DOI: https://doi.org/10.21203/rs.3.rs-620847/v1
Background: Head and neck squamous cell carcinoma (HNSCC) is an extremely heterogeneous malignant cancer with poor prognosis. Pyroptosis is defined as a novel inflammation-dependent programmed cell death. However,the pyroptosis-associated genes expression in HNSCC and their relationship with prognosis is still indistinct.
Material and Methods: We acquired HNSCC patients’ mRNA expression information from TCGA and GEO publicly available. We compared tumor issues and adjacent normal tissues in terms of the gene expression, for identifying differentially expressed genes (DEGs). Based on these genes, we established a risk signature by the LASSO Cox regression in the TCGA cohort and validated in a GEO cohort. We also verified the levels of relevant mRNA expression in the model by RT-qPCR. Eventually, functional enrichment approach was carried out to explore the potential mechanisms.
Results: Our team found a total of 18 differentially expressed genes (DEGs) between HNSCC and healthy sample, and 4 DEGs displayed a remarkable association with the overall survival (OS) (P< 0.05). A 4-gene signature was constructed, presenting beneficial forecast power both in TCGA and GEO cohort. Our team categorized cases into a group with high risk and another group with low risk in the light of the average risk value of the 4-gene feature. Cases in the low risk group displayed a notably greater OS compared with the high risk one (P< 0.01). The Cox regression study demonstrated the independent forecast capability of the risk score. The receiver operating characteristic approach facilitated the verification of the forecast function of the gene signature. Posterior to verification, the 4 genes were aberrantly expressed in HNSCC and healthy samples.Functional study displayed that these groups registered diverse immunity conditions.
Conclusions: Pyroptosis-associated genes paly an vital role in the prognosis of HNSCC and could be an potential therapeutic target.
HNSCC marks the 6th most commonly seen malignant cancer across the globe which originates from the nasal cavity, larynx, hypopharynx, oropharynx, nasopharynx, oral cavity, and salivary gland[1, 2]. Owing to the absence of effective methods in early diagnosis,most of the HNSCC patients are already at an advanced stage when diagnosed,and will suffer from recurrence after treatment ,with a poor survival rate of 35%[3].The multimodal strategies included surgery, radiotherapy and chemotherapy are the main treatments for HNSCC. In spite of recent improvements in therapies, the survival rate has been slow to improve[4] .Therefore, novel therapeutic targets are urgently required to bring better result for HNSCC patients ,and it is necessary to develop dependable new prognostic models to bring more feasibility for targeted therapies.
Pyroptosis is a cell death triggered by caspase-1/4/5/11 and results in cell swelling, chromatin fragmentation ,plasma membrane cleavage, and intracellular proinflammatory release[5]. More and more studies showed that pyroptosis is vital for the cancer progress.Some previous studies reported that some key components of pyroptosis,such as inflammatory vesicles, proinflammatory cytokines ,and gasdermin proteins are closely related to tumourigenesis, invasion, and metastasis[6, 7]. A few latest studies confirmed that pyroptosis play a critical role in tumour immune microenvironment and antitumour processes as well[8, 9]. However, it remains unclear whether pyroptosis has an effect on the development of HNSCC.
In this research,we conducted an systematic research to identify the pyroptosis-associated gene expression in healthy tissues and HNSCC tissues,and the DEGs were adopted to establish a model of prognosis to reveal the prognostic role of those genes. The predictive ability of the model was validated by GEO data,and the expression of mRNA of the pyroptosis-associated genes in our model was validated by RT-qPCR. Eventually, our research investigated the potential mechanisms by functional enrichment method.
The clinical information and the mRNA expression of 528 HNSCC sufferers were obtained from the TCGA data center (https://portal.gdc.cancer.gov/),and another 96 HNSCC sufferers from GEO cohort (GSE31056) were used as the verification set. Our research was conducted without ethical examine given that the entire data were accessible openly. Our team chose an overall 33 pyroptosis-associated genes as per past research[7, 10].
Our team compared the levels of pyroptosis-related genes expression of cancer and neighboring healthy samples to determine the DEGs via the "limma" R package[11]. The DEGs standard registered a false discovery rate (FDR) below 0.05. The DEGs are as described bellow: * P < 0.05, ** P < 0.01, and *** P < 0.001. A PPI network regarding the DEGs was established via STRING, version 11.0 (https://string-db.org/).Cox study facilitated the capability examination of pyroptosis-associated genes to forecast the OS. Benjamini-Hochberg modulated p-scores were adopted to diminish FDR. The "glmnet" R package was applied to implemented a LASSO Cox regression in order to determine whether DEGs could forecast the OS and HNSCC case conditions[12]. The best score of the penalty parameter (λ), correlating the lowest partial possible abnormality, was determined by cross-verification at 10 folds. Our team computed the risk score by the formula below: risk score = esum (the standardized expression level of every gene× the regression coefficient). Afterwards, our team applied the mid-value risk score as the criterion for categorizing sufferers into the two above mentioned groups. Out team described the gene distribution in these groups via implementing PCA by the "stats" R package[13]. The "surv_cutpoint" function of the "survminer" R package was adopted to analyze the survival in order to determine the ideal cut-off values. Subsequently, our team leveraged the "survivalROC" R package to carry out time-dependent ROC study to assess whether the genetic feature could forecast or not [14]. Afterwards, our research applied the univariable and multivariable Cox regression method to identify whether the risk score enabled the independent forecast of prognosis on OS. For the validation, a HNSCC cohort from the GEO (GSE31056) was included in the analysis. The risk score was computed via the identical formula in the TCGA cohort.The sufferers in the GSE31056 cohort were also classified into two groups likewise on account of the mid-value risk score of TCGA, and these groups were adopted for verifying the gene signature.
Our team implemented the GO enrichment and the KEGG pathway method of the DEGs in these groups with the "clusterProfiler" R package and |log2 (fold-change)| ≥ 1 and FDR < 0.05 were deemed as standards of these DEGs[15]. The "gsva" R package was adopted on the ssGSEA (single-sample gene set enrichment analysis) to compute the clustering value of immune cells and pathways associated with immunity[16].
Our team chose 15 HNSCC samples and 10 healthy samples from the First Hospital of Jiaxing between 2020 and 2021. This research was approved by the Ethics Committee of the First Hospital of Jiaxing. All people offered informed consents. Each sample was put in liquid nitrogen and reserved at − 80°C directly.
After trizol extracted the entire RNA from samples, it was turned into cDNA by reverse transcription.PCR was implemented via TB Green®Premix Ex Taq™II kit (Takara, China).The reactive activity is demonstrated as follows: half a minute at 94°C, half a minute at 58°C, one minute at 72°C, forty times.GAPDH served as interior control.The comparative level of expression was identify via 2 − ΔΔ CT.
Student's t-test was applied to contrast the gene expressions within cancer samples and neighboring healthy samples. Our team implemented the Mann-Whitney test to contrast the enrichment values of immune cells and pathways associated with immunity in these groups. The log-rank test and Kaplan-Meier approach were utilized to describe the survival curves. Afterwards, our team carried out Cox regression study to reveal the OS-predicting factors. Our research applied all statistical observation in R (Version 4.0.3). A two-tailed P value below 0.05 was important on statistics. The overall flow diagram of the study is shown in Fig. 1.
We compared the expression levels of 33 pyroptosis-associated genes between 44 healthy and 502 cancer samples from TCGA cohort,and we found 18 differentially expressed genes (DEGs)(P<0.05).Among them,17 genes(AIM2、CASP1、CASP5、 CASP6、 CASP8、GSDMB、 GSDMD、GSDME、IL1B、NLRC4、NLRP1、NLRP6、NLRP7、 NOD1、 PLCG1、PYCARD 、TNF) were upregulated while only 1 gene(ELANE) was downregulated in tumor group.The heatmap presented the expression levels of these genes (Fig. 2A).To further investigate the interacions of these genes,we used a protein-protein interaction (PPI) method,and the outcomes were presented in Fig. 2B. Figure 2C showed the associations between these pyroptosis-associated genes(red: positive association; blue: negative association).
Our team conducted the univariate Cox regression observation using the DEGs to screen out survival-related genes,and finally 4 genes(GSDME、NLRP1、NLRP6、IL1B)were tightly related to OS (P<0.05)(Fig. 3A). Aforementioned 4 genes were used for establishing a model of prognosis via LASSO Cox regression method. A 4-gene signature was then established in the light of the best score of λ(Fig. 3B,C༉. Our research adopted the formula bellow to compute the risk score: e (0.8938 * the NLRP1 expression level + 1.0402 * the GSDME expression level + 0.4383* the level of expression of NLRP6 +1.0041 * the IL1B expression level). Our study applied the mid-value risk score as the criterion for separating cases into low-risk and high-risk groups(Fig. 3D). The PCA study presented that there were two diverse distribution of these HNSCC cases (Fig. 3E). People in the high-risk group displayed a greater death ratio versus the other group (Fig. 3F). Likewise, the Kaplan-Meier curve revealed that low-risk group registered a notably greater OS versus the high-risk group(Fig. 3G, P < 0.001). Figure 3H unveiled the OS forecast role of the risk score, with the region below the curve (AUC) at 0.645, 0.612, and 0.623 of 1, 2, 3 years, separately.
A total of 96 HNSCC sufferers from Gene Expression Omnibus (GEO) cohort (GSE31056) were used as the verification set. Our team divided patients into two groups as mentioned above on the foundation of the mid-value risk score of the TCGA cohort(Fig. 4A). The PCA displayed acceptable separation between the two groups(Fig. 4B).People in the low-risk group displayed a lower mortality rate versus the other group (Fig. 4C). Moreover, the Kaplan-Meier curve also demonstrated that group with low risk exhibited remarkably higher OS compared to group with high risk (Fig. 4D, P < 0.001). Time-dependent ROC analysis of GEO cohort suggests that the forecast ability of the model was good, with the AUC at 0.744, 0.741, and 0.796 of 1, 2, 3 years, separately(Fig. 4E).
Cox regression study was employed to verify whether the risk score could independently forecast the OS. As discovered via the univariable Cox regression study, the risk score presented a tight relevance with OS in both TCGA and GEO cohorts(HR = 1.6629, 95% CI = 1.2390–2.2319, P = 0.0007 and HR = 5.7277, 95% CI = 1.8784–17.4645, P = 0.0022) (Fig. 5A,C). Likewise, the multivariable Cox regression study revealed a remarkable correlation between the OS and risk score in both cohorts (HR = 1.6631, 95% CI = 1.2316–2.2459, P<0.001 and HR = 4.2197, 95% CI = 1.3920-12.7916, P = 0.0109) (Fig. 5B,D).Moreover, our team made a heatmap of risk score and clinical characteristics in the TCGA cohort,and the results indicated that survival status of patients was distinctly diverse between the two groups (Fig. 5E).
In order to reveal the biological roles and pathways associated with the risk score,we firstly used the “limma”R package to find out the DEGs between the two groups. The DEGs standard was a FDR below 0.05 and |log2FC | ≥ 1.A total of 1025 DEGs were detected between the two groups in the TCGA cohort. Then, our team implemented the GO enrichment and KEGG pathway study of these DEGs. Figure 6A showed the top 10 MF, CC, and BP terms. The prime enriched Go terms were gated channel activity, postsynaptic membrane, integral and intrinsic components of synaptic membrane, and synaptic membrane. Figure 6B showed the main KEGG pathways, including Nicotine addiction, Primary immunodeficiency, Glutamatergic synapse, and cAMP signaling pathway.
The immunity condition was calculated via ssGSEA by virtue of the enrichment score and its correlation with the risk score was studied. By comparison, the enrichment scores of TIL, Tfh, T helper cells, pDCs, Neutrophils, CD8 + T cells, B cells, and aDCs were remarkably diverse (adjusted P < 0.05, Fig. 7A). These groups demonstrated a notable diversity on values of T cell co − activation, T cell co − suppression, inflammation promoting, HLA, Cytolytic activity, and Check point(adjusted P < 0.05, Fig. 7B).
Our team verified the levels of mRNA expression as to the 4 pyroptosis-associated genes in HNSCC samples and healthy samples via RT-qPCR,the outcomes revealed that GSDME (Fig. 8A)、NLRP1(Fig. 8B)、NLRP6(Fig. 8C)、IL1B (Fig. 8D)were all distinctly greater in HNSCC samples versus in healthy samples(P<0.001).
Pyroptosis is defined as a novel type of programmed cell death characterized by inflammatory response and was reported to play a dual role in the cancer progress[17]. Signaling pathways and inflammatory mediators in the process of pyroptosis are closely associated with the invasion,proliferation, and metatasis of cancer [18, 19]. Nevertheless, the induction of tumor cell pyroptosis could be a novel treatmen[20].However,whether pyroptosis-related genes are related to the outcome of HNSCC patients and how they act in HNSCC remain unkonwn.
In our research,we systematically analyzed the mRNA expression levels of 33 pyroptosis-associated genes in HNSCC and healthy samples.The outcomes showed that more than half of the pyroptosis-related genes (55%, 18/33) were DEGs by comparing cancer and neighboring healthy samples and four of them were associated with the OS, which hinted that pyroptosis participated in HNSCC and exhibited a potential forecast role of relevant genes.In order to explore more on the forecast power of those genes,we built a 4-gene signature through LASSO Cox regression analysis,and then the signature was validated to perform well in a GEO cohort.We further validated the mRNA levels of these 4 genes in HNSCC and healthy tissues via RT-qPCR, and the outcomes coincided with those in TCGA cohort. The functional analyses indicated a different status of immune between the two groups.
Our study used 4 pyroptosis-related genes (GSDME、NLRP1、NLRP6、IL1B)to establish a forecast model. Those genes function via different mechanisms[21, 22]. GSDME, also known as DFNA5 ,is from the gasdermin family which is the executor of pyroptosis.The activated caspase-3 could realize the cleavage of GSDME to release a N-terminal fragment and then to promote pyroptosis[23]. GSDME was recognized as a tumor suppressor gene due to its inactivation and DNA promoter region methylation was found in many tumors,including breast cancer[24], hepatocellular carcinoma[25] and so on,however༌it was reported to be overexpressed in nonsmall-cell lung cancer[26] and esophageal squamous cell cancer [27].Therefore༌GSDME may play a specific role in different tumors.Intersetingly,GSDME seemed to be an oncogenic gene because it was upregulated in tumor tissues.Due to the limited data and the conflicting results in different tumors,our result about GSDME provide a new direction for future research. NLRP1 is one of the NALP family proteins consisting of a pyrin domain, a CARD domain and an NBD-LRR domain which can induce pyroptosis[28].Previous studies[29] reported that NLRP1 was regulated downward in colorectal cancer samples relative to healthy samples. Nevertheless,in our research,the expression of NLRP1 was distinctly higher in HNSCC specimens versus in healthy ones,and related to a shorter survival time .The relationship between NLRP1-mediated pyroptosis and HNSCC was still unknown and need further study.NLRP6 is a new type of inflammasome which can play critical roles in the development of cancer.Knockdown of NLRP6 encourages gastric tumor cells to proliferate, migrate, and invade [30].However,our results demonstrated that higher NLRP6 expression was in tumor samples versus normal ones. Similarly ,IL1B is another type of inflammasome which is released by caspase-1 and can induce pyroptosis.A recent study presented that the IL 1B expression was lower in hepatocellular carcinoma samples [31].On the contrary, cervical cancer cells release more IL 1B than normal cervical cells[32].We found that IL 1B was upregulated in tumor tissues and its overexpression could predict poor outcome,revealing that it is a cancer-promoting gene in our research.Further research may highlight how IL 1B act in the process of pyroptosis and tumor development.In summary,all the 4 genes(GSDME、NLRP1、NLRP6、IL1B) were regulated upward in HNSCC cases and were associated with unfavourable clinical results. Nevertheless, whether those genes impact the HNSCCprognosis by virtue of modulating pyroptosis needs more studies.
We analysed the DEGs between the two groups divided by risk scores and discovered that the DEGs primarily emerged in immune and inflammatory responses.The outcomes of GO and KEGG methods displayed that pyroptosis might play a important role in tumor immune microenvironment.The enrichment scores of crucial anti-tumor immune cells and immune-associated pathways were lower in the high-risk group in contrast with the other one,suggesting that the unfavourable clinical outcomes in high-risk group might be induced by the damaged anti-cancer immune activity.
At present,the underlying mechanism of pyroptosis is less studied, especially in HNSCC.Our study identified 4 genes which are the regulators of pyroptosis,and then our team carried out preliminary research on the prognostic value of these pyroptosis-associated genes and offered a new direction for future investigation.Nevertheless,whether these regulators play the same role in pyroptosis-pathway in HNSCC is still elusive,and deserves further in-depth research.In addition, there was limitations for our model of prognosis due to openly available data. It’s pivotal to conduct further in vitro and in vivo researches to validate the clinical significance of this model.
This rescarch found that pyroptosis is tightly associated with HNSCC as more than half of the pyroptosis-associated genes were differently expressed by comparing tumor and healthy samples.Furthermore, we constructed a novel forecast model by 4 pyroptosis-associated genes. The risk score according to the signature were able to independently forecast the OS of HNSCC both in TCGA and GEO cohorts. The potential mechanism regarding the correlation of the pyroptosis-associated genes and HNSCC is vague and requires more in-depth researches.
The research was approved by the the Ethics Committee of the First Hospital of Jiaxing and all patients provided written informed consent prior to enrollment for research use of data and biospecimens. All the experiment protocol for involving human data was in accordance with the guidelines of Declaration of Helsinki.
Not Applicable.
The datasets supporting the conclusions of this article are available in the TCGA database (https://portal.gdc.cancer.gov/) and GEO database(https://www.ncbi.nlm.nih.gov/geo/).
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
This study was funded by the Science and Technology Project of Jiaxing City（2019AD32259）; the Natural Science Foundation of Zhejiang Province(LQ20H160059);the Project of the First Hospital of Jiaxing(2021077).
LL designed this study; XYQ took charge of data analysis, prepared the figures, and wrote the manuscript; JT, YQC, ZQC,LC,CS took charge of data collection as well as the critical reading regarding the manuscript. The final manuscript has been read and approved by all authors.
All authors acknowledge the contributions from TCGA and GEO project.
1Department of Head and Neck Surgery, The First Hospital of Jiaxing, The Affiliated Hospital of Jiaxing University,Jiaxing,China.
2Department of Nuclear Medicine Clinic, The First Hospital of Jiaxing, The Affiliated Hospital of Jiaxing University, Jiaxing,China.