consequently we implemented “Oncopredict” R package to predict IC50 of cisplatin in different groups. The METArisk-high group, with higher cellular hierarchy composition of META-active cells and up-regulated expression of PYGL, was correlated with higher IC50 of cisplatin (Fig. 5A). Splitting the HNSCC patients into subgroups according to the expression level of PYGL, higher IC50 of cisplatin was also observed in the PYGL-high group (Fig. 5B), and computer analysis showed a correlation with PYGL and drug resistance in the CARE database (CARE score < 0 in CCLE, CGP, CTRP respectively) (Fig. 5C), which indicated expression of PYGL was potential to induce chemotherapy resistance of cisplatin. Established by subcutaneously injecting HNSCC cell lines with PYGL knocked down into female BALB/c nude mice, xenograft tumor model was applied to validate the function of PYGL to cisplatin resistance of HNSCC. As Fig. 5D and E revealed, xenograft derived from PYGL knock-down cell lines was significantly diminished compared with the control, and cisplatin’s inhibition to HNSCC was also amplified at PYGL knock-down group. Furthermore, the effect of PYGL on HNSCC’s metastasis and its suppression by cisplatin was examined by determining the metastatic nodules in the lungs 60 days after inoculation, which illustrated that size of pulmonary metastatic nodules was suppressed while cisplatin’s function on metastasis suppression was increased in PYGL knock-down groups (Fig. 5F). In line with the cell function experiments in vitro, we demonstrated that the expression of PYGL was closely associated with HNSCC’s growth and metastasis in vivo, and further revealed its relationship with cisplatin’s resistance in the chemotherapy of HNSCC.

figure 5

Chemotherapy resistance and metastasis of HNSCC promoted by PYGL. A Prediction of cisplatin IC50 in METArisk-high and METArisk-low groups. B Prediction of cisplatin IC50 in high and low PYGL expression groups. C Computational analysis of resistance with PYGL of cisplatin in the CARE database. D Tumor growth was measured once a week in tumor-bearing nude mice stably transfected with HNSCC cells (n = 5). E The tumors were removed, and the tumor weight was analyzed after the mice were executed. F Metastasis of HNSCC was measured by the results of hematoxylin–eosin staining of mice lung tissue

TP53’s suppression was involved in PYGL’s promoting HNSCC malignancy

As previous studies have already proved that suppression of TP53 was closely related with tumor progression, metastasis and chemotherapy resistance [30, 31], we also discovered that low TP53 expression was associated with higher IC50 of cisplatin (Fig. 6A) and TP53 had negative correlation with PYGL at transcriptional level (R = -0.332, P = 3.38e-14) (Fig. 6B), which drew to a speculation that PYGL might promote HNSCC’s chemotherapy resistance by suppressing TP53’s expression. To further ascertain the regulatory relationship between PYGL and TP53, cell experiment in vitro was performed to demonstrate that PYGL knock-down activated the expression of TP53 along with its downstream target gene P21 (Fig. 6C and D). Simultaneously, TP53’s expression obviously increased after knock-down employed on both PYGL and TP53 (Fig. 6E), which indicated that TP53 was on the downstream of PYGL’s regulation pathway. Moreover, flow cytometry assay detected that PYGL knock-down facilitated TP53’s function of cell cycle checkpoint on proliferative malignant cells in HNSCC, evidenced by the significantly increasing proportion of cell stagnated in G0 stage (Fig. 6F and G). Basically, our findings verified the expression of PYGL suppressed TP53’s expression and function, leading to abnormal proliferation and chemotherapy resistance in HNSCC.

figure 6

PYGL promoted HNSCC malignancy by suppressing TP53 expression. A Prediction of cisplatin IC50 in high and low TP53 expression groups. B Pearson correlation analysis between PYGL and TP53 expression. C qRT-PCR of TP53 and P21 in PYGL knock-down groups (n = 3). D Western blot of p53 and p21 in PYGL knock-down groups. E Western blot of pygl and p53 in PYGL and TP53 knock-down groups. F Flow cytometry using DAPI and Ki67 for cell cycle phase ratio analysis in PYGL knock-down groups (n = 3). G Statistics of cell cycle phase ratios analyzed in Fig. 5F

Glycogen degradation and GSH metabolism participated in PYGL’s promotion of HNSCC.

As mentioned earlier, METArisk phenotype related to cell metabolism reprogramming was potential to classify inter-patient heterogeneity and clinical outcomes in HNSCC; hence we next sought to identify the metabolic characteristics that attributed to poor prognosis and correlated with PYGL’s expression. Employing gene set variation analysis (GSVA), we examined the activated discrepancy of 113 defined metabolism related pathways in PYGL-high and PYGL-low subgroups. Apart from glycogen degradation mediated directly by PYGL, several pathways including GSH metabolism, fructose and mannose metabolism, purine biosynthesis and cardiolipin metabolism were also up-regulated in PYGL-high group (Fig. 7A), and correlation analysis of these enriched pathways (Fig.S8) showed that PYGL-mediated glycogen degradation was significantly associated with GSH metabolism, wherein targeted analysis confirmed the positive correlation of the two pathways (R = 0.85, P < 2.2e-16) (Fig. 7B). To further determine the correlation between PYGL and GSH metabolism, we conducted serious correlation analysis between PYGL and genes of GSH metabolism pathway, which demonstrated the positive correlations between PYGL and nine genes motivating GSH metabolism at transcriptional level (R > 0.5, P < 2.2e-16) (Fig.S9). GSS and GCLM express enzymes that synthesize GSH; GSR, IDH1 and G6PD express enzymes promoting the transform progress from oxidized glutathione (GSSG) to GSH; GSTA1, GSTM3 and GSTM4 express the oxidoreductases of the redox reaction between GSH and peroxide; GPX2 expresses invertase catalyzing the biochemical reaction from GSH to GSSG, which serves as a significant biological progress for cells to dispose the majority of ROS. Results above all indicated that PYGL has significant correlation with GSH metabolism pathway. To explore the role of GSH metabolism in HNSCC, NMF cluster analysis based on the genes of GSH metabolism pathway were applied and HNSCC cohort was categorized into GSH-active cluster and GSH-silent cluster according to the distinct activation pattern of the pathway (Fig. 7C). Based on these two clusters, a series of analyses were conducted to investigate the impact of GSH metabolism on HNSCC. By using ESTIMATE algorithm, a method evaluating tumor microenvironment, the lower ESTIMATE scores representing for inferior immune infiltration, and the higher tumor purity scores suggesting superior malignancy, were both observed in GSH-active cluster, indicating that GSH metabolism pathway is associated with HNSCC malignancy. (Fig.S10A and B). Moreover, Kaplan–Meier analysis on two clusters also illustrated the relationship between GSH metabolism and poor prognosis of patients with HNSCC, as GSH-active cluster exhibited worse clinical outcomes than GSH-silent cluster (Fig. 7D). These results represented that the activation of GSH metabolism performed similar effect on tumor malignancy as PYGL activation, and also could lead to poor prognosis, illustrating that GSH probably in the same regulation pathway with PYGL in HNSCC.

figure 7

GSH metabolism correlated with PYGL-mediated glycogen degradation and poor prognosis. A GSVA between PYGL-low and PYGL-high group. B Correlation analysis between glycogen degradation and GSH metabolism. C Consensus map for NMF clustering. D Kaplan–Meier curves for survival probability of the two clusters. E The result of PAS and 2-NBDG assay in PYGL knock-down groups (n = 5). F Glycogen accumulation in PYGL knock-down groups was detected by standardized PAS staining. G The level of GSH and ROS in PYGL knock-down groups (n = 5). H ROS level was measured by ROS detection in PYGL knock-down groups

To further investigate the underlying regulatory relationship of PYGL-mediated glycogen degradation and GSH metabolism, we examined the variations of these two biochemical processes after PYGL knock-down. Standardized PAS staining and 2-NBDG detection revealed the increasing glycogen accumulation in PYGL knock-down groups (Fig. 7E and F), which indicated that glycogen degradation was blocked after the suppression of PYGL. Inhibition of glycogen degradation possibly led to reduction of glucose-6-phosphate (G6P) that was indispensable for the synthesis of GSH through pentose phosphate pathway, and decreasing GSH level was detected in PYGL knock-down groups (Fig. 7G). The consumption of ROS by GSH, an essential biological process maintaining the normal cells, was reported as an approach for malignant cells escaping the surveillance by TP53 [32]. By conducting ROS detection, we observed the rise of ROS after PYGL knock-down (Fig. 7G and H), which illustrated that PYGL probably suppressed TP53 through ROS consumption mediated by GSH metabolism and ultimately promoted the evolvement of HNSCC.


Cancer cells must react rapidly to internal and external stimuli to maintain high proliferation rates and survive in unfavorable environment with low oxygen, nutrition deficiency, and even chemotherapy drug intoxication. One way is to reprogram cell metabolism, thus affecting intra- and extra-cellular metabolites, which can have a major impact on gene expression, cellular differentiation and tumor microenvironment [9, 33]. Such metabolic reprogramming, especially in energy metabolism, has long been accepted as a hallmark and general phenomenon of tumors. For example, glycolysis has been found to significantly influence tumor development, metastasis and treatment susceptibility through a variety of biological processes [34]. Thus, the flexible and sophisticated metabolic network of cancer cells, as a hub for numerous cellular signaling pathways, may provide new therapeutic targets in the treatment of cancer [35]. Recent studies on metabolic reprogramming in HNSCC have provided promising potential targets in the treatment of HNSCC [36,37,38], demonstrating that comprehensively research on metabolic heterogeneity and relevant metabolic mechanisms is significantly demanded.

Primarily based on bulk RNA-seq, previous multi-patient sample transcriptomics studies on HNSCC mostly concentrated on screening prognostic genes [39] and predicting clinical outcomes [40], but with little concerns of intercellular heterogeneity. Simultaneously, single-cell sequencing studies on HNSCC mainly focused on functions of various cell component [41], discovery of new cell subsets [42], and investigation of intercellular heterogeneity [43], but were lack of the analyses between specific tumor cell subsets and patients prognosis for the limitation of small sample size. With combination of single-cell analysis and multi-patient sample sequencing, we firstly explored the relationship between specific cell subsets and prognosis in HNSCC, and HNSCC patients were classified from the perspective of unique metastasis-related malignant cell populations for further selection on therapeutic targets.

In our study, we first conducted single-cell RNA sequencing studies to investigate the metabolic heterogeneity in primary and metastatic samples from HNSCC patients. Combined with several functional analyses and AUCell algorithm, we revealed that the HNSCC metastases showed increased abundance of a specific cluster of malignant cells termed as META-active cells, which possessed high metabolic activity compared with the primary tumor. Analysis of patient-specific variation in hierarchy composition captured and integrated genomic profiles from single-cell and bulk RNA sequencing and clinical outcomes from large cohorts and functional properties of cancer metabolism within a single classification framework, which classified samples into METArisk-high group and METArisk-low group according to the proportion of METAactive malignant cells. METArisk-high group with high proportion of METAactive malignant cells showed lower overall survival, implying that metabolic heterogeneity might serve as a crucial factor affecting the prognosis of HNSCC. In order to figure out the biomarkers associated with the distinction of the hierarchy composition and clinical outcomes, we performed SVM-RFE and multivariate Cox regression analyses on the DEMRGs between the high and low METArisk groups. PYGL was finally selected as the key biomarker of our research, for it can lead to poorer prognosis, and was highly expressed both in tumor and METArisk-high group in transcriptional level, and also in HNSCC samples in translational level.

PYGL, located on chromosome 14q22.1 with 20 exons in total [44], is one of the genes related to hypoxia metabolism and was found to be up regulated in HNSCC [45]. PYGL is expressed as glycogen phosphorylase (GP), the key enzyme of glycogenolysis, which is responsible for the release of glycose-1-phosphate (G1P) from hepatic and muscle glycogen under physiological conditions [46]. In malignant cells, GP is involved in the glycogen catabolism and antioxidant defenses [47]. In previous studies, PYGL was reported as a gene signature derived from HNSCC, which defines the hypoxia ‘metagene’ [45]. It is also upregulated in several other cancers, such as seminoma, brain cancer and papillary renal cell carcinoma, as shown at oncomine website ( [48]. Previous researches also reported that PYGL could promote metastasis in pancreatic cancer [49], while disruption of PYGL induces necroptosis [50]. Moreover, inhibitors of glycogen degradation metabolism regulated by PYGL has already been discovered like CP91149, CP320626 and Flavoperidol, but neither of them has been applied in HNSCC treatment [51,52,53,54], implicating a great potential of PYGL targeting therapy. Despite these findings, the underlying molecular mechanisms between PYGL and HNSCC are yet to be clarified. Thus, we combined significant metabolic signatures aforementioned and performed further research to identify possible pathways.

Cell apoptosis and nuclear DNA damage remarkably increased while cell proliferation ability and the potential of migration and invasion diminished in PYGL knock-down HNSCC cells, which illustrated that PYGL can promote the malignancy of HNSCC. Drug resistance analysis and xenograft tumor model supported that PYGL enhanced the resistance of HNSCC to cisplatin, leading to its progression and metastasis. It was reported that expression of TP53 markedly enhanced the susceptibility to cisplatin and cisplatin-induced cell death in variable cancers [55,56,57], and we discovered that low TP53 expression in HNSCC was closely associated with higher IC50 of cisplatin while TP53 was proved to have negative correlation with PYGL in expression level. The following experiment confirmed the speculation that PYGL can promote HNSCC’s evolvement by suppressing TP53, which was functionally verified by the discovery that PYGL knock-down resulted in a higher proportion of cells in G0 phase and lower proportion of both G1/S/M and G2 phase.

Correlation analysis of enriched metabolic pathways based on GSVA illustrated that GSH metabolism was significantly correlated with glycogen degradation regulated by PYGL, which was simultaneously confirmed at the transcriptional level. NMF and ESTIMATE analysis further revealed the activation of GSH metabolism pathway was closely related to higher tumor malignance and poorer prognosis. GSH metabolism on tumor has been discussed in previous research and was certified to affect tumor in different ways. As an important antioxidant in organisms, GSH expends ROS to control oxidative stress in tumor and promote tumor development, which was demonstrated in breast cancer [58], prostate cancer [59], and liver cancer [60]. GSH can also affect the apoptotic process by regulating the anti-apoptotic proteins and caspase activity of the Bcl-2 family, which was certified in breast cancer [61]. Ferroptosis was detected to be suppressed by GSH simultaneously in liver cancer [62] and triple-negative breast cancer [63]. Additionally, GSH was proved to enhance cisplatin resistance of tumor by several pathways [64]. However, the mechanism of GSH on HNSCC was still not clearly determined. Here, we demonstrated that PYGL can promote HNSCC’s evolvement by activating GSH metabolism and downstream pathway. PYGL catalyzes glycogen phosphorylation, and the degradation metabolite G1P transforms into G6P, which produces NADPH from pentose phosphate pathway. With the participation of NADPH, GSH reductase (GR) can reduce GSSG to GSH [65], decreasing ROS in tumor cells. Previous researches proved cell death by the activation of p53 was closely correlated with the accumulation of cellular ROS [66, 67]. Besides, the reduction of ROS can suppress p53’s function and promote the progression of tumor [68, 69]. To sum up, PYGL activates GSH metabolism to reduce ROS level, which suppresses the function of TP53, and ultimately promotes the evolvement and decrease the chemosensitivity of HNSCC.

There are also some limitations in our study. For instance, single-cell samples can be further expanded to reduce selection bias and ensure the randomness. It’s also a better consideration to generalize our model for studies on other cancer, which can not only demonstrate its universality but also expand the study for excavating the metabolic pathways. Furthermore, generally recognized as a complex network, tumor development can be affected by multiple factors via the GSH/ROS/p53 pathway. PYGL knock-down is bound to change the expression of other molecules, which probably affects the function of p53 and thus influences the long-term efficacy. Finally, we were not able to collect and adjust all the potential confounding factors in our Kaplan–Meier analysis, which may cause bias in our survival analysis. In addition to our current discoveries, further systemic studies are demanded to clarify relational molecular mechanism and conduct corresponding clinical studies in the future.


Collectively, our study revealed cell metabolism reprogramming was a crucial risk factor for HNSCC through the discovery of a unique cluster of malignant cells with high metabolic activity in metastatic samples and the construction of METArisk phenotype which illustrated the composition of each patient’s cell hierarchy and correlated with the poor prognosis. Furthermore, analyses of the DEMRGs in METArisk phenotypes suggested PYGL, the key biomarker in glycogen degradation, was strongly potential to guide the development and drug resistance of HNSCC by the PYGL/GSH/ROS/p53 pathway, thereby setting the foundation for new clinical therapies of HNSCC in the future (Fig. 8).

figure 8

PYGL can serve as a therapeutic target for HNSCC

Availability of data and materials

The dataset supporting the conclusions of this article is available from the corresponding author upon request, and associated source codes is provided in



Head and neck squamous cell carcinoma


Sequence Read Archive


The Cancer Genome Atlas


Uniform manifold approximation and projection


Differential expressed genes


Kyoto Encyclopedia of Genes and Genomes


Metabolism-Related Genes


Gene Set Enrichment Analysis


Gene Ontology


Molecular Signatures Database


Area under the curve


Transcripts per million


Differential Expressed Metabolism-Related Genes


Support vector machine recursive feature elimination




Human Protein Atlas


Computational Analysis of Resistance


Non-negative matrix factorization




Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression


Dulbecco's modified Eagle's medium


Fetal bovine serum


Cell Counting Kit-8


Optical density


7-aminoactinomycin D




Reactive oxygen species




2-N-7-nitrobenz-2-oxa-1,3-diazol-4-yl amino-2-deoxyglucose


Sodium dodecyl sulphate–polyacrylamide gel electrophoresis


Polyvinylidene fluoride


Enhanced Chemiluminescence


Glyceraldehyde 3-phosphate dehydrogenase


Periodic acid Schiff


5,5′-dithiobis-2-nitrobenzoic acid


Gene set variation analysis


Oxidized glutathione




Glycogen phosphorylase




  1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians. 2018;68(6):394–424.

  2. Vigneswaran N, Williams MD. Epidemiologic trends in head and neck cancer and aids in diagnosis. Oral Maxillofac Surg Clin North Am. 2014;26(2):123–41.

    PubMed  PubMed Central  Google Scholar 

  3. Hanahan D. Hallmarks of Cancer: New Dimensions. Cancer Discov. 2022;12(1):31–46.

    CAS  PubMed  Google Scholar 

  4. Zanotelli MR, Zhang J, Reinhart-King CA. Mechanoresponsive metabolism in cancer cell migration and metastasis. Cell Metab. 2021;33(7):1307–21.

    CAS  PubMed  PubMed Central  Google Scholar 

  5. El Hassouni B, Granchi C, Vallés-Martí A, Supadmanaba IGP, Bononi G, Tuccinardi T, et al. The dichotomous role of the glycolytic metabolism pathway in cancer metastasis: Interplay with the complex tumor microenvironment and novel therapeutic strategies. Semin Cancer Biol. 2020;60:238–48.

    PubMed  Google Scholar 

  6. Wang Z, Dong C. Gluconeogenesis in Cancer: Function and Regulation of PEPCK, FBPase, and G6Pase. Trends Cancer. 2019;5(1):30–45.

    CAS  PubMed  Google Scholar 

  7. Li Z, Kang Y. Lipid Metabolism Fuels Cancer’s Spread. Cell Metab. 2017;25(2):228–30.

    CAS  PubMed  Google Scholar 

  8. Wang B, Wu L, Chen J, Dong L, Chen C, Wen Z, et al. Metabolism pathways of arachidonic acids: mechanisms and potential therapeutic targets. Signal Transduct Target Ther. 2021;6(1):94.

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Pavlova NN, Thompson CB. The Emerging Hallmarks of Cancer Metabolism. Cell Metab. 2016;23(1):27–47.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Pascual G, Avgustinova A, Mejetta S, Martín M, Castellanos A, Attolini CS, et al. Targeting metastasis-initiating cells through the fatty acid receptor CD36. Nature. 2017;541(7635):41–5.

    CAS  PubMed  Google Scholar 

  11. Jiang M, Wu N, Xu B, Chu Y, Li X, Su S, et al. Fatty acid-induced CD36 expression via O-GlcNAcylation drives gastric cancer metastasis. Theranostics. 2019;9(18):5359–73.

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Prendeville H, Lynch L. Diet, lipids, and antitumor immunity. Cell Mol Immunol. 2022;19(3):432–44.

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Cao Y. Adipocyte and lipid metabolism in cancer drug resistance. J Clin Investig. 2019;129(8):3006–17.

    PubMed  PubMed Central  Google Scholar 

  14. Dai W, Xu L, Yu X, Zhang G, Guo H, Liu H, et al. OGDHL silencing promotes hepatocellular carcinoma by reprogramming glutamine metabolism. J Hepatol. 2020;72(5):909–23.

    CAS  PubMed  Google Scholar 

  15. Qin C, Yang G, Yang J, Ren B, Wang H, Chen G, et al. Metabolism of pancreatic cancer: paving the way to better anticancer strategies. Mol Cancer. 2020;19(1):50.

    PubMed  PubMed Central  Google Scholar 

  16. Martínez-Reyes I, Chandel NS. Cancer metabolism: looking forward. Nat Rev Cancer. 2021;21(10):669–80.

    PubMed  Google Scholar 

  17. Vander Heiden MG, DeBerardinis RJ. Understanding the Intersections between Metabolism and Cancer Biology. Cell. 2017;168(4):657–69.

    CAS  PubMed  Google Scholar 

  18. Huang Y, Bell LN, Okamura J, Kim MS, Mohney RP, Guerrero-Preston R, et al. Phospho-ΔNp63α/SREBF1 protein interactions: bridging cell metabolism and cisplatin chemoresistance. Cell cycle (Georgetown, Tex). 2012;11(20):3810–27.

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Tao X, Lu Y, Qiu S, Wang Y, Qin J, Fan Z. AP1G1 is involved in cetuximab-mediated downregulation of ASCT2-EGFR complex and sensitization of human head and neck squamous cell carcinoma cells to ROS-induced apoptosis. Cancer Lett. 2017;408:33–42.

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Kraus D, Reckenbeil J, Veit N, Kuerpig S, Meisenheimer M, Beier I, et al. Targeting glucose transport and the NAD pathway in tumor cells with STF-31: a re-evaluation. Cell Oncol (Dordr). 2018;41(5):485–94.

    CAS  PubMed  Google Scholar 

  21. Faubert B, Solmonson A, DeBerardinis RJ. Metabolic reprogramming and cancer progression. Science (New York, NY). 2020;368(6487):eaaw5473.

  22. Zeng AGX, Bansal S, Jin L, Mitchell A, Chen WC, Abbas HA, et al. A cellular hierarchy framework for understanding heterogeneity and predicting drug response in acute myeloid leukemia. Nat Med. 2022;28(6):1212–23.

    CAS  PubMed  Google Scholar 

  23. Xiao Z, Dai Z, Locasale JW. Metabolic landscape of the tumor microenvironment at single cell resolution. Nat Commun. 2019;10(1):3763.

    PubMed  PubMed Central  Google Scholar 

  24. Junttila S, Smolander J, Elo LL. Benchmarking methods for detecting differential states between conditions from multi-subject single-cell RNA-seq data. Brief Bioinform. 2022;23(5):bbac286.

  25. Ivashkevich A, Redon CE, Nakamura AJ, Martin RF, Martin OA. Use of the γ-H2AX assay to monitor DNA damage and repair in translational cancer research. Cancer Lett. 2012;327(1–2):123–33.

    CAS  PubMed  Google Scholar 

  26. McGranahan N, Swanton C. Clonal heterogeneity and tumor evolution: past, present, and the future. Cell. 2017;168(4):613–28.

    CAS  PubMed  Google Scholar 

  27. Newman AM, Steen CB, Liu CL, Gentles AJ, Chaudhuri AA, Scherer F, et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat Biotechnol. 2019;37(7):773–82.

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Koyfman SA, Ismaila N, Crook D, D’Cruz A, Rodriguez CP, Sher DJ, et al. Management of the Neck in Squamous Cell Carcinoma of the Oral Cavity and Oropharynx: ASCO Clinical Practice Guideline. J Clin Oncol. 2019;37(20):1753–74.

    PubMed  PubMed Central  Google Scholar 

  29. Machiels JP, René Leemans C, Golusinski W, Grau C, Licitra L, Gregoire V. Squamous cell carcinoma of the oral cavity, larynx, oropharynx and hypopharynx: EHNS-ESMO-ESTRO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2020;31(11):1462–75.

    PubMed  Google Scholar 

  30. Tang Q, Su Z, Gu W, Rustgi AK. Mutant p53 on the Path to Metastasis. Trends Cancer. 2020;6(1):62–73.

    PubMed  Google Scholar 

  31. Cao X, Hou J, An Q, Assaraf YG, Wang X. Towards the overcoming of anticancer drug resistance mediated by p53 mutations. Drug Resist Updat. 2020;49: 100671.

    PubMed  Google Scholar 

  32. Cao J, Liu X, Yang Y, Wei B, Li Q, Mao G, et al. Decylubiquinone suppresses breast cancer growth and metastasis by inhibiting angiogenesis via the ROS/p53/ BAI1 signaling pathway. Angiogenesis. 2020;23(3):325–38.

    PubMed  Google Scholar 

  33. Kroemer G, Pouyssegur J. Tumor cell metabolism: cancer’s Achilles’ heel. Cancer Cell. 2008;13(6):472–82.

    CAS  PubMed  Google Scholar 

  34. Ganapathy-Kanniappan S, Geschwind JF. Tumor glycolysis as a target for cancer therapy: progress and prospects. Mol Cancer. 2013;12:152.

    PubMed  PubMed Central  Google Scholar 

  35. Tan YT, Lin JF, Li T, Li JJ, Xu RH, Ju HQ. LncRNA-mediated posttranslational modifications and reprogramming of energy metabolism in cancer. Cancer Commun (Lond). 2021;41(2):109–20.

    PubMed  Google Scholar 

  36. Chang S, Yim S, Park H. The cancer driver genes IDH1/2, JARID1C/ KDM5C, and UTX/ KDM6A: crosstalk between histone demethylation and hypoxic reprogramming in cancer metabolism. Exp Mol Med. 2019;51(6):1–17.

    PubMed  Google Scholar 

  37. Chen F, Fan Y, Cao P, Liu B, Hou J, Zhang B, et al. Pan-cancer analysis of the prognostic and immunological Role of HSF1: a potential target for survival and immunotherapy. Oxid Med Cell Longev. 2021;2021:5551036.

    PubMed  PubMed Central  Google Scholar 

  38. Li Z, Chen S, Feng W, Luo Y, Lai H, Li Q, et al. A pan-cancer analysis of HER2 index revealed transcriptional pattern for precise selection of HER2-targeted therapy. EBioMedicine. 2020;62: 103074.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Liu X, Liu P, Chernock RD, Kuhs KAL, Lewis JS Jr, Luo J, et al. A prognostic gene expression signature for oropharyngeal squamous cell carcinoma. EBioMedicine. 2020;61: 102805.

    PubMed  PubMed Central  Google Scholar 

  40. Schinke H, Shi E, Lin Z, Quadt T, Kranz G, Zhou J, et al. A transcriptomic map of EGFR-induced epithelial-to-mesenchymal transition identifies prognostic and therapeutic targets for head and neck cancer. Mol Cancer. 2022;21(1):178.

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Puram SV, Tirosh I, Parikh AS, Patel AP, Yizhak K, Gillespie S, et al. Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer. Cell. 2017;171(7):1611-24.e24.

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Kürten CHL, Kulkarni A, Cillo AR, Santos PM, Roble AK, Onkar S, et al. Investigating immune and non-immune cell interactions in head and neck tumors by single-cell RNA sequencing. Nat Commun. 2021;12(1):7338.

    PubMed  PubMed Central  Google Scholar 

  43. Davis-Marcisak EF, Sherman TD, Orugunta P, Stein-O’Brien GL, Puram SV, Roussos Torres ET, et al. Differential variation analysis enables detection of tumor heterogeneity using single-cell RNA-sequencing data. Can Res. 2019;79(19):5102–12.

    CAS  Google Scholar 

  44. Luo X, Hu J, Gao X, Fan Y, Sun Y, Gu X, et al. Novel PYGL mutations in Chinese children leading to glycogen storage disease type VI: two case reports. BMC Med Genet. 2020;21(1):74.

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Winter SC, Buffa FM, Silva P, Miller C, Valentine HR, Turley H, et al. Relation of a hypoxia metagene derived from head and neck cancer to prognosis of multiple cancers. Cancer Res. 2007;67(7):3441–9.

    CAS  PubMed  Google Scholar 

  46. Zhao CY, Hua CH, Li CH, Zheng RZ, Li XY. High PYGL Expression Predicts Poor Prognosis in Human Gliomas. Front Neurol. 2021;12: 652931.

    PubMed  PubMed Central  Google Scholar 

  47. Martínez-Navarro FJ, Martínez-Morcillo FJ, López-Muñoz A, Pardo-Sánchez I, Martínez-Menchón T, Corbalán-Vélez R, et al. The vitamin B6-regulated enzymes PYGL and G6PD fuel NADPH oxidases to promote skin inflammation. Dev Comp Immunol. 2020;108: 103666.

    PubMed  Google Scholar 

  48. Xu J, Liu X, Liu X, Zhi Y. Long noncoding RNA KCNMB2-AS1 promotes the development of esophageal cancer by modulating the miR-3194-3p/PYGL axis. Bioengineered. 2021;12(1):6687–702.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Ji Q, Li H, Cai Z, Yuan X, Pu X, Huang Y, et al. PYGL-mediated glucose metabolism reprogramming promotes EMT phenotype and metastasis of pancreatic cancer. Int J Biol Sci. 2023;19(6):1894–909.

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Zhang DW, Shao J, Lin J, Zhang N, Lu BJ, Lin SC, et al. RIP3, an energy metabolism regulator that switches TNF-induced cell death from apoptosis to necrosis. Science. 2009;325(5938):332–6.

    CAS  PubMed  Google Scholar 

  51. Khan T, Sullivan MA, Gunter JH, Kryza T, Lyons N, He Y, et al. Revisiting Glycogen in Cancer: A Conspicuous and Targetable Enabler of Malignant Transformation. Front Oncol. 2020;10: 592455.

    PubMed  PubMed Central  Google Scholar 

  52. Oikonomakos NG, Schnier JB, Zographos SE, Skamnaki VT, Tsitsanou KE, Johnson LN. Flavopiridol inhibits glycogen phosphorylase by binding at the inhibitor site. J Biol Chem. 2000;275(44):34566–73.

    CAS  PubMed  Google Scholar 

  53. Ma D, Wang J, Zhao Y, Lee WN, Xiao J, Go VL, et al. Inhibition of glycogen phosphorylation induces changes in cellular proteome and signaling pathways in MIA pancreatic cancer cells. Pancreas. 2012;41(3):397–408.

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Shapiro GI. Preclinical and clinical development of the cyclin-dependent kinase inhibitor flavopiridol. Clin Cancer Res. 2004;10(12 Pt 2):4270s-s4275.

    CAS  PubMed  Google Scholar 

  55. Ahn HJ, Kim KS, Shin KW, Lim KH, Kim JO, Lee JY, et al. Ell3 stabilizes p53 following CDDP treatment via its effects on ubiquitin-dependent and -independent proteasomal degradation pathways in breast cancer cells. Oncotarget. 2015;6(42):44523–37.

    PubMed  PubMed Central  Google Scholar 

  56. Matsuhashi N, Saio M, Matsuo A, Sugiyama Y, Saji S. The evaluation of gastric cancer sensitivity to 5-FU/CDDP in terms of induction of apoptosis: time- and p53 expression-dependency of anti-cancer drugs. Oncol Rep. 2005;14(3):609–15.

    CAS  PubMed  Google Scholar 

  57. Li Y, Chen F, Chen J, Chan S, He Y, Liu W, et al. Disulfiram/Copper Induces Antitumor Activity against Both Nasopharyngeal Cancer Cells and Cancer-Associated Fibroblasts through ROS/MAPK and Ferroptosis Pathways. Cancers (Basel). 2020;12(1):138.

  58. Harris IS, Treloar AE, Inoue S, Sasaki M, Gorrini C, Lee KC, et al. Glutathione and thioredoxin antioxidant pathways synergize to drive cancer initiation and progression. Cancer Cell. 2015;27(2):211–22.

    CAS  PubMed  Google Scholar 

  59. Cramer SL, Saha A, Liu J, Tadi S, Tiziani S, Yan W, et al. Systemic depletion of L-cyst(e)ine with cyst(e)inase increases reactive oxygen species and suppresses tumor growth. Nat Med. 2017;23(1):120–7.

    CAS  PubMed  Google Scholar 

  60. Anasagasti MJ, Martin JJ, Mendoza L, Obrador E, Estrela JM, McCuskey RS, et al. Glutathione protects metastatic melanoma cells against oxidative stress in the murine hepatic microvasculature. Hepatology. 1998;27(5):1249–56.

    CAS  PubMed  Google Scholar 

  61. Jiang Y, Xu H, Wang J. Alantolactone induces apoptosis of human cervical cancer cells via reactive oxygen species generation, glutathione depletion and inhibition of the Bcl-2/Bax signaling pathway. Oncol Lett. 2016;11(6):4203–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Zhang X, Du L, Qiao Y, Zhang X, Zheng W, Wu Q, et al. Ferroptosis is governed by differential regulation of transcription in liver cancer. Redox Biol. 2019;24: 101211.

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Zhang Z, Lu M, Chen C, Tong X, Li Y, Yang K, et al. Holo-lactoferrin: the link between ferroptosis and radiotherapy in triple-negative breast cancer. Theranostics. 2021;11(7):3167–82.

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Brozovic A, Ambriović-Ristov A, Osmak M. The relationship between cisplatin-induced reactive oxygen species, glutathione, and BCL-2 and resistance to cisplatin. Crit Rev Toxicol. 2010;40(4):347–59.

    CAS  PubMed  Google Scholar 

  65. González-Domínguez Á, Visiedo F, Domínguez-Riscart J, Ruiz-Mateos B, Saez-Benito A, Lechuga-Sancho AM, et al. Blunted Reducing Power Generation in Erythrocytes Contributes to Oxidative Stress in Prepubertal Obese Children with Insulin Resistance. Antioxidants (Basel). 2021;10(2):244.

  66. Liu T, Sun L, Zhang Y, Wang Y, Zheng J. Imbalanced GSH/ROS and sequential cell death. J Biochem Mol Toxicol. 2022;36(1): e22942.

    CAS  PubMed  Google Scholar 

  67. Aquilano K, Baldelli S, Cardaci S, Rotilio G, Ciriolo MR. Nitric oxide is the primary mediator of cytotoxicity induced by GSH depletion in neuronal cells. J Cell Sci. 2011;124(Pt 7):1043–54.

    CAS  PubMed  Google Scholar 

  68. Xing Y, Wei X, Liu Y, Wang MM, Sui Z, Wang X, et al. Autophagy inhibition mediated by MCOLN1/TRPML1 suppresses cancer metastasis via regulating a ROS-driven TP53/p53 pathway. Autophagy. 2022;18(8):1932–54.

    CAS  PubMed  Google Scholar 

  69. Chibaya L, Karim B, Zhang H, Jones SN. Mdm2 phosphorylation by Akt regulates the p53 response to oxidative stress to promote cell proliferation and tumorigenesis. Proc Natl Acad Sci U S A. 2021;118(4):e2003193118.

Download references


We thank Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology; Nanfang Hospital, Southern Medical University, and Zhongshan School of Medicine, Sun Yat-sen University for general supporting. We thank the reviewers and editors of this journal for their nice help during the manuscript preparation. We thank Dr. Jianming Zeng (University of Macau), and all the members of his bioinformatics team, biotrainee, for generously sharing their experience and codes. We thank the use of the biorstudio high performance computing cluster ( at Biotrainee and the Shanghai HS Biotech Co., Ltd for conducting the research reported in this paper. We deeply appreciate the support from Miss. Meihan Chen (Sun Yat-sen University), Mr. Ziwen Hui (Sun Yat-sen University), and the marvelous companion.


This work was supported by the National Natural Science Foundation of China (82001109, 82270975), Guangdong Basic and Applied Basic Research Foundation (2023A1515010205, 2019A1515110026), the Science and Technology Planning Project of Guangzhou, China (202206080009), the State Key Laboratory of Oral Diseases (SKLOD) Open Fund (SKLOD2022OF06), Open Funding of Guangdong Provincial Key Laboratory of Stomatology (KF2021120103), Special Funds for the Cultivation of Guangdong College Students' Scientific and Technological Innovation (“Climbing Program” Special Funds) [grant number pdjh2023b0017], and Undergraduate Training Program for Innovation and Entrepreneurship of Sun Yat-sen University (202310783).

Author information

Authors and Affiliations



J.G., X.X. and B.Y. performed study concept and design; X.Tao., B.C. and B.Y. conceived and supervised the project; J.G., Q.G, X.X., C.H., X.Lu, X.Liu., K.W. and B.Y. performed development of methodology and writing, review and revision of the paper; J.G., X.X., Q.G and C.H. planned, performed the experiments; J.G., Q.G, X.X., C.H., X.Lu., Y.Li. and Z.L. provided acquisition, analysis and interpretation of data, and statistical analysis; X.Lu., Y.Li., Z.L., X.Tang. and Y. Liang. processed and typeset the figures; C.H. carried out the experiments and analyses for the revised manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Xiaoan Tao, Bin Cheng or Bo Yang.

Ethics declarations

Ethics approval and consent to participate

Animal experiments in this study were approved by the Institutional Animal Care and Use Committee (IACUC), Sun Yat-Sen University. (Ethical approval code number: SYSU-IACUC-2021–80188).

Collection of patient samples and related experiments in this study were approved by the Medical Ethics Committee of Hospital of Stomatology, Sun Yat-Sen University. (Ethical approval code number: KQEC-2023–36-02).

Consent for publication

All authors have read the manuscript and agree to publish.

Competing interests

The authors declare no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Dr. Bo Yang is the lead contact.

Supplementary Information

Additional file 1: Figure. 

S1. A flowchart summarizing the study.  Figure S2. Distinct cellular construction of primary and metastatic samples. Figure S3. Cellular hierarchy of malignant cell including nine clusters. Figure S4. Volcano plot showing expression of DEGs (|logFC|>1 and adjusted P <0.05)between primary- and metastatic-specific populations. Figure S5. Distribution of AUC score and METArisk score. Figure S6. A total of 69 kinds of prediction models and further calculated the C-index of each model across validation datasets. Figure S7. Boxplot depicted the expression level in tumor and normal groups. Figure S8. Correlation analysis of pathways selected by GSVA. Figure S9. Pearson correlation analysis between PYGL and functional genes in GSH metabolism. Figure S10. ESTIMATE analysis illustrating ESTIMATE score (A) and tumor purity score (B) of GSH-active and GSH-silence cluster.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guan, J., Xu, X., Qiu, G. et al. Cellular hierarchy framework based on single-cell/multi-patient sample sequencing reveals metabolic biomarker PYGL as a therapeutic target for HNSCC. J Exp Clin Cancer Res 42, 162 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: