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. 2024 Jul 23;14:16972. doi: 10.1038/s41598-024-65090-5

Comprehensive analysis of endoplasmic reticulum stress related signature in head and neck squamous carcinoma

Yu Miao 1,#, Qiaorong Chen 2,3,4,#, Xinyu Liu 4,#, Jian Bu 4, Zhuoqi Zhang 4, Tongjing Liu 1, Zhenjie Yue 1, Lizhen Huang 1, Shuaishuai Sun 1, Hao Li 5, Ankui Yang 2,3, Zhongyuan Yang 2,3,✉, Cuifang Chen 1,✉
PMCID: PMC11266686  PMID: 39043683

Abstract

Head and neck squamous carcinoma (HNSC) is a prevalent malignant disease, with the majority of patients being diagnosed at an advanced stage. Endoplasmic reticulum stress (ERS) is considered to be a process that promotes tumorigenesis and impacts the tumor microenvironment (TME) in various cancers. The study aims to investigate the predictive value of ERS in HNSC and explore the correlation between ERS-related genes and TME. A series of bioinformatics analyses were carried out based on mRNA and scRNA-seq data from the TCGA and GEO databases. We conducted RT-qPCR and western blot to validate the signature, and performed cell functional experiments to investigate the in vitro biological functions of the gene. We identified 63 ERS-related genes that were associated with outcome and stage in HNSC. A three-gene signature (ATF6, TRIB3, and UBXN6) was developed, which presents predictive value in the prognosis and immunotherapy response of HNSC patients. The high-risk group exhibited a worse prognosis but may benefit from immunotherapy. Furthermore, there was a significant correlation between the signature and immune infiltration. In the high-risk group, fibroblasts were more active in intercellular communication, and more T cells were observed at the end of the sequential phase. The genes in the ERS-related signature were overexpressed in HNSC cells, and the knockdown of TRIB3 significantly inhibited cell proliferation and migration. This study established a novel ERS-related signature that has potential implications for HNSC therapy and the understanding of TME.

Keywords: Endoplasmic reticulum stress, Head and neck squamous carcinoma, Prognosis, TME, Immunotherapy response

Subject terms: Cancer, Computational biology and bioinformatics, Oncology

Introduction

Head and neck squamous carcinoma (HNSC), as a prevalent malignancy worldwide, is characterized by a high degree of aggressiveness and an unfavorable prognosis1. A significant proportion of HNSC patients are diagnosed at an advanced stage and often experience recurrence despite comprehensive multidisciplinary treatments2,3. Notably, the use of immune checkpoint inhibitors has emerged as a promising therapeutic strategy, resulting in improved outcomes in recent years4. Recent research findings suggest that reshaping the tumor microenvironment (TME) may enhance the body's response to immunotherapy5,6, but the mechanisms regulating TME in HNSC still require exploration.

The endoplasmic reticulum (ER) is the largest organelle in cells and is involved in the synthesis and transport of many substances7. Disruption of the ER's normal functioning leads to misfolded proteins accumulating and eventually triggers the endoplasmic reticulum stress (ERS)8. ERS has been reported in multiple types of tumors and is considered a crucial factor in tumorigenesis9. ERS in cancer cells can influence malignant progression by regulating three signaling pathways (ATF6, IRE1α, and PERK signaling pathways)10. In HNSC, ERS is considered as a critical player in carcinogenesis11,12. Recent studies have highlighted the effect of ERS in HNSC cells on the TME and immunotherapy response13,14. However, the predictive value of ERS in HNSC remains to be explored.

In the study, we performed Weighted Gene Co-expression Network Analysis (WGCNA) and differential expression analysis on 258 ERS-related genes and identified 63 genes associated with outcome and stage in HNSC. The three-gene ERS-related signature (comprising ATF6, TRIB3, and UBXN6) is established. The high-risk group displayed a poorer prognosis, but they may benefit from immunotherapy. CIBERSORT and ESTIMATE analyses were performed and results showed that there was a significant correlation between the signature and immune infiltration. To gain further insight into the TME, we integrated single-cell data from three GEO datasets (GSE162025, GSE150430, GSE150321). The findings presented that the high-risk group had more malignant cells and fewer T/NK cell infiltrations. Moreover, in the high-risk group, fibroblasts were more active, and T cells were mostly at the end of the time sequence. Finally, we validated the expression of the genes (ATF6, TRIB3, UBXN6) and further explored the function of TRIB3 in HNSC. In summary, we developed a novel ERS-related signature and explored the effect of the signature in HNSC.

Methods

mRNA data acquisition and processing

Data on mRNA and clinical characteristics of the TCGA cohort were obtained from the TCGA database. (https://tcga-data.nci.nih.gov/), which includes 547 samples (501 primary tumor samples, 2 metastasis samples, and 44 normal samples). On the basis of the AJCC stage, all patients are categorized as early stage (stages I and II) or advanced stage (stages III and IV). The mRNA data and clinical characteristics of the validation cohort (GSE65858) are acquired from the GEO database (https://www.ncbi.nlm.nih.gov/geo/), which includes 270 tumor samples and downloaded using R package GEOquery. The human ERS-related genes are taken from the MSigDB database (http://www.gseamsigdb.org/gsea/msigdb), and 258 genes are used in following study after removing duplicates and two microRNA genes (MIR199A1 and MIR200C, Table S1). R package DESeq2 is used for differential expression analysis and count data from the TCGA-HNSC cohort15, and genes with a p value less than 0.05 were considered as differentially expressed genes.

Weighted correlation network analysis (WGCNA)

WGCNA is an algorithm to group genes with similar expression patterns, analyzing the relationship between gene modules and phenotypes by converting gene expression into gene modules16. R package WGCNA is performed to divide ERS-related genes into different gene modules and select the most relevant gene modules with phenotypes.

Establishment and validation of the ERS-related signature

LASSO-Cox regression (performed by R package glmnet) is chosen to overcome the multicollinearity problem between variables and identify important predictors. In the regression, the independent variable is the normalized expression matrix of selected genes, the dependent variable is the patient's survival status and survival time, and the penalty regularization parameter λ is determined via the ten-fold cross validation. Multiple Cox regression is utilized to further discriminate the genes with prognosis value by R package survival, and the results are visualized by forest plot (ggforest function in R package survival). Then the ERS-related prognostic gene signature is constructed based on corresponding regression coefficients and normalized mRNA expression of the genes in multiple Cox regression. R package survival and survminer are utilized to overall survival and visualization of the results. The nomogram and calibration curves were constructed by R package rms.

Tumor mutation burden analysis

Somatic mutation data of the HNSC cohort is acquired from the TCGA database (including 510 samples and clinical information in 495 patients was consistent with the TCGA-HNSC cohort). The mutational landscape, tumor mutation burden (TMB) calculation, and visualization of protein domains mutation sites are implemented in R package maftools17.

The assessment of immune infiltration

The CIBERSORT method in R18 is utilized to evaluate the relative abundance of 22 types of human immune cells in mRNA samples in the HNSC cohort. An evaluation of immune infiltration, stromal content, and tumor purity can be performed with R package ESTIMATE19.

scRNA-seq data processing, Gene Set Variation Analysis (GSVA), trajectory analysis and cell–cell communication

R package Seurat and harmony were performed for integrating and clustering three scRNA-seq datasets (GSE162025, GSE150430, GSE150321, which included 25 patients with nasopharyngeal cancer and 2 patients with laryngeal cancer.) from GEO database (https://www.ncbi.nlm.nih.gov/geo/). We utilized the Harmony package to integrate single-cell data and remove batch effects between datasets. R package COSG was used to identified marker genes in each group20. Risk score was computed by the average risk score of all cells in the sample, and then divided into risk groups by median. GSVA was performed to quantify pathway activation. Trajectory analysis was also performed using R package monocle2. Cellchat method was performed to visualize intercellular communications21.

Cell culture

Human normal and HNSC cell lines (NOK, SAS, UM1, CAL27, CAL33) were purchased from iCell Bioscience Inc. (Shanghai, China) and cultured in DMEM ( Invitrogen, CA, USA) with 10% FBS ( Sigma-Aldrich, MO, USA) and 1% penicillin–streptomycin (Gibco, NY, US). All cell lines were authenticated by Short Tandem Repeat (STR) profiling and kept in a humidified atmosphere of 5% CO2 at 37 °C.

Total RNA isolation, reverse transcription and real-time quantitative PCR

Trizol kit (Invitrogen, US) is chosen to isolate total cellular RNA, and the concentration and purity of total RNA are measured by absorbance at 260/280 nm. Total RNA is reverse transcribed using a reverse transcription kit (Yishan Science, Shanghai, China). Quantitative Real-Time PCR (qRT-PCR) is performed with 2 × RealStar Green Fast Mixture (Genstar, Beijing, China). The expression of target genes is normalized to the mRNA expression of GAPDH, and the 2 − ΔΔCT method is used to calculate fold changes. Primers sequences of target genes and GAPDH are presented as follow:

GAPDH forward TGACTTCAACAGCGACACCCA
GAPDH reverse CACCCTGTTGCTGTAGCCAAA
ATF6 forward TCCTCGGTCAGTGGACTCTTA
ATF6 reverse CTTGGGCTGAATTGAAGGTTTTG
TRIB3 forward GCCTTTTTCACTCGGACCCAT
TRIB3 reverse CAGCGAAGACAAAGCGACAC
UBXN6 forward GGAGCGCATTAACTGCCTG
UBXN6 reverse GCTCAGCACGTAGAACTCCTC
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Western blot

Total protein is harvested using RIPA buffer (Beyotime, Jiangsu, China) and quantified by the bicinchoninic acid (BCA) protein Assay Kit (Beyotime, Jiangsu, China). Equal volume of samples is electrophoretically separated using 10% SDS-PAGE and then transferred to a PVDF membrane (Millipore, Bedford, MA, USA). Subsequently, the PVDF membrane is incubated with the primary antibody overnight at 4 °C and then incubated with the second antibody for 1.5h at room temperature. Finally, enhanced chemiluminescence assays are used to detect the signal. Primary antibodies are listed below: TRIB3(Abcam, Cambridge, UK), UBXN6(Proteintech, Wuhan, China), α-tubulin (Used as the loading control, Proteintech, Wuhan, China). The original western blot images in this study all were provided in Figure S1.

SiRNA transfection

The transfection was performed when cells reached 50% confluence in a 6-well culture dish. SiRNA was transfected in cells with jetPRIME transfection reagent (polyplus-transfection, New York, USA), and the medium is replaced with DMEM(+10%FBS) after 8h. After 48h, cells are collected for further assays. The sequences of siRNAs (Si#1 and Si#2) as follows:

SiTRIB3#1 GACAACUUAGAUACCGAGCGUTT
ACGCUCGGUAUCUAAGUUGUCTT
SiTRIB3#2 GAUCUCAAGCUGUGUCGCUUUTT
AAAGCGACACAGCUUGAGAUCTT
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Colony-forming assay

Cells are digested and counted in an automated cell counter (Nexcelom, Lawrence, USA). Then 800 cells were seeded into a 6-well plate and cultured for 7–10 days. 4% paraformaldehyde was performed to fix colonies (10 min), and the colonies were stained with 0.01% crystal violet solution for 10 min. Finally, we clean the plate with water and captured it with the camera.

Wound healing assay

When the cells are full of the 6-well plates, we straightly scratch three lines with a 200 μL pipette tip in the well. After scratching, we wash the cells with 1xPBS to remove any floating cells, observe them under a microscope, and photographs were taken in 0h and 24h.

CCK-8 proliferation assay

1000 cells per well were seeded in a 96-well plate and incubated with 100μL DMEM(+ 10%FBS). Every day 10 μl CCK-8 (Takara, Tokyo, Japan) was added in each well of the plate. Then the 96-well plate was incubated for 2h and measured the OD value at 450nm.

Statistical analysis and graphs plotting

Statistical graphs are plotted by R package ggpubr, R package ggplot2, R package corrplot, and GraphPad Prism (version 8.0). All statistical analyses were performed in R (venison 4.2.1) and GraphPad Prism. Student's t-test(two-tail) is used for paired samples, and non-parametric Wilcoxon rank-sum test is used for unpaired samples. Pearson analysis was used to assess the correlation. Significance in survival analysis was assessed using the Log-Rank test, and the Gehan-Breslow method was employed when survival curves intersected. P values less than 0.05 (*p < 0.05) and 0.01 (**p < 0.01) were considered significant.

Results

Identifying clinical phenotypes associated genes by Weighted Gene Co-expression Network Analysis (WGCNA)

To identify genes associated with pathological stage and outcome from the endoplasmic reticulum stress (ERS)-related genes (n = 258), we conducted WGCNA in TCGA-HNSC cohort (n = 501). A soft-thresholding power of 5 was chosen based on the scale-free R2 > 0.9 (Fig. 1A), and five significant modules were identified (Fig. 1B). After evaluating the module-phenotype relationships, we found that MEyellow and MEturquoise modules were most relevant to the pathological stage (0.15, **p = 8e−4) and outcome (0.12, **p = 0.009) among the five modules, respectively (Fig. 1C). These two modules contained a total of 105 genes, and 63 genes showed significant differential expression between cancer and normal samples (Fig. 1D), which were used for further analyses (Table S1).

Figure 1.

Figure 1

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Identifying clinical phenotypes associated genes by WGCNA. (A) Identifying soft-threshold power in WGCNA. (B) Cluster Dendrogram of five gene modules in WGCNA. (C) Module-phenotype relationships between five gene modules and phenotypes (stage and outcome). (D) Venn diagram of differentially expressed genes in MEyellow and MEturquoise modules.

Establishment of the ERS-related risk signature

To establish a prognostic marker for HNSC based on an ERS-related genes, we conducted LASSO-Cox regression analysis on 63 genes after WGCNA screening. Thirteen genes with nonzero coefficients were identified after ten-fold cross-validation (Fig. 2A,B, Table S1). We ultimately identified three independent prognosis factors after multivariate Cox regression analysis (*p < 0.05): ATF6 (HR 1.55, 95% CI 1.04–2.29), TRIB3 (HR 1.19, 95% CI 1.22–1.40), and UBXN6 (HR 0.70, 95% CI 0.49–0.99) (Fig. 2C). To establish a clinician user-friendly scoring system, we developed a risk signature based on the coefficients of the three genes in a multivariate cox analysis: Risk Scores = ATF6 exp*0.436295 + TRIB3 exp*0.176763 + UBXN6 exp*-0.363159. Additionally, we constructed a heatmap plot of the expression of these three genes with phenotype data (including gender, age, stage, and outcome) to present the change trends of phenotypes with increasing risk scores (Fig. 2D). The results indicate that this ERS-related risk signature may serve as a valuable prognostic marker for HNSC.

Figure 2.

Figure 2

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Establishment of an ERS-related risk signature. (A) Visualization of partial likelihood deviance in LASSO. (B) The minimum value and the optimal λ in LASSO. (C) Forest plot of the result in multivariate Cox regression analysis. (D) Heatmap of the expression of genes in ERS-related signature (ATF6, TRIB3, UBXN6) with phenotype data (including gender, age, stage and outcome).

Validation of the prognostic ERS-related signature

To investigate the prognostic potential of the risk model, we conducted survival analyses on both the TCGA cohort and Validation GEO cohort (GSE65858). The results indicate that the high-risk group presents a poorer overall survival compared to the low-risk group in both the TCGA cohort (**p < 0.0001, Fig. 3A) and the Validation cohort (*p = 0.01, Fig. 3B). To further assess the clinical significance of the risk score, we conducted a multivariable Cox analysis including clinical features, which revealed that the risk score is an independent risk factor for HNSC (p < 0.01**, Figure S2). In addition, we constructed a nomogram using clinical data such as age, gender, pathological stage, and risk scores from the TCGA-HNSC cohort (Fig. 3C), which showed good predictive performance, as indicated by the calibration curves (Fig. 3D,E).

Figure 3.

Figure 3

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Validation of the prognostic value in ERS-related signature. (A,B) Survival curves of risk groups and overall survival in TCGA HNSC cohort (A) and GEO cohort (B). (C) Nomogram for predicting prognosis in HNSC. (D-E) Calibration curves for the nomogram in 1 year (D) and 5 years (E).

Mutation profiles in the ERS-related signature in HNSC

To further investigate the mutational landscape of the signature in HNSC, we evaluated mutations in the TCGA-HNSC cohort. The findings indicate that mutations were more prevalent in the high-risk group (Fig. 4A,B). TP53 was found to be the most frequently mutated gene in both groups, with a mutation rate of 65% in the low-risk group and 75% in the high-risk group. Moreover, we found differences in the mutation hotspots between these two groups, with missense mutation in the low-risk group (p.R175H, Fig. 4C) and nonsense mutation in the high-risk group (p.R213*, Fig. 4D). Tumor mutation burden (TMB) is considered a valuable indicator of immunotherapy response, and high TMB has been associated with improved PFS and OS after immunotherapy22. According to the results, the high-risk group had a higher TMB (Fig. 4E) and risk scores were associated with TMB score (positive correlation, R = 0.16, p < 0.01, Fig. 4F), indicating that the high-risk group is more likely to benefit from immunotherapy. The analysis of the two melanoma immunotherapy cohorts also confirmed that the high-risk group has a better prognosis following immunotherapy (Fig. 4G).

Figure 4.

Figure 4

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Mutation profiles in ERS-related signature in HNSC. (A-B) Diagram of Mutation profiles in the low- (A) and high-risk group (B). (C,D) Lollipop mutation diagrams of TP53 in the low- (C) and high-risk group (D). (E) TMB between the in the high- and low-risk group. (F) The correlation between risk score and TMB score. (G) Survival analysis of different risk groups in the immunotherapy cohort.

The association between the ERS-related signature and immune infiltration in HNSC

Numerous studies indicate that ERS is closely related to the TME23. For further evaluating the relationship between immune infiltration and the signature, we assessed the relative abundance of 22 different immune cell types (by CIBERSORT in R) in the TME between the high- and low-risk group in the TCGA-HNSC cohort. The result shows that these two groups significantly differ in NK cells and T cells (Fig. 5A). The Immune infiltration assessment (by R package ESTIMATE) presents the immune scores is negatively linked with risk scores (**p = 0.0073, Fig. 5B), but there are no correlations between risk scores and stromal scores as well as tumorpurity scores (also evaluated by ESTIMATE, Fig. 5C,D), which might suggest the ERS-related signature mainly affects the immune components in HNSC. NK cells and CD8 + T cells are primary tumor-killing cells in the immune system. Risk scores is negative correlation with CD8 + T cells and activated NK cells (Fig. 5E,F) and positive correlation with resting NK cells (Fig. 5G). Finally, we evaluated the relevance between the mRNA expression of genes in the signature (ATF6, TRIB3, UBXN6) and the relative abundances of immune cells. The results indicate the expression of genes is extensively correlated with the relative contents of immune cells, and the mRNA expression of ATF6 and UBXN6 is significantly associated with the relative contents of activated NK cells and CD8 + T cells (Fig. 5H, Figure S3).

Figure 5.

Figure 5

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The assessment of immune infiltration in HNSC. (A) Relative abundance of 22 types of immune cells between low- and high-risk groups in HNSC. (B–E) Correlation plots of the association between risk score and immune score (B), CD8 + T cells (C), activated NK cells (D) and resting NK cells (E). (F) Bubble diagram of the association between mRNA expression of each gene in ERS-related signature (ATF6, TRIB3, UBXN6) and relative abundances of 22 types of immune cells. * means p value less than 0.05, **means p value less than 0.01.

Single cell RNA sequence (scRNA-seq) profiling

For further evaluating the effect of the signature on TME, we integrated scRNA-seq data from three datasets (GSE162025, GSE150430, GSE150321), clustered subpopulations (Fig. 6A), and identified marker genes in each cell subtype (Fig. 6B). The analysis of the expression of three genes in the signature presented that ATF6 was primarily expressed in endothelial cells, UBXN6 was mainly expressed in fibroblasts, and TRIB3 uniformly expressed among different cell types (Fig. 6C). Furthermore, the examination of the proportion of each cell subtype in high- and low-risk groups revealed an increase in malignant tumor cells and a decrease in T/NK cell infiltration in the high-risk group (Fig. 6D), which may suggest that the high-risk group is correlated with a higher degree of malignancy. We further sub-grouped the T/NK subtypes and found that CD8+T cells were significantly increased in the low-risk group (Fig. 6E–G). Additionally, we conducted GSVA to explore the activation of pathways in CD8+ T cells between the high- and low-risk groups. The results indicated that the complement pathway, cell cycle pathway, and Toll-like receptor (TLR) pathway were highly activated in the high-risk group (Fig. 6H).

Figure 6.

Figure 6

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Single cell RNA sequence (scRNA-seq) profiling. (A) The UMAP plot provides an annotation and color codes for the different cell types present in tumors and adjacent normal tissues. (B) Marker genes in each cluster. (C) The expression of genes in the ERS-related signature in each cluster. (D) Histograms shows the percentage of cell types between high- and low-risk groups. (E) The UMAP plot shows the clustering of T/NK cells.(F)Marker genes for the clustering of T/NK cells. (G) Histograms shows the proportion of different T/NK cell clusters in the high-risk and low-risk groups. (H) GSVA shows the enrichment of specific pathways in CD8+ T cells between high-(red) and low-risk(blue) groups.

Cell–cell communications and trajectory analysis

To further investigate intercellular interactions in the tumor microenvironment, we conducted an analysis of the communication network among cell subtypes. The findings showed that fibroblasts were significantly more active in the high-risk group (Fig. 7A–D), which suggests a potential role for fibroblasts in promoting malignant transformation. In addition, we performed trajectory analysis of CD8+ T cells across all samples, which revealed a differentiation process consisting of three stages and seven subsets (Fig. 7E). Notably, T cells in the high-risk group were primarily located at the later stages of the differentiation process (Fig. 7F,G), indicating a possible association with functional exhaustion, in contrast to the low-risk group.

Figure 7.

Figure 7

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Cell–cell communications and trajectory analysis. (A–D) The visualization of interactions of different cell subtypes between low- (A,B) and high-risk (C,D) groups. (E) The state, clusters and pseudotime of CD8+ Tcells. (F,G) The dot plot diagram (F) and density-distribution map (G) of T cells between high- and low-risk groups.

In vitro validation of the genes in HNSC cell lines

To validate the expression and function of the prognostic signature genes (ATF6, TRIB3, UBXN6), we performed RT-qPCR to examine their mRNA expression levels. The results demonstrated that these genes were highly expressed in tumor cells, indicating their potential as HNSC prognostic markers (Fig. 8A–C). Since the expression and function of ATF6 have been previously studied in HNSC13,24, we focused on the protein expression of TRIB3 and UBXN6. The results showed that TRIB3 was significantly highly expressed in tumor cells compared to normal cells (Fig. 8D), whereas there no differential expression of UBXN6 (Figure S4). To explore the function of TRIB3 in HNSC, we knocked down TRIB3 using siRNA and conducted cell function experiments in SAS and CAL33 cells. The knockdown of TRIB3 significantly inhibited cell clone formation capacity and proliferation of both SAS and CAL33 cells, as shown by clone formation assays (Fig. 8F) and CCK8 assays (Fig. 8G,H). The wound healing assay revealed a decrease in cell migration ability in both SAS and CAL33 cells upon down-regulation of TRIB3 expression (Fig. 8I).

Figure 8.

Figure 8

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In vitro validation of ERS-related signature in HNSC cell lines. (A–C) The mRNA expression of ATF6(A), TRIB3(B) and UBXN6(C) between normal and tumor cells. (D) The protein expression of TRIB3 in normal and tumor cells. (E) Knockdown of TRIB3 in mRNA (upper panel) and protein (lower panel) levels. (F) Images and quantification of colony assays in SAS and CAL33 cells. (G,H) The CCK-8 assay in SAS (G) and CAL33 (H) cells. (I) The wound healing assay in CAL33 and SAS cells. Data are presented as the mean ± SD.

Discussion

Endoplasmic reticulum stress (ERS) is a common pathological mechanism observed in various malignancies and is strongly associated with tumor growth and progression25. ERS in tumor cells can trigger the activation of several proteins, including ATF6, IRE1α, PERK, and TRIB3, and increased expression of these proteins can facilitate tumor cell adaptation to ERS23,26,27. Our study shows that risk scores increase with the mRNA expression of ATF6 and TRIB3, suggesting that risk scores might reflect the degree of ERS in HNSC samples to some extent. Growing evidence indicates that ERS is closely associated with the tumor microenvironment (TME), and persistent activation of ERS in tumor can affect immune infiltration28. Our study suggests that the ERS-related signature significantly correlates with immune cell composition and number in HNSC patients (Figs. 5A, 6D), thereby supporting the relationship between ERS and TME. Additionally, ERS is known to regulate tumor cell invasion and metastasis in HNSC, as well as affect the TME by exosome secretion13,29. A recent study has shown that statins can enhance the efficacy of anti-PD-L1 agents for HNSCC by regulating ERS14. Overall, our findings highlight the close relationship between HNSC and ERS, with ERS-related genes playing a crucial role in tumor progression and TME modulation.

CD8 + T cells and NK cells are main immune cells in the tumor microenvironment (TME) responsible for eliminating tumor cells through diverse mechanisms. In this investigation, we have determined that risk scores exhibit a negative correlation with the proportional abundance of CD8 + T cells and activated NK cells in HNSC samples (Fig. 5C,D), suggesting that ERS-related genes might impede anti-tumor immunity in HNSC by affecting these cell types. Moreover, we observed an inverse relationship between the risk scores and the relative abundance of activated NK cells, while an opposite trend was observed for resting NK cells (Fig. 5D,E). These results imply that ERS-related genes in HNSC may affect NK cell activation, although further research is warranted to validate this hypothesis.

In numerous types of cancers, cancer-associated fibroblasts (CAFs) have been recognized as key mediators of proliferation, drug resistance, and immunosuppression30,31. In our investigation, we noted a significant enhancement of cell communication by tumor-associated fibroblasts in the high-risk group. Additionally, the high-risk group exhibited a greater number of malignant cells and fewer T-cells, with most of the latter being at the end of the time sequence. These results suggest CAFs might play a crucial role in the malignant progression of HNSC. However, further experimentation is necessary to verify these findings.

Tumor mutational burden (TMB) is defined as the number of mutations per DNA megabase (Mut/Mb)32, and high TMB usually means more neoantigens for T-cells to recognize and a immunotherapy response33. In the study, we observed that the high-risk group had a higher median TMB (Fig. 4E), and risk scores were positively associated with TMB (Fig. 4F), indicating that the high-risk group might benefit from ICIs. Although single-cell sequencing results show higher CD8+ T cell infiltration in the low-risk group, cellular pathway and pseudotime analyses indicate that the high-risk group experiences higher levels of CD8 T cell exhaustion and apoptosis (Figs. 6H, 7G). Without immunotherapy, the prognosis for the high-risk group might be poorer (Fig. 3A), but the more severe immunosuppressive microenvironment in the high-risk group could provide a better response to ICIs (Fig. 4G). This suggests that patients with high-risk HNSC may need to receive ICIs earlier to achieve optimal prognosis.

Interestingly, we also found that the low-risk group had a lower relative abundance of regulatory T cells (Treg) compared to the high-risk group (Fig. 5A), and the mRNA expression of CTLA4 (also known as CD152) in HNSC samples showed the same trend (Figure S5). CTLA4 is mainly expressed on Treg and other activated T cells and plays a vital role in Treg-mediated immunosuppression34,35. These findings might predict the response of anti-CTLA4 agents in immunotherapy to some extent, but further studies are necessary. Moreover, we evaluated the somatic mutations of genes in the ERS-related signature (ATF6, TRIB3, and UBXN6) and found that mutations were present at low levels in these genes (less than 1%, Figure S6), indicating that these genes maintain DNA stability in HNSC.

Tribbles Pseudokinase 3 (TRIB3), a member of the tribbles subfamily, is a gene family with evolutionary conservation36. It serves as a signaling hub for various signaling pathways and cellular processes, including endoplasmic reticulum stress (ERS) 37. In recent years, TRIB3 has emerged as a crucial factor in tumorigenesis and is overexpressed in several tumors, playing a vital role in growth, proliferation, invasion, and metastasis27. In our study, we conducted mRNA and protein expression validation in HNSC cell lines and performed gene silencing experiments to confirm that TRIB3 can contribute to cell proliferation and migration (Fig. 8). These findings provide further support and validation for previous research38. Moreover, TRIB3 is believed to be linked to AKT signaling in HNSC38,39, but further investigation is necessary to elucidate the underlying mechanisms.

There are some limitations in our study. Firstly, the investigation mainly relies on publicly available datasets, and further experimental validation is necessary to consolidate the findings. Secondly, although we conducted extensive validation of the score we proposed, it is undeniable that in the WGCNA analysis, the module-trait correlations are low, which necessitates further verification. Additionally, the research merely confirms the mRNA and protein expression of genes within the signature and conducts cell functional experiments of TRIB3 in HNSC cells. As such, the molecular mechanisms through which the genes modulate ERS and TME in HNSC remain elusive and require further exploration.

Conclusions

In this study, we developed an ERS-related signature consisting of ATF6, TRIB3 and UBXN6, and evaluated its potential for predicting prognosis and response to immunotherapy in HNSC. Our findings indicate that the signature is closely associated with immune infiltration. In the high-risk group, fibroblasts exhibited increased activity in cell-to-cell communication and T cells tended to accumulate at the end of the time sequence. We also demonstrated that genes in the ERS-related signature were overexpressed in HNSC and that the knockdown of TRIB3 effectively suppressed cell proliferation and migration. Overall, our study suggests that this novel ERS-related signature may provide insight into the treatment of HNSC and enhance our understanding of the relationship between ERS-related genes and the TME in HNSC.

Supplementary Information

Supplementary Figure S1. (486.1KB, docx)
Supplementary Figure S2. (3.2MB, tif)
Supplementary Figure S3. (3.5MB, tif)
Supplementary Figure S4. (310.9KB, tif)
Supplementary Figure S5. (842.7KB, tif)
Supplementary Figure S6. (2.9MB, tif)
Supplementary Table S1. (18.8KB, xlsx)

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 82072981 and 82272649); Guangdong Basic and Applied Basic Research Foundation (2022A1515110033) and Open Project Fund of the Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People’s Hospital (202011–201).

Author contributions

M.Y. designed the study. L.X.Y., C.Q.R. performed the experiments. C.C.F., M.Y. assessed the accuracy of results. C.Q.R. wrote the first paper draft of the manuscript, C.C.F. and Y.Z.Y. provided assistance during writing process. Y.A.K., Y.Z.Y. critically reviewed and edited the manuscript. All authors reviewed the manuscript.All authors reviewws the manuscript.

Data availability

All data presented in this study are included in the article/supplementary material. Further data requests are available by contacting the corresponding authors.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's note

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These authors contributed equally: Yu Miao, Qiaorong Chen and Xinyu Liu.

Contributor Information

Zhongyuan Yang, Email: yangzhy@sysucc.org.cn.

Cuifang Chen, Email: 785910176@qq.com.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-65090-5.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Figure S1. (486.1KB, docx)
Supplementary Figure S2. (3.2MB, tif)
Supplementary Figure S3. (3.5MB, tif)
Supplementary Figure S4. (310.9KB, tif)
Supplementary Figure S5. (842.7KB, tif)
Supplementary Figure S6. (2.9MB, tif)
Supplementary Table S1. (18.8KB, xlsx)

Data Availability Statement

All data presented in this study are included in the article/supplementary material. Further data requests are available by contacting the corresponding authors.

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