
==== Front
Discov Oncol
Discov Oncol
Discover Oncology
2730-6011
Springer US New York

39264524
1315
10.1007/s12672-024-01315-3
Research
Pyroptosis-associated genes and tumor immune response in endometrial cancer
Gong Xiaodi gong_xdi@sina.com

Wang Zhifeng
You Jiahao
Gao Jinghai
Chen Kun
Chu Jing
Sui Xiaoxin
Dang Jianhong wsdang168@sina.com

Liu Xiaojun liuxiaojun@smmu.edu.cn

https://ror.org/012f2cn18 grid.452828.1 0000 0004 7649 7439 Department of Gynecology and Obstetrics, The Second Affiliated Hospital of Naval Medical University, Shanghai, 200003 China
12 9 2024
12 9 2024
12 2024
15 43314 1 2024
4 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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 http://creativecommons.org/licenses/by-nc-nd/4.0/.
The occurrence and progression of tumors are linked to the process of pyroptosis. However, the precise involvement of pyroptosis-associated genes (PRGs) in endometrial cancer (EC) remains uncertain. 29 PRGs were identified as being either up-regulated or down-regulated in EC. PRGs subgroup analysis demonstrated distinct survival outcomes and diverse responses to chemotherapy and immune checkpoint blockade therapy. A higher expression of GPX4 and NOD2, coupled with lower levels of CASP6, PRKACA, and NLRP2, were found to be significantly associated with higher overall survival (OS) rates (p < 0.05). Conversely, lower expression of NOD2 was linked to lower progression-free survival (p = 0.021) and advanced tumor stage(p = 0.0024). NOD2, NLRP2, and TNM stages were identified as independent prognostic factors (p < 0.001). The LASSO prognostic model exhibited a notable decrease in OS among EC patients in the high-risk score group (ROC-AUC10-years: 0.799, p = 0.00644). Furthermore, NOD2 displayed a positive correlation with the infiltration of immune cells and the expression of immune checkpoints (p < 0.001). GPX4 and CASP6 are significantly associated with TMB and MSI (RTMB = 0.39; RMSI = 0.23). Additionally, a substantial upregulation of NOD2 was confirmed in both EC cells and tissue, indicating a positive relationship between advanced TNM stage (p < 0.0001) and infiltration of M1 phenotype macrophages. Nonetheless, its impact on patient OS did not reach statistical significance (p = 0.141). Our findings have contributed to the advancement of a prognostic model for EC patients. NOD2 receptor-mediated pyroptosis mechanism potentially regulates tumor immunity and promotes the transformation of macrophages from the M2 phenotype to the M1 phenotype, which significantly impacts the progression of EC.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-024-01315-3.

Keywords

Pyroptosis-related gene
NOD2
Prognostic model
Immunotherapy
Endometrial cancer
the "Pyramid Talent Project" initiative, a three-year Action Plan for talent Construction of Shanghai Chang Zheng Hospital.YQ104 Gong Xiaodi the university-level research fund of the Naval Medical University2023QN078 Gong Xiaodi issue-copyright-statement© Springer Science+Business Media, LLC 2024
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pmcIntroduction

Endometrial cancer (EC) is the predominant gynecological malignancy affecting the female reproductive tract in developed nations. Its incidence has been observed to rise by approximately 1% annually, primarily attributed to declining fertility rates and persistent weight gain [1]. While survival rates for most cancers have improved since the mid-1970s, uterine cancer stands as an exception. Mortality rates for uterine corpus cancer have accelerated from a yearly increase of 0.3% (1997–2008) to 1.9% (2008–2018), surpassing the rate of morbidity escalation by two-fold [2]. These findings suggest that the treatment of EC has encountered a significant obstacle. Hence, it is imperative to expeditiously devise novel therapeutic interventions to enhance the treatment efficacy for EC.

Currently, the primary therapeutic modalities employed for the management of endometrial cancer encompass surgical intervention and chemoradiotherapy. Recent studies indicate that the management of endometrial cancer informed by molecular typing offers enhanced precision in guiding postoperative treatment strategies for this condition [3]. Of note, several foundational studies have demonstrated that there exists a regulated immune response within the tumor microenvironment of endometrial cancer cells. This finding highlights the potential of immunotherapy as a promising approach for targeting endometrial cancer. This potential is further supported by the high tumor mutation load, elevated expression rate of PD-1/PD-L1, significant infiltration of tumor-associated lymphocytes in certain tumors, and positive outcomes observed in diverse clinical trials [4–6]. Immunotherapy, especially PD-1 inhibitors, as second-line or more than second-line therapy, has shown good efficacy in metastatic endometrial cancer [7]. However, the reactivity of immunotherapy was found to be linked to patient selectivity in both monotherapy and combination immunotherapy. Moreover, the efficacy of immunotherapy was observed to be more favorable in patients exhibiting POLE mutations, MSI-H or dMMR, and TMB levels equal to or exceeding 10 [8–10]. Consequently, the selection of immunotherapy for recurrent endometrial cancer necessitates the guidance of biomarkers. However, the currently available biomarkers, namely POLE, MSI or MMR status, and TMB, fail to fully meet the requirements.

Pyroptosis is a form of programmed cell death characterized by the cleavage of Gasdermin D (GSDMD) by caspase1 or caspase11/4/5. This cleavage releases the N-terminal domain of GSDMD, which possesses the ability to bind membrane lipids and induce the formation of pores in the cell membrane. Consequently, the cell experiences alterations in osmotic pressure and swelling until the eventual rupture of the cell membrane, resulting in the release of cellular content, and triggering a robust inflammatory response [11, 12].

In certain instances, pyroptosis is regarded as a type of immunogenic cell death (ICD) due to the release of immunogenic substances, such as tumor-associated antigens and endogenous damage-associated molecular patterns (DAMPs) [13, 14]. These substances facilitate the activation and infiltration of immune cells, ultimately leading to the initiation of an adaptive immune response [15]. Despite the lack of complete clarity regarding the association between pyroptosis and anticancer immunity, an increasing number of studies have demonstrated that the elimination of tumors through cell pyroptosis is accomplished by augmenting immune activation and function [13].

Several recent studies have suggested that pyroptosis may play a role in the pathogenesis of endometrial cancer [16–18]. Additionally, various pyroptosis-related molecules have been implicated in the anti-tumor immune response, with their expression potentially influencing the efficacy of immunotherapy [19, 20]. Nevertheless, these findings have yet to be confirmed in endometrial cancer specimens. Hence, this study undertook an evaluation of the expression and functionality of cellular pyroptosis-related genes (PRGs) in EC, aiming to ascertain their significance in terms of patient prognosis, immune score, immune checkpoint expression, and immunotherapy efficacy. Ultimately, the identification of pertinent indicators of cellular pyroptosis can serve as a valuable tool in guiding the selection of immunotherapeutic interventions, thereby facilitating the broader accessibility of immunotherapy for individuals afflicted with EC.

Results

PRGs expression in uterine corpus endometrial carcinoma (UCEC)

In this study, we initially investigated the expression patterns of 33 PRGs in both UCEC and normal endometrial tissues, utilizing the TCGA-UCEC dataset (Table S1, S2). A total of 29 PRGs exhibited either upregulation or downregulation in UCEC (Fig. 1a). In UCEC, a decrease in the expression of IL6, NLRC4, NLRP1, NLRP3, NOD1, PJVK, PLCG1, PRKACA, SCAF11, TIRAP, CASP1, CASP9, ELANE, and GSDME was observed when compared to normal tissues. Conversely, an increase in IL18, NLRP2, NLRP7, NOD2, PYCARD, TNF, AIM2, CASP3, CASP5, CASP6, CASP8, GPX4, GSDMB, GSDMC, and GSDMD expression was noted. A predominant positive correlation was observed among the 29 PRGs exhibiting differential expression (Fig. S1). Notably, CASP3 and SCAF11 exhibited the highest correlation coefficient of 0.71. Additionally, strong correlations were observed between CASP8 and SCAF11, as well as between CASP6 and CASP3, with correlation coefficients of 0.67 and 0.64, respectively. Protein-protein interaction (PPI) assays were developed with a threshold of physical scores exceeding 0.132 to identify interactions involving PRGs. The findings revealed CASP4, CASP5, GSDMD, NLRP1, NLRP6, CASP1, NLRP3, PYCARD, AIM2, and NLRC4 as the central genes, as depicted in Fig. S2a. Additionally, Fig. S2b illustrates the comprehensive network encompassing all PRGs. Furthermore, to clarify the roles and mechanisms of PRGs, a functional enrichment analysis of PRGs was conducted utilizing the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. The results of the functional enrichment analysis indicated that these 33 PRGs were primarily associated with processes such as pyroptosis, the NOD-like receptor signaling pathway, and the response to bacterium (Fig. S3).

Using the gene expression levels of PRGs, we employed the Consensus ClusterPlus (v1.54.0) package in R to discern distinct subgroups within a cohort of 545 endometrial cancer samples. By computation of cluster consensus and project consensus outcomes, we observed the emergence of k (ranging from 2 to 6) subgroups, as depicted in Fig. 1b. Our analysis revealed that a k value of 3 yielded satisfactory discrimination. Consequently, all patients were effectively stratified into three subgroups, utilizing the most stable k value. Figure 1c illustrates that subgroup 2 exhibits a considerable degree of gene expression, while subgroup 3 demonstrates a diminished gene expression level. Additionally, subgroup 1 encompasses both components. These findings align with the outcomes derived from principal component analysis (PCA). Furthermore, the analysis of overall survival indicates that subgroup 3 exhibits a significantly reduced survival time (median time = 9.2 years, p = 0.025) (Fig. 1d).

Furthermore, the IC50 values of Cisplatin and Doxorubicin were found to be significantly elevated in the G3 subgroup compared to the G1 and G2 subgroups (****p < 0.0001), suggesting a potential resistance to these drugs among patients in the G3 subgroup. Conversely, the IC50 value of Paclitaxel was reduced in the G3 subgroup compared to the G1 subgroup, indicating a heightened sensitivity to Paclitaxel among patients in the G3 subgroup. Consequently, the administration of Paclitaxel may offer greater therapeutic benefits to individuals in the G3 subgroup (Fig. 1e).

The advent of immune checkpoint blockade (ICB) therapy has brought about a significant transformation in the management of human cancers. In this study, the responsiveness of three subtypes of endometrial cancer to immune checkpoint inhibitors was predicted using the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm based on the analysis of pyroptotic gene expression profiles from three distinct groups. According to the findings in Fig. 1f, the TIDE score exhibited a higher value in the G1 subgroup when compared to the G2 and G3 subgroups (*p < 0.01, **p < 0.05). Consequently, the effectiveness of ICB was notably diminished in the G1 group, resulting in a shorter survival time following the treatment (p = 0.011).

Fig. 1 Expression of pyroptosis-related genes (PRGs) in UCEC.  a  The expression distribution of 33 PRGs genes in UCEC and normal tissues. b  CDF and CDF Delta area curves, PCA, and Consensus clustering matrix of the three subgroups. c  Consistency of clustering results in heatmap (k = 3). d  Kaplan–Meier survival plots of the three subgroups. e  Distribution of IC50 scores in three different subgroups. f  The distribution of immune response scores in three subgroups in the prediction results. The above table presents the statistical data on the immune response of samples belonging to various subgroups (*p  < 0.05, **p  < 0.01, ***p  < 0.001, ****p  < 0.0001)

Clinical characteristics analysis and genetic variation of PRGs in UCEC

The univariate Cox regression analysis results indicated that five PRGs exhibited prognostic significance. The Kaplan-Meier survival data revealed that among the 29 PRGs genes, the expression levels of GPX4, CASP6, NOD2, NLRP2, and PAKACA were associated with significant survival outcomes in terms of overall survival (OS) (Table S3). Additionally, the expression of NOD2 among the aforementioned 5 hub genes was found to have a significant impact on progression-free survival (PFS) (Table S4 and Fig. S4). The findings suggested that UCEC patients with decreased expression of GPX4 (p = 0.019) and increased expression of CASP6 (p = 0.042), NLRP2 (p = 0.034), NOD2 (pOS = 0.019, pPFS = 0.021), and PRKACA (p = 0.041) experienced unfavorable survival outcomes (Fig. 2a). However, it was observed that only NOD2 expression was significantly associated with tumor Stages in patients with UCEC (p = 0.0024), as depicted in Fig. 2b and Fig. S5. Notably, NOD2 expression was found to be significantly diminished in patients diagnosed with Stage IV disease. Among the five identified PRGs, Mutations in NOD2 and NLRP2 were the most frequent, with PRKACA following suit. The prevailing variant classification was missense mutations, as depicted in Fig. 2c. The findings indicated that the expression of NOD2 may confer a protective effect in patients with UCEC.

Fig. 2 Analysis of clinical characteristics and genetic variation of PRGs in UCEC. a Kaplan–Meier survival plots of five PRGs genes. b An association between five PRGs expression and major tumor stages in patients with UCEC. c Genetic mutation frequency and classification of five PRGs genes in UCEC 

Construction of a pyroptosis-related LASSO prognostic model

The prognostic model (Fig. 3a) is constructed using the five PRGs in Fig. 2, as determined by the LASSO-Cox regression analysis. The risk score = (-0.3818) * NOD2 + (-0.0777) * CASP6 + (0.0681) * NLRP2 + (− 0.3966) * GPX4 + (0.261) * PRKACA. The present study has revealed that the NOD2 gene and GPX4 exhibit a higher protective capacity in the context of UCEC based on the model employed. This finding underscores the significant research potential of investigating the NOD2 and GPX4 genes in UCEC. Subsequently, UCEC patients were categorized into two groups based on risk scores. The distribution of risk scores, living status, and the expression of the five PRGs are depicted in Fig. 3b. As the risk score increased, UCEC patients faced a higher risk of mortality and experienced a decrease in their survival time. The Kaplan-Meier curves demonstrated that UCEC patients with a high-risk score had a significantly worse overall survival probability than those with a low-risk score (median time = 9.1 years, p = 0.00644, Fig. 3c). The area under the curve (AUC) values for the 3-year, 5-year, and 10-year receiver operating characteristic (ROC) curves were 0.641, 0.686, and 0.799, respectively, as depicted in Fig. 3d. Given the favorable prognosis of UCEC among the three primary gynecological malignancies, this genetic model demonstrates enhanced suitability for prognosticating patient survival beyond a 5-year time frame.

Fig. 3 Modeling prognosis related to pyroptosis using LASSO analysis. a A 10-fold cross-validation was performed on the coefficients of five PRGs in the LASSO model. The five selected PRGs were analyzed using X-tiles. b Survivorship time and status of selected datasets, along with the Risk score. Gene expression from the signature was shown in the heatmaps. c A Kaplan-Meier survival analysis of signature UCEC patients. The HR (High groups) denotes the hazard ratio between the high-expression sample relatives and the low-expression sample in the context of this study. d The ROC curves for 3-, 5-, and 10-year overall survival were derived from PRGs risk score

Predictive nomogram construction

Univariate and multivariate Cox regression analyses were conducted to examine the impact of the above five PRGs genes and clinical factors on the prognosis of UCEC patients. According to Fig. 4a and b, the expression of NOD2, NLRP2, and pTNM stage exhibited noteworthy disparities in both univariate and multivariate analysis (p < 0.05). These results suggest that these three variables function as autonomous factors that impact the prognosis of UCEC patients.

Subsequently, a nomogram was developed utilizing the variables with significant differences to forecast the probability of patient survival. The results demonstrated that the prognostic efficacy for 3- and 5-year overall survival was relatively commendable when juxtaposed with the ideal model encompassing the entire cohort (Fig. 4c and d).

Fig. 4 Nomogram construction using predictive models. a, b Univariate and multivariate Cox regression in UCEC considering clinical parameters and five prognostic PRGs. c, d The nomogram predicts UCEC patients’ overall survival at 1 year, 3 years, and 5 years. An overall survival model calibration curve for the discovery group. Ideal nomograms are shown as dashed diagonal lines

PRGs were associated with tumor immune infiltration in UCEC

Subsequently, an evaluation was conducted on the distribution of immune scores within UCEC tissues. In comparison to normal tissues, a notable decrease was observed in the immune infiltration scores of CD8+T cells, macrophages, and myeloid dendritic cells within the UCEC tumor microenvironment, whereas there was a significant increase in B cell infiltration (Fig. 5a). Next, the relationship between the expression of five prognostic PRGs genes and immune scores was assessed using a butterfly map, where red or green lines were employed to depict this correlation. Furthermore, the heat map on the right provided the specific correlation coefficients. Our study revealed a significant positive correlation between NOD2 expression and six distinct immune cell types (**p < 0.01). Specifically, the Spearman correlation coefficients were as follows: R B cell = 0.31, R Macrophage = 0.34, R Myeloid dendritic cell = 0.37, R Neutrophil = 0.38, R Tcell CD4+ = 0.39, and R Tcell CD8+ = 0.21. Additionally, the interconnections among immune cells were illustrated by blue circles (Fig. 5b).

Fig. 5 Tumor immune infiltration in UCEC was associated with PRGs. a Distribution of TIMER immune score expression in UCEC and normal tissues. b The heat map depicted in the schematic illustrates the correlation analysis of the immune score. Blue hues indicate a positive correlation, while red hues indicate a negative correlation. The intensity of the color, whether red or blue, signifies the strength of the correlation. Additionally, the size of the circle corresponds to the magnitude of the correlation. The schematic also includes a red line, which signifies the negative correlation between the gene expression of PRGs and the immune score, while green denotes a positive correlation. The table situated on the right predominantly presents the precise correlation coefficients (*p < 0.05, **p < 0.01, ***p < 0.001)

The inhibition of immune cell function by expressing immune checkpoint molecules hinders the body’s ability to generate a potent anti-tumor immune response, resulting in tumor immune evasion. Consequently, we investigated the contrasting expression patterns of eight immune checkpoint-associated genes in UCEC and normal tissues. According to the data presented in Fig. 6a, the CD274, LAG3, and PDCD1LG3 distribution in the UCEC organization exhibited a significant reduction (***p < 0.001). Conversely, CTLA4, HAVCR2, PDCD1, TIGIT, and SIGLEC15 showed varying degrees of increase (**p < 0.01, ***p < 0.001). In the context of the prognostic model (Fig. 6b), a comparative analysis of the correlations between the five PRGs and immune checkpoint expression revealed a positive association between NOD2 expression and all immune checkpoint molecules, except TIGIT (**p < 0.01). Tumor mutation burden (TMB) and PDL1 are significant biomarkers utilized in prognosticating PD1 antibody treatment response. Furthermore, conducting microsatellite instability (MSI) testing in UCEC patients is imperative due to the potential presence of Lynch syndrome defects involving the inactivation of at least one DNA mismatch repair gene. According to the data presented in Fig. 6c, a positive correlation was observed between tumor mutation burden and microsatellite instability with the expression levels of GPX4 and CASP6 (GPX4: RTMB=0.39, p = 8.78*10−20, RMSI = 0.23, p = 1.25*10−7; CASP6: RTMB = 0.15, p = 4.62*10−4, RMSI = 0.16, p = 2.87*10−4). Simultaneously, the positive correlation observed between GPX4 expression and PDCD1 expression (Fig. 6b) implies that UCEC patients exhibiting elevated GPX4 expression may experience greater therapeutic advantages from PD-1 inhibitor treatments.

Fig. 6 Correlation analysis of PRGs with immune checkpoint genes, TMB, and MSI expression in UCEC. a A comparison of immune checkpoint gene expression in UCEC and normal tissues. b The diagram displays a heat map illustrating the correlation between PRGs genes and immune checkpoint-related genes. The color scheme assigns red to indicate a positive correlation, blue to indicate a negative correlation, and the intensity of the color signifies the strength of the correlation between the two gene sets (*p < 0.05, **p < 0.01, ***p < 0.001). (c) Spearman’s method was employed to conduct a correlation analysis between the gene expression of PRGs and TMB/MSI.

Analysis of the expression of PGRs on EC cells

Subsequently, we examined to confirm the fundamental expression of five PGRs in both normal endometrial epithelial cells (hEEC) and various endometrial cancer cell lines (AN3-CA, HEC-1B, KLE, Ishikawa, and RL95-2). In Fig. 7a, except for the PRKACA gene, the RNA levels of CASP6, GPX4, NLRP2, and NOD2 genes exhibited varying degrees of increase in EC cell lines (**p < 0.01, ***p < 0.001, ****p < 0.0001). However, the protein expression of these five PGRs did not entirely align with their respective mRNA levels, suggesting the possibility of post-transcriptional or post-translational modifications affecting these genes (Fig. 7b and Figure S6). Specifically, the expression of NOD2 was found to be significantly reduced in the Ishikawa and RL95-2 cell lines, aligning with the findings reported by Zhang et al. [21]. Furthermore, there was no significant disparity observed in the expression levels of NLRP2 between the hEEC and EC cell lines.

Fig. 7 Evaluation of PRGs expression in EC cell lines. a, b The mRNA and corresponding protein expression levels of CASP6, GPX4, NLRP2, NOD2, and PRKACA were determined in hEEC, AN3-CA, HEC-1B, KLE, Ishikawa, and RL95-2 cells. For normalization, GAPDH was used as an internal reference. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, ns: no statistically significant.

The relationship between NOD2 expression and survival or immune infiltration

We further evaluated the expression correlation of NOD2, CD86, and PDL1 in endometrial cancer and their relationship with patient survival by tissue microarray (TMA) (Figure S7). In the detection of 80 pairs of endometrial cancer and adjacent normal tissues, the expression levels of NOD2 in cancer were significantly higher than that in para-cancerous tissues (Table S5 and Fig. 8a, p < 0.0001). In addition, the correlation between NOD2 expression level and clinical indicators of EC patients, including age, TNM stage, and tumor size, was also analyzed (Table S6 and Table S7). The receiver operating characteristics (ROC) curves were also illustrated. The optimal cut-off value for NOD2 was 107.4 (AUC = 0.695, p < 0.0001). Unfortunately, Kaplan-Meier analysis showed that NOD2 expression was not associated with the prognosis of patients with endometrial cancer (Fig. 8b, p = 0.141). However, inconsistent with the results in Fig. 2b, high NOD2 levels were strongly associated with a later stage of endometrial cancer (Table S7, p = 0.009), with a significantly shorter median survival in such patients (Fig. 8b, p < 0.0001). To investigate the correlation between NOD2, CD86, and PDL1 in endometrial cancer tissues, tumor TMA immunofluorescence detection was performed using antibodies against NOD2, CD86, PDL1, and PANCK (Fig. 8c). The results showed that NOD2 expression in EC tissues was positively correlated with PANCK (Pearson correlation = 0.544, p < 0.0001). Unexpectedly, it was found that although the double positive rate of NOD2 and CD86 (indicator molecule of M1 macrophages) was less than 5% (Fig. 8d), there was a certain correlation between NOD2 and CD86 (Pearson correlation = 0.231, p = 0.040), consistent with the results in Fig. 5. However, NOD2 expression was not significantly correlated with PDL1 expression (Pearson correlation = 0.184, p = 0.102), different from Fig. 6b. The analysis of double positive area revealed that the percentage of NOD2+/PANCK + cells (38.71%) in EC tissues was much higher than that of CD86+/PANCK + cells (0.687%), PDL1+/PANCK+ (0.491%), NOD2+/CD86+ (0.750%), NOD2+/PDL1+(0.523%) and CD86+/ HPDL1+ (0.015%) (Fig. 8e, ****p < 0.0001). Multiple positive area analysis showed that the proportion of NOD2+/CD86/PANCK + cells (0.647%) was significantly higher than that of CD86/PDL1/PANCK + cells (0.013%) and NOD2/CD86/PDL1/PANCK + cells (0.012%) (*p < 0.05). These results indicate that EC tissues with high NOD2 expression are accompanied by M1-type macrophage infiltration, which is beneficial for tumor immunotherapy.

Fig. 8 Relationship between NOD2 expression and endometrial cancer patient survival, or immune markers. a NOD2 expression in endometrial carcinoma and para-cancer tissues. The ROC curves of NOD2 are based on independent tests. b Kaplan–Meier OS curves based on NOD2 expression level. c Representative images from Immunofluorescence staining of NOD2, CD86, PDL1, and PANCK in EC tissues from TMA samples (n = 80) (scale bar, 200 μm). d Pearson correlation analysis between the gene expression of NOD2 and PANCK, CD86, PDL1. e The percentages of double-positive and multiple positiveareasa of indicated proteins in EC tissues were analyzed

Discussion

Inflammation is intricately associated with tumors, particularly chronic inflammation, and serves as a primary catalyst for numerous tumor formations. By disrupting the signal transduction process of cells, inflammation contributes to the initiation, invasion, and progression of cancer [22]. NOD-like receptors, as prominent inflammatory immune receptors, exhibit non-specific recognition of diverse pathogenic substances, encompassing pathogen-related molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs), thereby engaging in anti-infection mechanisms via their mediating signal pathways [23]. The NOD-like receptor (NLR) family is classified into several subfamilies, including NLRAs, NLRBs (also known as NAIPs), NLRCs, and NLRPs. Among these subfamilies, NLRCs are mainly represented by NOD1 and NOD2, which are also the most extensively studied members of the NLR family [24]. Notably, the expression of Nucleotide-binding and oligomerization domain‐containing protein 2 (NOD2) exhibits variations in different tumor types. In clinical hepatocellular carcinoma, the expression of NOD2 is either completely lost or significantly down-regulated, and this loss of expression is closely associated with disease progression [25]. Conversely, NOD2 is highly expressed in renal cancer cells and tissues [26]. In the YD-10B cell line of oral squamous cell carcinoma, the expression of NOD1 was found to be high, while the expression of NOD2 was low [27]. This study observed that NOD2 expression was abnormally high in endometrial carcinoma tissues and cell lines, except for Ishikawa and RL95-2 cells (***p < 0.001, Figs. 1a and 7a, b), which aligns with a previous report [21]. Furthermore, patients with elevated levels of NOD2 exhibited longer overall survival (p = 0.019), progression-free survival (p = 0.021), and earlier pathological stage (p = 0.0024) (Fig. 2a, b), consistent with findings in melanoma cases displaying high NOD2 expression [28]. This study revealed that the mutation frequency of the NOD2 gene was observed to be merely 7%, predominantly consisting of missense mutations (Fig. 2c). Despite the presence of NOD2 gene polymorphisms (rs5743260, rs2066844, rs2066845) in individuals with endometrial cancer, no substantial correlation was observed between NOD2 gene polymorphism and the susceptibility to endometrial cancer [29]. Notably, the absence of NOD2 leads to an intestinal ecological imbalance, thereby elevating the likelihood of infectious colitis and colitis-associated carcinogenesis in mice [30]. Udden et al. [31] further substantiated that the knockout of NOD2 in mice can lead to an elevated occurrence of tumors. However, it is important to note that the development of tumors is not contingent upon an imbalance in the intestinal microbiota. The NOD2 protein plays a crucial role in down-regulating the Toll-like receptor (TLR) signaling pathway, thereby diminishing the activation of NF-KB and MAPK pathways through the production of IRF4. This mechanism effectively inhibits colonic inflammation and the initiation of tumorigenesis.

The multifaceted involvement of NOD2 in neoplasms may be attributed to variances in tumor subtypes, stages, genetic profiles, and duration of induction. Notably, heightened NOD2 expression during the initial stages can elicit the activation of the body’s adaptive immune response, thereby facilitating the eradication of malignant cells. Conversely, elevated NOD2 expression during the advanced stages can exacerbate systemic inflammation, leading to the generation of numerous inflammatory mediators and the facilitation of tumor proliferation, invasion, and metastasis.

Subgroup analysis revealed a significant association between the expression of pyroptosis-associated genes (PRGs) and sensitivity to chemotherapy and immunotherapy (Fig. 1e-f). Da Silva Correia et al. [32] conducted a groundbreaking study demonstrating that NOD1 can serve as a sensitizer of the TNF pathway, promoting cell apoptosis, inhibiting estrogen-sensitive breast tumors, and significantly reducing the expression of estrogen receptors in tumors. However, their findings also indicated that NOD2 had no significant impact on this cell line. Additionally, Wang et al. [33] discovered a derivative of benzidine (benzidine, BZD) that effectively inhibits both NOD1 and NOD2, thereby enhancing the therapeutic potential of paclitaxel in the management of Lewis lung cancer. Furthermore, NOD2 activation directly induces apoptosis through the AMPK signaling pathway, thereby substantially augmenting the susceptibility of hepatocellular carcinoma cells to sorafenib, renvatinib, and 5-FU [25]. The study demonstrates that the utilization of cell membrane camouflage and bufalin-loaded poly (lactic acid co-glycolic acid) nanoparticles (CBAP) effectively binds to NOD2, leading to the inhibition of nuclear factor kappa-light chain enhancer of activated B cells expression, as well as the suppression of ATP binding cassette transporter expression. Furthermore, CBAP exhibits the ability to overcome the multidrug resistance commonly observed in pancreatic cancer [34].

Recently, several models and methods have been designed to predict the prognostic risk of endometrial cancer. For example, radiomic analysis that includes patient ultrasound assessments, ultrasound images, and molecular/genomic analysis are combined to predict prognosis [35]. Here, the LASSO prognostic model constructed by five PRGs showed that the high expression of NOD2 and GPX4 was the protective factor of UCEC patients (Fig. 3). The AUC value of this model for predicting the 10-year survival time of UCEC patients reached 0.799, indicating that it is more applicable to predict the survival rate of patients with more than 5 years. Other studies have also confirmed that NOD2 is involved in the construction of prognostic models for patients with breast cancer, esophageal cancer, and hepatocellular carcinoma [36–38]. Nomogram analysis also showed that NOD2, NLRP2, and TNM stage were independent prognostic factors of UCEC (Fig. 4).

  The NOD-like receptor, as a component of the type recognition receptor, assumes a crucial role in the innate immune response. It primarily engages in the anti-bacterial immune response by facilitating the NF-KB pathway, MAPK pathway, and autophagy pathway. Moreover, the NOD2 receptor mediates the MAVS-IRF3/7-type I interferon signal pathway to contribute to the antiviral immune response [39]. Furthermore, the NOD-like receptor possesses a distinctive pathway that connects with the adaptive immune response [40].

Effector cells present in tumor-infiltrating lymphocytes (TIL) possess the ability to exert a cytotoxic immune response against solid tumors of various types. In the case of patients with UCEC, the enhancement of cytotoxic T lymphocyte (CTL) infiltration within the tumor microenvironment has been observed to correlate with the extension of disease progression survival (DPS) and OS [41]. Consequently, the reversal of the inhibitory tumor microenvironment represents a crucial strategy for effective immunotherapy. This investigation further revealed a significant decrease in the infiltration of CD8 + T cells, macrophages, and myeloid dendritic cells in UCEC patients (Fig. 5), thereby indicating suboptimal immunotherapeutic outcomes for the majority of UCEC patients.

Recent research findings have demonstrated that the induction of cell death plays a crucial role in enhancing the body’s immune response against tumors, thereby augmenting the effectiveness of immune checkpoint inhibitors (ICIS) [42]. The expression of immune checkpoint genes, particularly PD-L1, exhibits a significant association with the responsiveness to ICIS therapy [43]. Our study provides novel evidence indicating a positive correlation between NOD2 and most immune checkpoint genes within the context of five prognostic-related genes (PRGs) (Fig. 6b). Furthermore, apart from PD-L1, tumor mutation burden (TMB) serves as a valuable biomarker for predicting the efficacy of ICIS treatment, as confirmed by previous investigations in the field of immunotherapy for melanoma and lung cancer [44]. Typically, the TMB is directly proportional to the load of novel antigens, whereby new antigens resulting from mutations can be presented on the surface of tumor cells through MHCI molecules, thereby stimulating an immune response against the tumor [45]. Furthermore, it has been demonstrated that high TMB, microsatellite instability-high (MSI-H), and DNA polymerase epsilon (POLE)-mutated tumors are associated with a favorable response to ICIS [46, 47]. Given that higher TMB and MSI correspond to increased production of novel antigens, the elevated expression of GPX4 and CASP6 in uterine corpus endometrial carcinoma (UCEC) appears to enhance sensitivity to ICIS treatment (Fig. 6c) by facilitating the generation of additional novel antigens.

The expression of CASP6, GPX4, NLRP2, NOD2 and PRKACA was verified in five EC cell lines, revealing inconsistencies in the RNA and protein expression levels of these PRGs (Fig. 7). This disparity may be attributed to post-transcriptional or post-translational modification of genes. Additionally, the occurrence of endometrial carcinoma is intricately linked to estrogen level [48], and whether there is a relationship between pyroptosis state and estrogen receptor expression needs to be further investigated. In endometrial cancer TMA, significantly high NOD2 expression was confirmed as a predictive marker of endometrial cancer (Fig. 8a, p < 0.0001). However, NOD2 expression had no significant impact on the overall survival of patients, which may be related to the limited sample size of endometrial cancer tissue chips (n = 80). The results in Fig. 2 were 545 endometrial cancer cases from the TCGA database. Therefore, further investigation through multi-center clinical studies with larger sample size is warranted. The most intriguing discovery was the positive correlation between NOD2 and CD86 expression. This suggests that NOD2 receptor-mediated pyroptosis may promote tumor site inflammation and shift tumor-associated macrophages (TAM) from M2 to M1 phenotype, inhibiting tumor growth. Wang et al. [49] found that deleting NOD2 in lung adenocarcinoma cells can inhibit the NF-κB pathway, causing macrophages to switch from a protective M1 phenotype to a pro-tumor M2 subtype. They did not find a link between NOD2 and PDL1 in EC tissues. The multi-color immunofluorescence assay may result in interference between multiple antibodies due to the need for re-staining of multiple target molecules, potentially leading to false negative results. In conclusion, the elevated expression of NOD2 in endometrial carcinoma tissues enhances anti-tumor immunity and facilitates the eradication of tumor cells.

Conclusion

In conclusion, a prognostic model for endometrial cancer was constructed by conducting a thorough examination of the expression patterns of pyroptosis-related genes and their prognostic implications. Notably, NOD2 can serve as an independent prognostic indicator, exhibiting a connection with the infiltration of immune cells and the expression of immune checkpoints, suggesting that NOD2 may be a viable therapeutic target for the management of endometrial cancer. The results presented herein provide a basis for the customization of precise and personalized interventions for endometrial cancer, as well as a wide range of predictive biomarkers. Consequently, these findings hold the potential to improve the effectiveness of chemotherapy, molecularly targeted pharmacotherapy, and immunotherapy in a broader population of patients diagnosed with endometrial cancer.

Materials and methods

Datasets

The RNA-sequencing profiles and relevant clinical data of 545 UCEC patients were obtained from the TCGA dataset (https://portal.gdc.com). Additionally, 177 normal control samples were accessed from both the TCGA and Genome Type tissue expression (GTEx) datasets (V8) (https://www.gtexportal.org/home/datasets). The clinical parameters of patients with UCEC were documented in Table S1. A comprehensive set of 33 proptosis related genes (PRGs), previously identified in literature reviews [50], were listed in Table S2. Prior to additional analysis, the expression data was standardized to transcripts per kilobase million (TPM) values. Our statistical analyses were conducted using R software v4.0.3 (R Foundation for Statistical Computing, Vienna, Austria). Furthermore, we conducted an analysis of the expression of PRGs in the primary stage of UCEC by employing a box plot within the “pathological stage plot” column through the utilization of the GEPIA2 analyzer. Significance in the statistical analysis was determined by a p-value of less than 0.05.

Functional enrichment analysis

The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analysis, encompassing the biological processes (BP), cellular components (CC), and molecular functions (MF) categories, were performed using the Metascape Database (http://metascape.org). Additionally, this search tool was employed to construct a protein-protein interaction (PPI) network for 33 PRGs.

Subgroup analysis of PRGs genes

The ConsensusClusterPlus R package (v1.54.0) was utilized for consistency analysis, with a maximum of 6 clusters and 100 iterations of drawing 80% of the total sample. The clusterAlg parameter was set to “hc” with innerLinkage specified as ‘ward. D2’. Through the comprehensive expression analysis of five genes, distinct subgroups consisting of 545 tumor samples were identified using R’s ConsensusClusterPlus package (v1.54.0). Principal component analysis (PCA) was employed to validate the grouping results. Clustering heatmaps were generated using the pheatmap R software package (v1.0.12), with genes retained based on a standard deviation (SD) threshold of 0.1. The Kaplan-Meier method was employed to generate survival curves for three distinct subgroups.

The chemotherapeutic response for each sample was predicted using the Genomics of Drug Sensitivity in Cancer (GDSC) database (https://www.cancerrxgene.org/), which is the largest publicly available pharmacogenomics database. The prediction process was carried out using the R package “pRRophetic”. The estimation of the samples’ half-maximal inhibitory concentration (IC50) was performed through ridge regression. Additionally, the TIDE algorithm was utilized to predict potential immune checkpoint blockade (ICB) response.

The analysis methods and R package were implemented by the R Foundation for Statistical Computing (2020), specifically version 4.0.3.

Construction of the PRGs prognostic model

Univariate and multivariate Cox regression analyses were conducted to determine the appropriate variables for constructing the nomogram. Subsequently, a nomogram was developed using the findings from the multivariate Cox proportional hazards analysis, enabling the prediction of overall recurrence rates at 1, 3, and 5 years.

Utilizing five prognostic PRGs, the LASSO Cox regression analysis was employed to establish the prognostic model. Subsequently, the UCEC patients were stratified into low- and high-risk subgroups based on the median risk score, and the Kaplan-Meier method was utilized to compare the overall survival (OS) time between these subgroups. The time receiver-operating characteristic (ROC v0.4) analysis was conducted to assess the predictive accuracy of each gene and the risk score. Moreover, considering the clinical characteristics, a predictive nomogram was developed to estimate the 3-, 5-, and 10-year OS. The ‘forestplot’ R package was employed to display the p-value, hazard ratios (HRs) with 95% confidence intervals (CIs) of each variable within the forest.

Immune infiltration, TMB and MSI analysis

To obtain reliable results for the evaluation of immune scores, we utilized the TIMER algorithm within the immuneeconv R software package. Specifically, we selected SIGLEC15, TIGIT, CD274, HAVCR2, PDCD1, CTLA4, LAG3, and PDCD1LG2 as transcripts relevant to immune checkpoints, and extracted the expression values of these eight genes. Subsequently, analysis and visualization were conducted using the ggClusterNet package in R software. Additionally, the ggstatsplot package in R software was employed to depict the correlations between gene expression and immune scores, while the heatmap package was utilized for generating multi-gene correlations.

Spearman’s correlation analysis was conducted to determine the correlation between quantitative variables that did not follow a normal distribution, such as PRGs gene expression, tumor infiltrating immune cells, immune checkpoint expression, TMB, and MSI score. Statistical significance was defined as p values less than 0.05 (*p < 0.05).

Cell lines and culture

The human endometrial cancer cell lines AN3-CA (RRID: CVCL_0028), KLE (RRID: CVCL_1329), Ishikawa (RRID: CVCL_2529), RL95-2 (RRID: CVCL_0505), and HEC-1B (RRID: CVCL_0294), obtained from the American Type Culture Collection (ATCC), were cultured in DMEM-F12 medium (HyClone, SH30234.01) supplemented with 10% Foetal Bovine Serum (FBS), 1% penicillin, and 1% streptomycin. Human normal endometrial epithelial cells (hEEC), generously provided by Linlin Yang (International Peace Maternity and Child Health Hospital, China), were cultured in Epithelial Cell Medium (ScienCell, 4101). All cells were maintained in a controlled environment at 37 °C and 5% CO2. All human cell lines have been authenticated using STR profiling. All cells were mycoplasma-free.

After transfection, the total RNA and whole-cell lysates were collected for analysis using Western blots 48 h later.

RT-PCR

Total RNA was extracted from the above cells using the Trizol method. The reaction solution was prepared in a 0.2 mL Ep tube following the instructions provided by the TAKARA reverse transcription kit. The reverse transcription process was carried out at 37 ℃ for 15 minutes. The expression levels of the target gene and internal reference gene in the cell sample were determined using qPCR. Data analysis was performed using the RT2Profiler PCR Array Data Analysis system provided by QIAGEN Company. Primer sequence for CASP6, GPX4, NOD2, NLRP2, PRKACA and GAPDH (5′ to 3′):

CASP6-human-F: acaggaaagtttctcagcgc;

CAPS6-human-R: gcattgaggcaaaacaggga.

GPX4-human-F: ctcatcgacaagaacggctg;

GPX4-human-R: agaaatagtggggcaggtcc.

NOD2-human-F: caacacctccttgcagttcc;

NOD2-human-R: caatgttgttccccaccagg.

NLRP2-human-F: tgtttcccgcattgtgtgag;

NLRP2-human-R: cttcaagggccaaggagaga.

PRKACA-human-F: ccacaactgactggattgcc;

PRKACA-human-R: actccttgccacacttctca.

GAPDH-human-F: gcgagatccctccaaaatcaa;

GAPDH-human-R: gttcacacccatgacgaacat.

The PCR reaction was conducted under the following conditions: pre-denaturation at 95 °C for 10 min, followed by 40 cycles of denaturation at 95 °C for 15 s and annealing at 60 °C for 60 s. Dissociation curves were obtained by subjecting the reaction to a temperature range of 60 to 95 °C.

Western blots

The cells AN3-CA, KLE, Ishikawa, RL95-2, and HEC-1B, as well as hEEC cells, were collected at a concentration above 5 × 106. For Western blotting analysis, whole-cell extracts were lysed using RIPA lysis buffer. The protein lysates from the cells were then subjected to electrophoresis using a 10% sodium dodecyl sulfate-polyacrylamide gel (SDS-PAGE) under the following conditions: concentration at 100 V for 20 min and separation at 120 V for 60 min. The resulting protein bands were transferred onto nitrocellulose (NC) membranes. Subsequently, all NC membranes were blocked with 1×protein-free rapid blocking buffer at room temperature for 45 min. An additional incubation step was conducted at 4℃ overnight with anti-CASP6, anti-GPX4, anti-NOD2, anti-NLRP2, anti-PRKACA, and HRP-conjugated mouse anti-β-Tubulin (diluted to 1:1000). The membranes were subsequently washed three times in Tris-buffered saline containing 0.1% Tween-20 (TBST) for five minutes each. Following this, the membranes were incubated with corresponding secondary antibodies (diluted to 1:5000) for 1 h. Protein signals were visualized on an Image Quant LAS4000 system using an enhanced chemiluminescence reagent (Millipore WBKLS0500). These images were then subjected to semi-quantitative analysis using ImageJ 1.8.0 (USA) software and normalized to a background image.

Multiplex immunohistochemistry assay

The endometrial cancer tissue microarray (EC1601), acquired from Shanghai Liao ding Biotechnology Company, Ltd, comprises 80 pairs of endometrial cancer specimens along with corresponding adjacent tissue and associated follow-up data. Details on endometrial cancer patient tissue are provided in Figure S7. All human studies were conducted with the necessary approval from the ethics committee and Institutional Review Board of The Second Affiliated Hospital of Naval Medical University. The multiplex immunohistochemistry (mIHC) technique was employed, adhering to established protocols, which included heat-induced antigen-retrieval and removal of endogenous peroxidase procedures. The primary antibodies, namely anti-NOD2 (Signalway Antibody, 54121), anti-PDL1 (Abcarta, PA167), anti-PANCK (Abcarta, PA125), and anti-CD86 (Abcarta, PA250), were diluted at a ratio of 1:200 and applied individually. They were stored at room temperature for a duration of 1 h, followed by incubation at room temperature for 10 min with the addition of the secondary antibody. Subsequently, Opal dye diluent was added at a dilution ratio of 1:100 and incubated at room temperature for 10 min. DAPI dyeing and sealing were then performed after microwave treatment. Finally, all staining was captured using a multispectral fluorescence microscope.

Statistical analysis

The experiments were conducted independently in triplicate, and the data were presented as mean ± standard deviation (SD). Statistical analyses were performed using GraphPad Prism software version 8.0. Multiple group comparisons were conducted using a one-way ANOVA test. The expression of molecules in tissues was analyzed using the Mann-Whitney test. The relationship between molecular and clinical indicators was assessed using the Chi-square test, Kaplan-Meier survival analysis, and Log-rank Statistical test. A p-value of less than 0.05 was considered statistically significant.

Supplementary Information

Supplementary Material 1

Acknowledgements

The authors express their sincere gratitude for the support provided by the “Pyramid Talent Project” initiative, a three-year Action Plan for talent Construction of Shanghai Chang Zheng Hospital (YQ104) and the university-level research fund of the Naval Medical University (2023QN078).

Author contributions

Xiaodi Gong, Jianhong Dang and Xiaojun Liu conceived and designed the research. Xiaodi Gong, Zhifeng Wang and Jiahao You performed the experiments, Jinghai Gao and Kun Chen analyzed the data, Jing Chu and Xiaoxin Sui prepared the supplemental material . Both authors contributed to the article and approved the submitted version.

Data availability

The datasets utilized in this study are available for download from online repositories. For any additional inquiries, please contact the authors directly.

Declarations

Competing interests

The authors declare no competing interests.

Publisher’s note

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

Xiaodi Gong, Zhifeng Wang and Jiahao You have contributed equally to this work.
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