1. The expression and potential pathways of pyroptosis-related genes (PRGs).
First explored the expression levels of 33 PRGs in UCEC tissues and normal control tissues based on the TCGA database. As shown in Figure 2A, a total of 15 PRGs were up-regulated (AIM2, CASP3, CASP5, CASP8, GPX4, GSDMB, GSDMC, GSDMD, IL18, NLRP2, NLRP7, NOD2, PYCARD, TNF) and 12 PRGs were down-regulated (CASP9, ELANE, GSDME, IL6, NLRP1, NLRP3, NOD1, PJVK, PLCG1, PRKACA, SCAF11, TIRAP). Then, based on these 15 up-regulated PRGs and 12 down-regulated PRGs, GO analysis and KEGG analysis were performed to further explore the possible functions of these meaningful PRGs in UCEC. As shown in Figure 2B, the biological processes were mainly enriched in immune regulation and inflammation regulation, such as ‘activation of innate immune response’ and ‘positive regulation of activated T cell’. At the same time, Figure 2C showed that cell signaling pathways were mainly enriched in NOD-like, TNF, Toll-like and MAPK. The results of GO analysis and KEGG analysis will help future detailed research on these PRGs.
2. NLRP2, GSDME, NOD2 and GPX4 were key PRGs related to the prognosis of UCEC patients and were related to genetic mutations.
After exploring the differential expression levels and functions of 33 PRGs genes, an attempt was made to screen out key PRGs related to the prognosis of UCEC patients. The univariate COX analysis in Figure 3A showed that NLRP2, GSDME, NOD2, and GPX4 were the prognostic factors of UCEC patients. The Kaplan-Meier survival analysis in Figure 3B-E also showed that these 4 key PRGs have an impact on the survival of UCEC. The combination of univariate COX analysis and Kaplan-Meier survival analysis showed that the high expression of NLRP2 and GSDME were risk factors for the survival and prognosis of UCEC, while the high expression of NOD2 and GPX4 were the protective factors for the survival and prognosis of UCEC. Afterwards, the genetic mutations of these 4 key PRGs were explored. Figure 4A-B showed that NOD2 (P=0.04) and GPX4 (P=6.18e-05) were significantly correlated with MSI, while GPX4 (P=1.42e-16) and GADME (P=0.01) were significantly correlated with TMB. Interestingly, it can be seen that GPX4 had a significant positive correlation with MSI and TMB. Then Figure 4C showed the genetic mutation spectrum of these 4 key PRGs in UCEC. Figure 4D indicated that the prognosis of altered group of the 4 key PRGs was worse for UCEC patients (P=0.0319). In general, these four key PRGs were closely related to genetic mutations and may affect the survival prognosis of UCEC patients through this reason.
After that, the 4 key PRGs and multiple key clinical characteristics were combined to conduct univariate and multivariate COX analysis. The results were shown in Figure 4F-G. Only NOD2 was meaningful in univariate and multivariate COX analysis, while most of the other factors were only meaningful in univariate COX analysis. This also suggested that when the variables involved were too many and too complex, univariate and multivariate COX analysis were too simple to be used for prognostic evaluation of UCEC, therefore a more complex and effective prognostic evaluation model was inevitably needed.
3. The prognostic evaluation model based on 4 key PRGs has important evaluation value for the prognosis of UCEC patients.
The gene expression levels of 4 key PRGs and the survival prognosis of UCEC patients were included in the Lasso Cox regression analysis, and the risk score calculation formula was obtained: Riskscore= (0.4323) * GPX4 + (0.2385) * GSDME + (0.0525) * NLRP2 + (-0.3299) * NOD2, as shown in Figure 5A. At the same time, the upper part of Figure 5B showed the distribution of the risk scores of 542 UCEC patients in the TCGA database. The median value of the risk score is -4. According to this median value, UCEC patients are divided into high-risk group/low-risk group. The middle part of Figure 5B showed the survival status of UCEC patients. The lower part of Figure 5B showed the expression of 4 key PRGs in UCEC patients, and it can be seen that there are obvious differences in their expression between the two groups. And Figure 5C-D was the verification of Lasso Cox regression analysis results.
Figure 6A showed the distribution and differences of gene expression and clinical characteristics of the high-risk group/low-risk group. It can be seen that the FIGO stage was different in the two groups. Figure 6B-I also showed that there were significant differences in the risk scores of the two clinical characteristics of Molecular infiltration (P=0.01) and Hypertension (P=0.02). For FIGO stage, both stage Ⅲ (P=1.9e-05) and stage Ⅳ (P=7.0e-03) have higher risk scores than stage Ⅰ.
Figure 7A showed that the survival of the high-risk group was significantly worse than that of the low-risk group (P=6.45e-05). Figure 7B showed that the prognostic assessment model has a certain diagnostic value for predicting the 5-year survival probability of UCEC patients (AUC=0.711). Figure 7C-D showed that the high-risk group, age and FIGO stage were all independent risk factors for the prognosis of UCEC patients (p <0.05, HR>1). Figure 7E showed that when the prognostic assessment model, age, FIGO stage, pathological grade, Fertility, and Molecular infiltration were included in the AUC curve, the prognostic assessment model still has a certain diagnostic value for predicting the 5-year survival probability of UCEC patients (AUC=0.69). The DCA curve in Figure 7E showed that the prognostic evaluation model has a certain predictive evaluation value for the prognosis of UCEC patients. Finally, the nomogram in Figure 8A showed how the prognostic assessment model combined with clinical characteristics can be used to predict the survival of UCEC patients after surgery. This also improves the clinical translation value of this research. In general, the prognostic evaluation model based on 4 key PRGs has important evaluation value for the prognosis of UCEC patients.
4. Enriched signaling pathways in the high-risk group of the prognostic evaluation model.
Figure 9 indicated that the gene expression of the high-risk group was mainly enriched in endometrial cancer, JAK STAT, natural killer cell-mediated cytotoxicity, T cell receptors, cancer pathways, MAPK, B cell receptors and chemokine signaling pathway. This showed that the prognostic evaluation model was closely related to the molecular pathological mechanism of UCEC and was inseparable from the regulation of the immune microenvironment of UCEC.
The determination of the signal pathways related to the prognostic evaluation model on the one hand supported the scientific nature of the model, and on the other hand, it also provided a direction for the discussion of the detailed molecular mechanism of the model.
5. The relationship between the prognostic assessment model and the immune microenvironment.
Figure 2B-C indicated that PRGs were closely related to immune regulation, and Figure 9 also indicated that the prognostic evaluation model based on 4 key PRGs was closely related to the tumor immune microenvironment, so we try to explore the relationship between the prognostic evaluation model and the immune microenvironment. For the aspect of immune infiltration, Figure 10B indicated that based on the EPIC algorithm, the degree of immune cell infiltration (CD8+ T cells, CD4+ T cells, macrophages, and NK cells) in the high-risk group was lower than that in the low-risk group, and the correlation analysis also suggested that there were significant negative correlations between the risk score and the degree of immune cell infiltration. Similar results can be obtained in Figure S1 and Figure S2. As for immune checkpoints, Figure S3 indicated that the expression of LAG3, SIGLEC15, and CD274 in the high-risk group was significantly increased compared with the low-risk group. On the other hand, the expressions of CTLA4, PDCD1LG2, and PDCD1 in the high-risk group were significantly reduced.
In short, the prognostic evaluation model was closely related to the immune microenvironment, and it may be used to predict the degree of immune infiltration and the expression of immune checkpoints in UCEC patients, and to better develop individualized immunotherapy programs for patients to improve the prognosis.