3.1 Identification of DEGs between Normal and Tumor Tissues
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).
3.2 Construction of a Prognostic Gene Signature in the TCGA Cohort
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.
3.3 Validation of the Risk Signature in the GEO Cohort
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).
3.4 Independent Prognostic Value of the Risk Signature
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).
3.5 Functional Analyses Based on the Risk Model
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.
3.6 Comparison of the Immune Activity
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).
3.7 Validation of the 4 Pyroptosis-Related Genes in HNSCC Tissues
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).