Pyroptosis-related genes expression and nomogram predict overall survival of gastric cancer

Pyroptosis-related genes expression and nomogram predict overall survival of gastric cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Pyroptosis-related genes expression and nomogram predict overall survival of gastric cancer Song Wang, Xing-Chuan Li, Jia-Rui Zhu, Jun-Tao Ran, Quan-Lin Guan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2993160/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The prognosis of gastric cancer remains poor. Pyroptosis-related genes (PRGs) have been investigated as a potential biomarker in several types of cancer, including gastric cancer. This study aimed to investigate the expression, mutation and diagnostic and prognostic value of PRGs, analyzing data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Methods RNA-sequencing data (RNA-seq), somatic datasets, and copy number variation (CNV) data for gastric cancer were also collected from the TCGA. Gene expression matrix and clinical information of GSE84437 were obtained from GEO data. Bioinformatics analysis was performed to investigate expression profiles of PRGs and their infiltration of immune cells, as well as prognostic significance in gastric cancer. Results A total of 22 out of 33 PRGs were up-regulated, only one PRGs was down-regulated in GC compared to normal tissues, while 10 of them showed no difference between the two groups. A total of 117 out of 433 (27.02%) gastric cancer samples demonstrated genetic mutations, missense mutation was the most common variant classification. More than half of the 33 PRGs had copy number amplification. We performed unsupervised consensus clustering based on the expression of PRGs. Two clusters associated with PRGs named cluster A and cluster B were identified in gastric cancer. Compared with cluster B, cluster A not only had worse overall survival, more patients younger than 65 years, and more deaths, but also had a lower infiltration level of T cell and greater activation B cells and mast cells. According to Gene set variation analysis, cluster A showed greater enrichment of vascular smooth muscle contraction, ECM receptor interaction and KEGG pathways of dilated cardiomyopathy. PRGs cluster B was markedly enriched in cytosolic DNA sensing, non-homologous end joining, and basal transcription KEGG pathways. Multivariate cox analyses revealed that CASP5 was the independent factor affecting the prognosis of patients with gastric cancer. The discriminative ability of the final model for overall survival was assessed using the C statistics, 0.651 for overall survival. A predictive nomogram suggested that 3-year and 5-year overall survival rates could be predicted relatively well compared to an ideal model across the entire cohort. Conclusions PRGs was relatively up-regulated in gastric cancer, it was associated with worse overall survival. The overall survival risk for an individual patient can be estimated using PRGs-based nomograms, which can lead to individualized therapeutic choices. Figures Figure 1 Figure 2 Figure 3 Introduction Gastric cancer remains one of the most common malignancies and the fourth leading cause of cancer-related death worldwide [ 1 ]. The survival rate is low, with an overall 5-year survival rate of 39%. Although many genetic markers predict gastric cancer prognosis, they are still in the molecular research stage and are of no significant clinical significance. Therefore, there is an urgent need to discover new clinical biomarkers to predict prognosis. Pyroptosis is a regulated cell death (RCD), characterized by gasdermin family protein-mediated (GSDM) pore formation, cell lysis and release of pro-inflammatory cytokines [ 2 – 3 ]. The classical pyroptosis pathway is mediated by the inflammasome, and the main types of inflammasome include NLRP1, NLRP3, NLRC4, AIM2 and along with others. Many studies have demonstrated the effect of pyroptosis on tumor cell proliferation, invasion and metastasis [ 4 – 6 ]. One of the Gasdermin family, GSDMD, participates in the key link in pyroptosis and is the ultimate and direct performer. The expression level of GSDMD in gastric cancer is lower than in normal tissues, which can significantly promote tumor proliferation in vivo and in vitro [ 7 ]. GSDME promotes inflammasome activation by regulating caspase-3, which is involved in the occurrence of gastric cancer, and chemotherapy can induce pyroptosis of GSDME-expressed gastric cancer cells [ 8 – 10 ]. The expression level of NLRC4 in gastric cancer cells is higher than that in normal gastric epithelial cells, which can activate caspase-1-mediated pyroptosis [ 11 ]. NLRP3 is also closely related to the occurrence and development of gastric cancer, and its mediated inflammatory response can accelerate the occurrence of gastric cancer [ 12 ]. Studies have proved that in the treatment of gastric cancer, drugs can induce apoptosis of cancer cells through NLRP3 mediated pyroptosis, thus achieving the goal of anticancer [ 13 – 14 ]. Many studies have shown that pyroptosis affects the proliferation, invasion and metastasis of tumor cells and therefore affects immune cell infiltration and cancer prognosis. High expression of GSDMD in gastric cancer is associated with invasive traits, and down-regulation of GSDMD expression can limit tumor growth and spread, which can be used as an indicator to assess tumor prognosis [ 7 ]. Ye et al. identified a novel pyroptosis-related gene that is associated with ovarian cancer prognosis and plays an important role in tumor immunity, which can be used to predict ovarian cancer prognosis [ 15 ]. Tumor cells pyroptosis will cause the infiltration of a large number of CD4 + and CD8 + T cells, causing acute inflammation and enhancing anti-tumor immunity [ 16 – 17 ]. Newly designed drugs induce GSDMD-mediated pyroptosis and enhance anti-tumor immunity [ 18 ]. These studies prove that pyroptosis is expected to be a new idea for targeted tumor immune therapy. In this study, expression levels of PRGs were tested in gastric cancer tissues and adjacent normal tissues. Unsupervised consensus clustering was performed to group tumor samples into subgroups based on the expression matrix of 33 PRGs using R software. Gene set variation analysis (GSVA) and Tumour Immune Estimation Resource (TIMER) analyses were performed to identify the most enriched signaling pathways and immune infiltration. A Cox regression model was used to develop a nomogram capable of predicting the overall survival of individual patients. Our data may provide additional evidence for prognostic biomarkers and therapeutic targets for gastric cancer. Materials and methods Datasets and preprocessing The RNA-sequencing (RNA-seq) data and clinical information of 414 GC patients and 30 normal adjacent cancer tissue samples were obtained from The Cancer Genome Atlas (TCGA) database ( https://cancergenome.nih.gov/ ). In addition, somatic datasets and copy number variation (CNV) data for GC were also downloaded from TCGA. The gene expression matrix and clinical information of GSE84437, including 433 gastric cancer samples, were obtained from the GEO data set ( https://www.ncbi.nlm.nih.gov/gds/ ). The clinical information of the GC patients is shown in Table S1 . Data analysis was performed with the R (version 4.2.1) and R Bioconductor packages. The expression data were normalised to transcripts per kilobase million (TPM) values before further analysis. Identification of differentially expressed PRGs A total of 33 PRGs were obtained from prior reviews [ 19 ], as shown in Table S2 . The differences in PRGs expression between GC tissues and normal adjacent cancer tissues were identified using the "limma" and "reshape2" R packages [ 15 ]. Mutation analysis of PRGs The mutation frequency and oncoplot waterfall plot of 33 PRGs in GC patients were generated using the "maftools" package in R. The location of CNV alteration of 33 PRGs on 23 chromosomes was plotted using the "RCircos" package in R. Unsupervised consensus clustering To identify PRGs mediated GC subtypes, unsupervised consensus clustering was performed to cluster tumor samples into subgroups based on the expression matrix of 33 PRGs using "ConsensusClusterPlus" R package. The following parameters were used for clustering: pItem: 0.8 (resampling 80% of any sample), distance: "euclidean", pFeature: 0.8(resampling 80% of any protein), number of repetitions: 1000 and clustering algorithm: k-means method. We increased the sample size by merging the TCGA gastric cancer dataset with GSE84437, reducing random errors and selective bias. The clustering that exhibited the most significant survival difference was considered. Gene set variation analysis and gene set enrichment analysis GSVA was conducted to quantify the pathway enrichment scores using "GSVA" R package [ 20 ]. Differential analysis was performed to determine the significantly enriched pathways in each cluster. Gene function enrichment analysis To further explore the biological functions of key PRGs, R software was used to perform Kyoto Encyclopaedia of Genes and Genomes (KEGG) signalling pathway and Gene Ontology (GO) function enrichment analyses. Statistically, P < 0.05 indicated that the enrichment was statistically significant. Construstruction of the pyroptosis-related gene prognostic model Cox regression analysis was conducted to assess the prognostic significance of the PRGs. Kaplan-Meier curves and hazard ratios (HRs) with 95% confidence intervals (CIs) were generated by log-rank tests and univariate Cox proportional hazard regression. Significant PRGs were selected for further analysis. The prognostic model was then built using multivariate cox regression analysis based on these prognostic PRGs. A nomogram was developed based on the results of multivariate Cox regression analysis using the “RMS” package in R(version 4.2.1). The nomogram's predictive performance was measured by the C-statistic and calibration with 1,000 bootstrap samples to reduce overfitting bias. Immune infiltration, tumour mutation burden, and microsatellite-instability analysis We then used the Tumour IMmune Estimation Resource (TIMER, https://cistrome.shinyapps.io/timer/ ) to analyze the correlation between prognostic PRGs and immune infiltration. TIMER is a comprehensive web portal for analyzing the infiltration of immune cells in tumors. The "Gene" module of TIMER was used to visualize the correlation between PRGs expression and immune infiltration levels in GC. Statistical analysis Survival curves were plotted using the Kaplan-Meier method, and differences were compared using the log-rank test. A multivariate Cox proportional hazards regression model was used to identify independent prognostic factors for OS. Univariate and multivariate Cox proportional hazards regression analyses were performed using R software (version 4.2.1). No significant collinearity or interactions were found. A nomogram was constructed based on multivariate Cox regression analysis results using the “RMS” R package. All tests were two-sided, with P < 0.05 indicating a statistically significant difference. The data analysis period was from September 1, 2022 to March 10, 2023. Results Expression of PRGs in GC Firstly, we explored the expression of the 33 PRGs in GC and normal gastric tissues using the TCGA GC dataset.The expression of GSDMC, GSDMD, AIM2, NLRP3, TNF, PYCARD, GSDME, GSDMB, GSDMA, NOD2, PJVK, NOD1, CASP4, CASP5, SCAF11, CASP8, NLRC4, GPX4, IL18, PLCG1, CASP6, CASP3 was up-regulated, while the expression of ELANE was down-regulated in GC compared with normal tissues (Fig. 1 A, all P < 0.05). The expression of IL6, CASP9, TIRAP, CASP1, IL1B, NLRP7, NLRP2, NLRP6, NLRP1, PRKACA was not difference between the two groups. Landscape of genetic variation of PRGs in GC We then summarised the incidence of somatic mutations and copy number variations (CNV) of 33 PRGs in GC.As shown in Fig. 1 B, 117 of 433 (27.02%) GC samples demonstrated genetic mutations, missense mutation was the most common variant classification.Single nucleotide polymorphism(SNP) was the most common variant type, and C > T ranked as the top SNV class(Fig. 1 B).The results also demonstrated PLCG1 as the highest mutation frequency gene, followed by CASP5, NLRP3 and CASP8, among PRGs (Fig. 1 B). Figure 1 C showed the location of CNV alterations of 33 PRGs on chromosomes.We also investigated 33 PRGs CNV alteration frequency.More than half of the 33 PRGs had copy number amplification, while the CNV deletion frequencies of CASP9, ELANE, SCAF11, GPX4, PJVK, TIRAP, NLRP6, NOD2, NLRC4, CASP3, CASP5, IL18, CASP4 and CASP1 were widespread (Fig. 1 D). Survival analysis of PRGs on GC Prognosis analysis of PRGs on OS was shown in Fig. 1 E.We found that the higher expression of PLCG1, NOD1, NLRP3, GPX4, IL6 in GC patients, the worse OS, while the higher expression of TNF, TIRAP, NLRP2, NLRP6, NLRP7, NOD2, CASP1, CASP1, CASP3, CASP4, CASP5, CASP6, CASP8, AIM2 in GC patients, the better OS. The above analyses revealed the multi-omics characteristics of PRGs in GC.Not only the genomic, CNV and transcriptomic changes, but also OS impacts, suggested that these PRGs were of most importance in GC. Consensus clustering analysis of PRGs In order to analyze the heterogeneity and effects of PRGs in GC, we performed the unsupervised consensus clustering based on the expression of PRGs.Interestingly, two PRGs associated clusters were identified in GC, termed here as PRGs cluster A and B(Fig. 2 A ). Cluster A patients had worse OS compared with cluster B cohort(Fig. 2 B ). We then compared the clinical characteristics of different cluster patients.No significant distribution difference was found in terms of gender, T stage and N stage.However, significant clinical age and survival status differences were observed among clusters (Fig. 2 C and Table 1 ). Table 1 clinical characteristics differences of PRGs associated cluster A and cluster B based on TCGA and GSE84437 data sets. Clinicopathological feature Cluster A N = 487 Cluster B N = 296 χ 2 P-value Age ≤ 65 290(59.5%) 147(49.7%) 7.296 0.007 * > 65 197(40.5%) 149(50.3%) Gender Female 159(32.6%) 104(35.1%) 0.510 0.475 Male 328(67.4%) 192(64.9%) T stage T1-2 82(16.8%) 58(19.6%) 0.953 0.329 T3-4 405(83.2%) 238(80.4%) N stage N0 108(22.2%) 81(27.4%) 2.706 0.100 N1-3 379(77.8%) 215(72.6%) Survival status Dead 234(48.0%) 115(38.9%) 6.304 0.012 * Alive 253(52.0%) 181(61.1%) To describe the biological behavior differences among two clusters, functional annotations were performed using GSVA algorithm.The results showed that the two PRGs clusters displayed significant differences in the KEGG pathways enriched (Fig. 2 D).PRGs cluster A showed higher enrichment of vascular smooth muscle contraction, ECM receptor interaction and Dilated cardiomyopathy KEGG pathways.PRGs cluster B was markedly enriched in cytosolic DNA sensing, non homologous end joining and basal transcription KEGG pathways. Identification of PRGs cluster tumor microenvironment infiltrating cells phenotypes Growing studies have shown that PRGs are involved in tumor immunity [ 21 – 22 ].Previous studies have shown that higher infiltration of immune cells such as CD8 T cell in the tumor microenvironment could suggest a better prognosis[ 23 – 24 ].To further demonstrated the association between PRGs and tumor microenvironment in GC, we subsequently quantified the infiltrating immune cells in these two PRGs cluster.The results showed that compared with PRGs cluster A,PRGs cluster B showed higher infiltration level of adaptive immune cells such as activated CD4 + T cell, Gamma delta T cell, Neutrophil, Type 17 T helper cell, Type2 T helper cell, lower activated B cell, Mast cell(Fig. 2 E). Gene expression differential between PRGs cluster A and B To systematically assess such impacts, analysis of gene expression differential between PRGs cluster A and B was performed.The result show that there was 2601 DEGs among the two groups.Then, we further clarify the function of DEGs, the pathways were analysed using GO and KEGG databases.We found that these 2601 DEGs were mainly involved in the regulation of mitotic cell cycle phase, transition nuclear division, collagen-containing, extracellular matrix cell-substrate junction, actin binding, ATP hydrolysis activity, extracellular matrix (Fig. 2 F).Moreover, KEGG pathway analysis suggested that these 2601 DEGs were mainly involved in the PI3K-Akt signaling pathway, MAPK signaling pathway and Focal adhesion (Fig. 2 G). Construction of a pyroptosis-related genes prognostic risk model Univariate Cox regression analysis was performed to screen those PRG with a prognostic value.As a result, age, T stage, N stage and a total of 4 genes(CASP5, CASP1, CASP8 and GPX4) with a prognostic value were identified(Table 2 ), and the Kaplan-Meier survival curves are shown in Fig. 3 A-G. Table 2 Univariate Cox regression analysis of GC patient based on TCGA and GEO data Variables HR HR.95L HR.95H P age ≤ 65 vs > 65 1.0268 1.0167 1.0370 < 0.01 T stage T1-2 vs T3-4 1.2298 1.0740 1.4082 < 0.01 N stage N0 vs N1-3 1.5325 1.3676 1.7173 < 0.01 CASP5 low vs high expression 0.8358 0.7455 0.93714 < 0.01 CASP1 low vs high expression 0.8871 0.81307 0.96799 < 0.01 CASP8 low vs high expression 0.7181 0.5577 0.9246 < 0.05 GPX4 low vs high expression 1.2109 1.0137 1.4464 65 years, T3-T4 stage, N1-N3 stage, low expression of CASP1, CASP5, CASP8 and high GPX4 expression (Fig. 3 A-E, p < 0.01).Multivariate cox regression analysis was performed to construct a prognostic gene model based on clinical features and prognostic PRGs. Multivariate analyses revealed that CASP5 expression, age, T stage and N stage were independent factors affecting the prognosis of GC patients. We also built a predictive nomogram considering the clinical features and prognostic PRGs to predict the survival probability. Nomograms and Model Performance Accurate prognostication for GC is vital, not only for informing patients about their risk of prognosis, but also for selecting patients for further adjuvant treatment.Nomogram to predict OS of the patients with GC are shown in Fig. 3 .The nomogram to predict OS was created based on the following 4 independent prognostic factors: age (≤ 65 or > 65 years), T stage (T1, T2, T3 or T4), N stage (N0, N1, N2 or N3), and CASP5 expression(low or high expression).Higher total points based on the sum of the assigned number of points for each factor in the nomograms were associated with a worse prognosis.The discriminative ability of the final model for OS was assessed using the C statistics, 0.651 for OS.The accuracy of the model and potential model overfit were assessed by bootstrap validation with 1000 resampling.The 120-sample bootstrapped calibration plot for the prediction of 3-year and 5-year OS is shown in Fig. 3 . Discussion In this study, we explored the expression of the 33 PRGs and identified 22 of the 33 PRGs up-regulated and only one of them down-regulated. The expression of PRGs in gastric cancer is relatively active. This was consistent with previous results [ 25 ]. Studies have shown that GSDMB is highly expressed in gastric cancer [ 26 – 27 ]. The up-regulated expression of GSDMC, CASP3 and GPX4 may promote apoptosis of cancer cells [ 28 – 30 ]. These studies suggest that the key gene of each component of pyroptosis is differentially expressed in gastric cancer and other cancers. These PRGs have the potential to be used as biomarkers in the diagnosis and treatment of gastric cancer. Inhibition of GPX4 expression can inhibit the vitality of liver cancer cells and induce pyroptosis [ 30 ], and up-regulation of NLRP6 can inhibit gastric cancer progression [ 31 ]. By increasing or inhibiting the expression of PRGs, inhibiting the vitality of tumor cells and inducing pyroptosis, we can achieve the goal of tumor treatment. Pyroptosis is a recently discovered programmed cell death that is associated with progression, prognosis, and response to treatment in gastric cancer. Pyroptosis can promote tumor cell death, making pyrolysis a potential prognostic and therapeutic target [ 6 , 19 , 32 ]. The results of the present study revealed global alterations in PRGs at the transcriptional and genetic levels in gastric cancer. Somatic mutation can lead to the occurrence of gastric cancer [ 33 – 34 ]. Our study found that the somatic mutation rate in gastric cancer was 27.02%. Further studies on somatic mutations, such as gene interaction and mutation sites of frequent somatic cell mutation genes, may analyze the role of somatic mutations in the occurrence and development of gastric cancer from the level of tumor genomics, identify different "subtypes" of somatic mutations in gastric cancer and provide a theoretical basis for targeted drug therapy [ 35 – 37 ]. Collectively, these studies reveal that the PRGS involved may provide potential biomarkers for early diagnosis and anti-tumor therapy. We used the TCGA and GEO databases to clarify the prognostic value of PRGs in gastric cancer. The study generated a signature with 19 PRGs and found it could predict overall survival in patients with gastric cancer. Previous studies have also shown that PRGs can effectively predict the prognosis of patients with lung adenocarcinoma and ovarian cancer [ 15 , 38 ]. A new signature of PRG (AIM2, PLCG1, ELANE, PJVK, CASP3, CASP6, and GSDMA) was identified to predict prognosis in ovarian cancer [ 15 ] and found that it could predict overall survival. Wang Qingqing discovered that decreased NLRP6 expression is associated with a poor prognosis in gastric cancer [ 31 ]. Based on the above research, we postulate that PRGs predict the prognosis of tumor patients, which will be the direction of future research and exploration. The molecular mechanisms underlying the differential expression of PRGs and their potential prognostic impact in gastric cancer are still poorly understood. Unsupervised consensus clustering based on the expression of PRGs, clusters A and B associated with PRGs were identified. Furthermore, we found that patients in cluster A had worse OS compared to the cluster B cohort. No significant distribution difference was found in terms of gender, T stage and N stage among them. Compared to cluster B, cluster A has more patients under 65 years and more deaths. We boldly presume that pyroptosis may be more likely to occur in patients with gastric cancer ≤ 65 years of age, as pyroptosis promotes gastric cancer progression and worsens the prognosis in these patients. This interesting phenomenon forces us to delve deeper into the reasons to design and implement in vivo and in vitro experiments for the molecular mechanisms behind it. First, differences in immune infiltration could be one of the reasons. Compared to cluster B, PRGs cluster A showed a lower infiltration level of T cell and greater activation of B cells and mast cells. This can be attributed to the regulation of immune micro-environment cells such as CD8 T cell in the immune system. The pyroptosis process is accompanied by the secretion of inflammatory factors and immune response. Some studies have found that GSDME increases phagocytosis of tumor related macrophages and the number and function of tumor-infiltrating NK cells and CD8 + T lymphocytes through pyroptosis [ 39 ]. This further confirms the correlation between the expression level of PRGs in gastric cancer and immune infiltration, providing a new approach for clinical immunotherapy. Deeper understanding of the role of pyroptosis in regulating the tumor immune micro-environment has expanded understanding of cancer, increased understanding of pyroptosis mechanisms and pathology, and revealed its role in tumor development and treatment. These findings will contribute to the future exploration of tumor immunotherapy based on pyroptosis. We also performed functional enrichment analysis of PRGs based on GSVA, which revealed that KEGG pathways were mainly different between cluster A and B. Activation and inhibition of different KEGG pathways lead to changes in clinical phenotype and prognosis in two groups of patients with gastric cancer. The results of the differential analysis of gene expression showed that there were 2601 DEGs between the two groups. Functional enrichment analysis of KEGG reveals that these 2601 DEGs were mainly involved in the PI3K-Akt signaling pathway, MAPK signaling pathway and focal adhesion KEGG pathways. These pathways have been correlated with the oncogenesis and progression of gastric cancer [ 40 – 42 ]. When the relevant signaling pathway is activated, it will affect tumor cell activities including cell proliferation, differentiation, cell vitality, cell stress and apoptosis. Significantly, our results using two different analyzes showed that they had many overlapping functional pathways such as DNA replication, RNA degradation, mismatch repair and the cell cycle. All these results do not further confirm the idea that these 33 PRGs play a vital role in the oncogenesis and progression of gastric cancer, but also reflect the consistency and stability of our research from a lateral perspective. In order to further explore the clinical application value of PRGs, we attempted to build a clinical prognosis model based on PRGs. Reliable and workable nomogram to predict gastric cancer overall survival is clinically valuable and difficult to create. Cox regression analysis was performed to build a prognostic genetic model based on five prognostic PRGs (CASP5, CASP1, CASP8 and GPX4), which could predict the overall survival of patients with gastric cancer with medium to high accuracy. Multivariate cox analyses revealed that CASP5 was the independent factor affecting the prognosis of patients with gastric cancer. The discrimination ability of the final model for overall survival was evaluated using the C statistics, 0.651 for overall survival. Although the accuracy and discrimination of a model with a biomarker may be limited, our result showed that the proposed nomogram provided a more accurate overall survival prediction for patients with gastric cancer than the AJCC TNM-based nomogram [ 43 – 44 ]. A predictive nomogram suggested that the 3-year and 5-year overall survival rates could be predicted relatively well compared to an ideal model across the entire cohort. Considering the graphical results, the 3-year-old overall survival are more persuasive than 5-year overall survival. This suggests that PRGs can be used as biomarkers for assessing the prognosis of gastric cancer. The limitations of our study included the following: first, as retrospective study has inherent defects such as selection bias. Second, gastric cancer is a complex disease and all kinds of clinical factors such as race, histology and treatment details should be considered to clarify the key role of PRGs in gastric cancer development; however, this information is absent or inconsistently available in public databases. Third, our nomograms have been internally validated using bootstrap validation and lack external validation. Finally, the present study was based on TCGA and GEO data mining; therefore, the protein level of PRGs expression could not be evaluated directly, and the signaling pathways involved in PRGs clusters in patients with gastric cancer need to be verified by in vivo and in vitro experiments. Conclusions In summary, our study demonstrated that most PRGs were abnormally up-regulated in gastric cancer. PRGs have been associated with tumor immunity. CASP5 was an independent risk factor for predicting overall survival in the TCGA and GEO cohorts. The overall survival risk for an individual patient can be estimated using PRGs-based nomograms, which can lead to individualized therapeutic choices. Declarations Ethics approval and consent to participate This study did not involve humans or animals. Consent for publication Not applicable. Availability of data and materials The original data of this study were downloaded from TCGA-STAD dataset (https://cancergenome.nih.gov/) and GEO dataset (https://www.ncbi.nlm.nih.gov/gds/GSE84437). The code and analysed data sets generated during the study are available from the corresponding author on reasonable request. Conflicts of Interest All authors have no conflicts of interest to declare. Funding This work was supported by the 2021 Innovation Fund Project of Education Department of Gansu Province(2021B-014), the 2019 Hospital Fund of The First Hospital of Lanzhou University (ldyyyn2019-53) ,Gansu Province Natural Science Foundation(21JR11RA111) and Education and Teaching Reform Project of Lanzhou University(2022-011). Author contributions Wang S, Li XC and Guan QL contributed to the study conception and design. Database searches, data extraction and analysis were performed by Wang S, Zhu JR and Li XC. The frist draft of the manuscript was written by Wang S, while Ran JT and Guan QL revised the manuscript. All authors approved the final version. Acknowledgements Thanks to TCGA and GEO database builders and participants, providing open access to gene expression and clinical phenotype data for authors. References Sung H, Ferlay J, Siegel RL,Laversanne M, Soerjomataram I,Jemal A,Bray F. 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Cancer Discov. 2020;10(2):254–69. 10.1158/2159-8290.CD-19-0672 . Liu YY, Fang YL, Chen XF, et al. Gasdermin E-mediatedtarget cell pyroptosis by CAR T cells triggers cytokine releasesyndrome. Sci Immunol. 2020;5(43):eaax7969. 10.1126/sciimmunol.aax7969 . Cheng HC, Ma KL, Zhang LJ, et al. The tumor microenvironment shapes the molecular characteristics of exhausted CD8 + T cells. Cancer Lett. 2021;28(506):55–66. 10.1016/j.canlet.2021.02.013 . Duan QQ, Zhang HL, Zheng JN, et al. Turning Cold into Hot:Firing up the Tumor Microenvironment. Trends Cancer. 2020;6(7):605–18. 10.1016/j.trecan.2020.02.022 . Yuan Fang,Shengwang Tian, Pan Y et al. Pyroptosis:A new frontier in cancer.Biomed Pharmacother. 2020;121:109595doi: 10.1016/j.biopha.2019.109595 . Komiyama H, Aoki A, Tanaka S, et al. Alu-derived cis-element regulates tumorigenesis- dependent gastric expression of GASDERMINB (GSDMB). Genes Genet Syst. 2010;85(1):75–83. 10.1266/ggs.85.75 . Zhou Z, He H, Wang K et al. Granzyme A from cytotoxic lympho-cytes cleaves GSDMB to trigger pyroptosis in target cells.Science.2020;368(6494):eaaz7548.doi: 10.1126/science.aaz7548 . Hou JW, Zhao RC, Xia WY, et al. PD-L1-mediated gasdermin C expression switches apoptosis to pyroptosis in cancer cells and facilitates tumour necrosis. Nat Cell Biol. 2020;22(10):1264–75. 10.1038/s41556-020-0575-z . Ke HaoL,Wang XP, Zhou Z, et al. Effect of weimaining on apoptosis and Caspase-3 expression in a breast cancer mouse model. J Ethnopharmacol. 2021;264:113363DOI. 10.1016/j.jep.2020.113363 . He GN, Bao Na-Ren, Wang S et al. Ketamine Induces Ferroptosis of Liver Cancer Cells by Targeting lncRNA PVT1/miR-214-3p/GPX4.Drug Des Devel Ther. 2021;15: 3965–78.DOI: 10.2147/DDDT.S332847 . Wang QQ, Wang CM,Chen JL. NLRP6,decreased in gastric cancer, suppresses tumorigenicity of gastric cancer cells. Cancer Manag Res. 2018;10:6431–44. 10.2147/CMAR.S182980 . Ruan J, Wang S, Wang J. Mechanism and regulation of pyroptosis-mediated in cancer cell death. Chem Biol Interact. 2020;323:109052DOI. 10.1016/j.cbi.2020.109052 . Bass AJ, Thorsson V, Shmulevich I, et al. Cancer Genome Atlas Research Network.Comprehensive molecular characterization of gastric adenocarcinoma. Nature. 2014;513(7517):202–9. 10.1038/nature13480 . Alexandrov LB, Nik-Zainal S, Wedge DC et al. Signatures of mutational processes in human cancer.Nature.2013;500(7463):415–21. doi: 10.1038/nature12477 . Wang K, Yuen ST, Xu JC, et al. Whole-genome sequencing and comprehensive molecular profiling identify new driver mutations in gastric cancer. Nat Genet. 2014;46(6):573–82. 10.1038/ng.2983 . Cho SY, Park JW, Liu Y et al. Sporadic Early-Onset Diffuse Gastric Cancers Have High Frequency of Somatic CDH1 Alterations,but Low Frequency of Somatic RHOA Mutations Compared With Late-Onset Cancers.Gastroenterology. 2017;153(2):536–549e26. doi: 10.1053/j.gastro . Wang SS, Kim KM, Ting JC, et al. Genomic landscape and genetic heterogeneity in gastric adenocarcinoma revealed by whole-genome sequencing. Nat Commun. 2014;5:5477. 10.1038/ncomms6477 . Lin WL, Chen Y, Wu B, Chen Y, Li ZW. Identification of the pyroptosis-related prognostic gene signature and the associated regulation axis in lung adenocarcinoma. Cell Death Discov. 2021;7(1):161. 10.1038/s41420-021-00557-2 . Zhang ZB, Zhang Y, Xia SY, Kong Q, Li SY, Liu X, Junqueira C, Meza-Sosa KF, Mok TM, Ansara J, Sengupta S, Yao YD, Wu H, Lieberman J. Immun Nat. 2020;579(7799):415–20. 10.1038/s41586-020-2071-9 . Gasdermin E suppresses tumour growth by activatinganti-tumour. Liu MR, Yang P, Fu DL, Gao T, Deng XY, Shao MJ, Liao JQ. Jiang H,Li XL.Allicin protects against myocardial I/R by accelerating angiogenesis via the miR-19a-3p/PI3K/AKT axis.Aging(Albany NY).2021;13(19):22843–55.doi: 10.18632/aging.203578 . Shu X, Zhan PP, Sun LX, Yu L, Liu J, Sun LC, Yang ZH, Ran YL. Sun YM.BCAT1 Activates PI3K/AKT/mTOR Pathway and Contributes to the Angiogenesis and Tumorigenicity of Gastric Cancer. Front Cell Dev Biol. 2021;9:659260. 10.3389/fcell.2021.659260 . He Y, Ge YG, Jiang MK, Zhou JD, Luo DK, Fan H, Shi L, Lin LL. Yang L.MiR-592 Promotes Gastric Cancer Proliferation,Migration,and Invasion Through the PI3K/AKT and MAPK/ERK Signaling Pathways by Targeting Spry2. Cell Physiol Biochem. 2018;47(4):1465–81. 10.1159/000490839 . Yang YM, Qu AL, Zhao R, Hua MM, Zhang X, Dong ZG, Zheng GX, Pan HW, Wang HC, Yang XY, Zhang Y. Genome-wide identification of a novel miRNA-based signature to predict recurrence in patients with gastric cancer. Mol Oncol. 2018;12(12):2072–84. 10.1002/1878-0261.12385 . Zhang ZQ, Dong YQ, Hua J, Xue H, Hu J, Jiang T, Shi LB, Du JJ. A five-miRNA signature predicts survival in gastric cancer using bioinformatics analysis. Gene. 2019;699:125–34. 10.1016/j.gene.2019.02.058 . Additional Declarations No competing interests reported. Supplementary Files TableS1TheclinicalinformationofGCpatientsbasedonTCGAdatabases.xlsx TableS2listof33pyroptosisgenes.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2993160","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":207903556,"identity":"d0d3d5f8-7e3e-4f92-8757-499c7dbb2a18","order_by":0,"name":"Song Wang","email":"","orcid":"","institution":"First Hospital of Lanzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Song","middleName":"","lastName":"Wang","suffix":""},{"id":207903557,"identity":"a8c54a95-66d6-428a-a802-9a8853dcbfb0","order_by":1,"name":"Xing-Chuan Li","email":"","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xing-Chuan","middleName":"","lastName":"Li","suffix":""},{"id":207903558,"identity":"ab4d68c3-7bdd-4418-9807-33d0447e1768","order_by":2,"name":"Jia-Rui Zhu","email":"","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jia-Rui","middleName":"","lastName":"Zhu","suffix":""},{"id":207903559,"identity":"d5487aff-ffbf-44de-8f1d-10a91803acf2","order_by":3,"name":"Jun-Tao Ran","email":"","orcid":"","institution":"First Hospital of Lanzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jun-Tao","middleName":"","lastName":"Ran","suffix":""},{"id":207903562,"identity":"29d42848-b559-4115-8185-3ffb91dc0f37","order_by":4,"name":"Quan-Lin Guan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYNCCAhsZMM1DvBaDNB6StRwmQYt8RPKzB28MzvPIz0hgfPC2jUHenJAWwxtp5oZzDG7zMM5IYDac28ZguLOBkJYZCWbSPEAtzBIJbNK8bQwJBgcIakn/BtRyjodNIoH9N1Fa5CVyQLYc4OEB2sJMlBYDnjdlknMMknkkeB42S845J2G4gaAt7enbJN5U2MnJtycf/PCmzEaesC0gBZDoYGwAEhIE1INsaWAgKdJHwSgYBaNgJAIAmpY1ZIp6fkAAAAAASUVORK5CYII=","orcid":"","institution":"First Hospital of Lanzhou University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Quan-Lin","middleName":"","lastName":"Guan","suffix":""}],"badges":[],"createdAt":"2023-05-29 01:44:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2993160/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2993160/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":38299567,"identity":"0de05600-0dce-457b-8638-ae37d116adb2","added_by":"auto","created_at":"2023-06-09 15:58:32","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1077456,"visible":true,"origin":"","legend":"\u003cp\u003eLandscape of genetic and expression variation of PRGs in gastric cancer.(A) The expression of 33 PRGs in normal gastric tissues and GC, Tumour, red;Normal, blue.The upper and lower ends of the boxes represented the interquartile range of values.The lines in the boxes represented median value.* P \u0026lt; 0.05, ** P \u0026lt; 0.01, *** P \u0026lt; 0.001;(B) The location of CNV alteration of 33 PRGs on 23 chromosomes in theTCGA GC cohort.(C) The mutation frequency and classification of 33 PRGs in GC.(D) The CNV variation frequency of 33 PRGs.The height of the column represented the alteration frequency.(E) Prognosis network diagram of pyroptosis-related genes, among them, the purple node represents prognosis high-risk genes, the green node represents prognosis low-risk genes, the node size represents P-values, the gray line represents positive co-expression, and the blue line represents negative co-expression.From the density of the lines, it can be inferred that there is a close relationship between them.PRG, pyroptosis-related genes;GC, gastric cancer; INS insertion, DEL deletion.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2993160/v1/84289da8dae6c2c67e227361.jpg"},{"id":38299569,"identity":"cc5d37f2-5f50-4a97-9524-5c1aa725eda3","added_by":"auto","created_at":"2023-06-09 15:58:32","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1870690,"visible":true,"origin":"","legend":"\u003cp\u003eConsensus clustering analysis of 33 PRGs with abnormal expression in GC. (A) Unsupervised consensus clustering based on the expression of PRGs, two PRGs associated clusters were identified termed here as PRGs cluster A and B; (B) The clinical outcomes of these two clusters also varied significantly, cluster A patients had worse OS compared with cluster B cohort;(C) Heatmap showing the clinical characteristics differences among PRGs clusters (adjusted P-value \u0026lt;0.05);(D) Heatmap showing the biological pathway differences among PRGs clusters (adjusted P-value \u0026lt;0.05);(E) Comparison of the abundance of infiltrating immune cells among PRGs clusters (*P \u0026lt; 0.05; **P \u0026lt; 0.01;***P \u0026lt; 0.001); F and G, the functional enrichment analysis of DEGs among PRGs clusters in GC. (F) the enriched item in gene ontology analysis; (G) the enriched item in Kyoto Encyclopedia of Genes and Genomes analysis.The size of circles represented the number of genes enriched.BP biological process, CC cellularcomponent, MF molecular function, PRG pyroptosis-related gene.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2993160/v1/17a9ff9ae130bcdea125c935.jpg"},{"id":38299575,"identity":"bd249c8f-cd53-4c6b-a38c-e3e78b48f9ba","added_by":"auto","created_at":"2023-06-09 15:58:32","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3034455,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate Cox proportional hazards regression model and nomogram for predicting GC OS.(A-G) Age, T stage, N stage and a total of 4 PRGs(CASP5, CASP1, CASP8 and GPX4) with a prognostic value were identified;(H) Nomogram for predicting OS in GC patients.(I-J )Calibration plot comparing predicted and actual survival probabilities at the 3-year follow-up.The 120-sample bootstrapped calibration plot for 3-year OS and 5-year OS prediction is shown.The 45-degree line represents the ideal fit; rhombuses represent nomogram-predicted probabilities; crosses represent the bootstrap-corrected estimates; and error bars represent the 95% CIs of these estimates.OS, overall survival; RFS, recurrence-free survival; CI, confidence interval.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2993160/v1/402a5e1f8d8c8953c4242bfb.jpg"},{"id":38651735,"identity":"42be679b-93c1-4255-98aa-653e170bd523","added_by":"auto","created_at":"2023-06-16 10:14:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":780699,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2993160/v1/b5543139-407c-4b2f-b040-e7802568fd72.pdf"},{"id":38299571,"identity":"466f62cb-2316-4f3c-ba4b-17d6a14db80c","added_by":"auto","created_at":"2023-06-09 15:58:32","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":46796,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1TheclinicalinformationofGCpatientsbasedonTCGAdatabases.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2993160/v1/382febaf718cfcd77077d1a2.xlsx"},{"id":38300776,"identity":"31c6934d-405a-46f8-b3d5-d4578731a3cc","added_by":"auto","created_at":"2023-06-09 16:06:32","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":9389,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2listof33pyroptosisgenes.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2993160/v1/69d40c5420d6296375aae761.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pyroptosis-related genes expression and nomogram predict overall survival of gastric cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGastric cancer remains one of the most common malignancies and the fourth leading cause of cancer-related death worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The survival rate is low, with an overall 5-year survival rate of 39%. Although many genetic markers predict gastric cancer prognosis, they are still in the molecular research stage and are of no significant clinical significance. Therefore, there is an urgent need to discover new clinical biomarkers to predict prognosis.\u003c/p\u003e \u003cp\u003ePyroptosis is a regulated cell death (RCD), characterized by gasdermin family protein-mediated (GSDM) pore formation, cell lysis and release of pro-inflammatory cytokines [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The classical pyroptosis pathway is mediated by the inflammasome, and the main types of inflammasome include NLRP1, NLRP3, NLRC4, AIM2 and along with others. Many studies have demonstrated the effect of pyroptosis on tumor cell proliferation, invasion and metastasis [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. One of the Gasdermin family, GSDMD, participates in the key link in pyroptosis and is the ultimate and direct performer. The expression level of GSDMD in gastric cancer is lower than in normal tissues, which can significantly promote tumor proliferation in vivo and in vitro [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. GSDME promotes inflammasome activation by regulating caspase-3, which is involved in the occurrence of gastric cancer, and chemotherapy can induce pyroptosis of GSDME-expressed gastric cancer cells [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The expression level of NLRC4 in gastric cancer cells is higher than that in normal gastric epithelial cells, which can activate caspase-1-mediated pyroptosis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. NLRP3 is also closely related to the occurrence and development of gastric cancer, and its mediated inflammatory response can accelerate the occurrence of gastric cancer [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Studies have proved that in the treatment of gastric cancer, drugs can induce apoptosis of cancer cells through NLRP3 mediated pyroptosis, thus achieving the goal of anticancer [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMany studies have shown that pyroptosis affects the proliferation, invasion and metastasis of tumor cells and therefore affects immune cell infiltration and cancer prognosis. High expression of GSDMD in gastric cancer is associated with invasive traits, and down-regulation of GSDMD expression can limit tumor growth and spread, which can be used as an indicator to assess tumor prognosis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Ye et al. identified a novel pyroptosis-related gene that is associated with ovarian cancer prognosis and plays an important role in tumor immunity, which can be used to predict ovarian cancer prognosis [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Tumor cells pyroptosis will cause the infiltration of a large number of CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cells, causing acute inflammation and enhancing anti-tumor immunity [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Newly designed drugs induce GSDMD-mediated pyroptosis and enhance anti-tumor immunity [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These studies prove that pyroptosis is expected to be a new idea for targeted tumor immune therapy.\u003c/p\u003e \u003cp\u003eIn this study, expression levels of PRGs were tested in gastric cancer tissues and adjacent normal tissues. Unsupervised consensus clustering was performed to group tumor samples into subgroups based on the expression matrix of 33 PRGs using R software. Gene set variation analysis (GSVA) and Tumour Immune Estimation Resource (TIMER) analyses were performed to identify the most enriched signaling pathways and immune infiltration. A Cox regression model was used to develop a nomogram capable of predicting the overall survival of individual patients. Our data may provide additional evidence for prognostic biomarkers and therapeutic targets for gastric cancer.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDatasets and preprocessing\u003c/h2\u003e \u003cp\u003eThe RNA-sequencing (RNA-seq) data and clinical information of 414 GC patients and 30 normal adjacent cancer tissue samples were obtained from The Cancer Genome Atlas (TCGA) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cancergenome.nih.gov/\u003c/span\u003e\u003cspan address=\"https://cancergenome.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In addition, somatic datasets and copy number variation (CNV) data for GC were also downloaded from TCGA. The gene expression matrix and clinical information of GSE84437, including 433 gastric cancer samples, were obtained from the GEO data set (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/gds/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/gds/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The clinical information of the GC patients is shown in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Data analysis was performed with the R (version 4.2.1) and R Bioconductor packages. The expression data were normalised to transcripts per kilobase million (TPM) values before further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of differentially expressed PRGs\u003c/h2\u003e \u003cp\u003eA total of 33 PRGs were obtained from prior reviews [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], as shown in Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. The differences in PRGs expression between GC tissues and normal adjacent cancer tissues were identified using the \"limma\" and \"reshape2\" R packages [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMutation analysis of PRGs\u003c/h2\u003e \u003cp\u003eThe mutation frequency and oncoplot waterfall plot of 33 PRGs in GC patients were generated using the \"maftools\" package in R. The location of CNV alteration of 33 PRGs on 23 chromosomes was plotted using the \"RCircos\" package in R.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eUnsupervised consensus clustering\u003c/h2\u003e \u003cp\u003eTo identify PRGs mediated GC subtypes, unsupervised consensus clustering was performed to cluster tumor samples into subgroups based on the expression matrix of 33 PRGs using \"ConsensusClusterPlus\" R package. The following parameters were used for clustering: pItem: 0.8 (resampling 80% of any sample), distance: \"euclidean\", pFeature: 0.8(resampling 80% of any protein), number of repetitions: 1000 and clustering algorithm: k-means method. We increased the sample size by merging the TCGA gastric cancer dataset with GSE84437, reducing random errors and selective bias. The clustering that exhibited the most significant survival difference was considered.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGene set variation analysis and gene set enrichment analysis\u003c/h2\u003e \u003cp\u003eGSVA was conducted to quantify the pathway enrichment scores using \"GSVA\" R package [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Differential analysis was performed to determine the significantly enriched pathways in each cluster.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGene function enrichment analysis\u003c/h2\u003e \u003cp\u003eTo further explore the biological functions of key PRGs, R software was used to perform Kyoto Encyclopaedia of Genes and Genomes (KEGG) signalling pathway and Gene Ontology (GO) function enrichment analyses. Statistically, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated that the enrichment was statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eConstrustruction of the pyroptosis-related gene prognostic model\u003c/h2\u003e \u003cp\u003eCox regression analysis was conducted to assess the prognostic significance of the PRGs. Kaplan-Meier curves and hazard ratios (HRs) with 95% confidence intervals (CIs) were generated by log-rank tests and univariate Cox proportional hazard regression. Significant PRGs were selected for further analysis. The prognostic model was then built using multivariate cox regression analysis based on these prognostic PRGs. A nomogram was developed based on the results of multivariate Cox regression analysis using the \u0026ldquo;RMS\u0026rdquo; package in R(version 4.2.1). The nomogram's predictive performance was measured by the C-statistic and calibration with 1,000 bootstrap samples to reduce overfitting bias.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eImmune infiltration, tumour mutation burden, and microsatellite-instability analysis\u003c/h2\u003e \u003cp\u003eWe then used the Tumour IMmune Estimation Resource (TIMER, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cistrome.shinyapps.io/timer/\u003c/span\u003e\u003cspan address=\"https://cistrome.shinyapps.io/timer/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to analyze the correlation between prognostic PRGs and immune infiltration. TIMER is a comprehensive web portal for analyzing the infiltration of immune cells in tumors. The \"Gene\" module of TIMER was used to visualize the correlation between PRGs expression and immune infiltration levels in GC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eSurvival curves were plotted using the Kaplan-Meier method, and differences were compared using the log-rank test. A multivariate Cox proportional hazards regression model was used to identify independent prognostic factors for OS. Univariate and multivariate Cox proportional hazards regression analyses were performed using R software (version 4.2.1). No significant collinearity or interactions were found. A nomogram was constructed based on multivariate Cox regression analysis results using the \u0026ldquo;RMS\u0026rdquo; R package. All tests were two-sided, with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating a statistically significant difference. The data analysis period was from September 1, 2022 to March 10, 2023.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eExpression of PRGs in GC\u003c/h2\u003e \u003cp\u003eFirstly, we explored the expression of the 33 PRGs in GC and normal gastric tissues using the TCGA GC dataset.The expression of GSDMC, GSDMD, AIM2, NLRP3, TNF, PYCARD, GSDME, GSDMB, GSDMA, NOD2, PJVK, NOD1, CASP4, CASP5, SCAF11, CASP8, NLRC4, GPX4, IL18, PLCG1, CASP6, CASP3 was up-regulated, while the expression of ELANE was down-regulated in GC compared with normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The expression of IL6, CASP9, TIRAP, CASP1, IL1B, NLRP7, NLRP2, NLRP6, NLRP1, PRKACA was not difference between the two groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLandscape of genetic variation of PRGs in GC\u003c/h2\u003e \u003cp\u003eWe then summarised the incidence of somatic mutations and copy number variations (CNV) of 33 PRGs in GC.As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, 117 of 433 (27.02%) GC samples demonstrated genetic mutations, missense mutation was the most common variant classification.Single nucleotide polymorphism(SNP) was the most common variant type, and C\u0026thinsp;\u0026gt;\u0026thinsp;T ranked as the top SNV class(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).The results also demonstrated PLCG1 as the highest mutation frequency gene, followed by CASP5, NLRP3 and CASP8, among PRGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC showed the location of CNV alterations of 33 PRGs on chromosomes.We also investigated 33 PRGs CNV alteration frequency.More than half of the 33 PRGs had copy number amplification, while the CNV deletion frequencies of CASP9, ELANE, SCAF11, GPX4, PJVK, TIRAP, NLRP6, NOD2, NLRC4, CASP3, CASP5, IL18, CASP4 and CASP1 were widespread (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSurvival analysis of PRGs on GC\u003c/h2\u003e \u003cp\u003ePrognosis analysis of PRGs on OS was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE.We found that the higher expression of PLCG1, NOD1, NLRP3, GPX4, IL6 in GC patients, the worse OS, while the higher expression of TNF, TIRAP, NLRP2, NLRP6, NLRP7, NOD2, CASP1, CASP1, CASP3, CASP4, CASP5, CASP6, CASP8, AIM2 in GC patients, the better OS.\u003c/p\u003e \u003cp\u003eThe above analyses revealed the multi-omics characteristics of PRGs in GC.Not only the genomic, CNV and transcriptomic changes, but also OS impacts, suggested that these PRGs were of most importance in GC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eConsensus clustering analysis of PRGs\u003c/h2\u003e \u003cp\u003eIn order to analyze the heterogeneity and effects of PRGs in GC, we performed the unsupervised consensus clustering based on the expression of PRGs.Interestingly, two PRGs associated clusters were identified in GC, termed here as PRGs cluster A and B(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA ). Cluster A patients had worse OS compared with cluster B cohort(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB ).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe then compared the clinical characteristics of different cluster patients.No significant distribution difference was found in terms of gender, T stage and N stage.However, significant clinical age and survival status differences were observed among clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e ).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eclinical characteristics differences of PRGs associated cluster A and cluster B based on TCGA and GSE84437 data sets.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinicopathological feature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCluster A\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;487\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCluster B\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;296\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e290(59.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e147(49.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e197(40.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e149(50.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e159(32.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e104(35.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e328(67.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e192(64.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82(16.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58(19.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e405(83.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e238(80.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e108(22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81(27.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e379(77.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e215(72.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurvival status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e234(48.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e115(38.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e253(52.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e181(61.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo describe the biological behavior differences among two clusters, functional annotations were performed using GSVA algorithm.The results showed that the two PRGs clusters displayed significant differences in the KEGG pathways enriched (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).PRGs cluster A showed higher enrichment of vascular smooth muscle contraction, ECM receptor interaction and Dilated cardiomyopathy KEGG pathways.PRGs cluster B was markedly enriched in cytosolic DNA sensing, non homologous end joining and basal transcription KEGG pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of PRGs cluster tumor microenvironment infiltrating cells phenotypes\u003c/h2\u003e \u003cp\u003eGrowing studies have shown that PRGs are involved in tumor immunity [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].Previous studies have shown that higher infiltration of immune cells such as CD8 T cell in the tumor microenvironment could suggest a better prognosis[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].To further demonstrated the association between PRGs and tumor microenvironment in GC, we subsequently quantified the infiltrating immune cells in these two PRGs cluster.The results showed that compared with PRGs cluster A,PRGs cluster B showed higher infiltration level of adaptive immune cells such as activated CD4\u003csup\u003e+\u003c/sup\u003e T cell, Gamma delta T cell, Neutrophil, Type 17 T helper cell, Type2 T helper cell, lower activated B cell, Mast cell(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eGene expression differential between PRGs cluster A and B\u003c/h2\u003e \u003cp\u003eTo systematically assess such impacts, analysis of gene expression differential between PRGs cluster A and B was performed.The result show that there was 2601 DEGs among the two groups.Then, we further clarify the function of DEGs, the pathways were analysed using GO and KEGG databases.We found that these 2601 DEGs were mainly involved in the regulation of mitotic cell cycle phase, transition nuclear division, collagen-containing, extracellular matrix cell-substrate junction, actin binding, ATP hydrolysis activity, extracellular matrix (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF).Moreover, KEGG pathway analysis suggested that these 2601 DEGs were mainly involved in the PI3K-Akt signaling pathway, MAPK signaling pathway and Focal adhesion (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of a pyroptosis-related genes prognostic risk model\u003c/h2\u003e \u003cp\u003eUnivariate Cox regression analysis was performed to screen those PRG with a prognostic value.As a result, age, T stage, N stage and a total of 4 genes(CASP5, CASP1, CASP8 and GPX4) with a prognostic value were identified(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), and the Kaplan-Meier survival curves are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-G.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate Cox regression analysis of GC patient based on TCGA and GEO data\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR.95L\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR.95H\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage\u003c/p\u003e \u003cp\u003e\u0026le;\u0026thinsp;65 vs\u0026thinsp;\u0026gt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT stage\u003c/p\u003e \u003cp\u003eT1-2 vs T3-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.2298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.4082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN stage\u003c/p\u003e \u003cp\u003eN0 vs N1-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.5325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.3676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.7173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCASP5\u003c/p\u003e \u003cp\u003elow vs high expression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.93714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCASP1\u003c/p\u003e \u003cp\u003elow vs high expression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.81307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.96799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCASP8\u003c/p\u003e \u003cp\u003elow vs high expression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGPX4\u003c/p\u003e \u003cp\u003elow vs high expression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.2109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.4464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results suggested a poor survival rate in GC patients with age\u0026thinsp;\u0026gt;\u0026thinsp;65 years, T3-T4 stage, N1-N3 stage, low expression of CASP1, CASP5, CASP8 and high GPX4 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-E, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).Multivariate cox regression analysis was performed to construct a prognostic gene model based on clinical features and prognostic PRGs. Multivariate analyses revealed that CASP5 expression, age, T stage and N stage were independent factors affecting the prognosis of GC patients. We also built a predictive nomogram considering the clinical features and prognostic PRGs to predict the survival probability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eNomograms and Model Performance\u003c/h2\u003e \u003cp\u003eAccurate prognostication for GC is vital, not only for informing patients about their risk of prognosis, but also for selecting patients for further adjuvant treatment.Nomogram to predict OS of the patients with GC are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.The nomogram to predict OS was created based on the following 4 independent prognostic factors: age (\u0026le;\u0026thinsp;65 or \u0026gt;\u0026thinsp;65 years), T stage (T1, T2, T3 or T4), N stage (N0, N1, N2 or N3), and CASP5 expression(low or high expression).Higher total points based on the sum of the assigned number of points for each factor in the nomograms were associated with a worse prognosis.The discriminative ability of the final model for OS was assessed using the C statistics, 0.651 for OS.The accuracy of the model and potential model overfit were assessed by bootstrap validation with 1000 resampling.The 120-sample bootstrapped calibration plot for the prediction of 3-year and 5-year OS is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we explored the expression of the 33 PRGs and identified 22 of the 33 PRGs up-regulated and only one of them down-regulated. The expression of PRGs in gastric cancer is relatively active. This was consistent with previous results [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Studies have shown that GSDMB is highly expressed in gastric cancer [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The up-regulated expression of GSDMC, CASP3 and GPX4 may promote apoptosis of cancer cells [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. These studies suggest that the key gene of each component of pyroptosis is differentially expressed in gastric cancer and other cancers. These PRGs have the potential to be used as biomarkers in the diagnosis and treatment of gastric cancer. Inhibition of GPX4 expression can inhibit the vitality of liver cancer cells and induce pyroptosis [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and up-regulation of NLRP6 can inhibit gastric cancer progression [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. By increasing or inhibiting the expression of PRGs, inhibiting the vitality of tumor cells and inducing pyroptosis, we can achieve the goal of tumor treatment.\u003c/p\u003e \u003cp\u003ePyroptosis is a recently discovered programmed cell death that is associated with progression, prognosis, and response to treatment in gastric cancer. Pyroptosis can promote tumor cell death, making pyrolysis a potential prognostic and therapeutic target [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The results of the present study revealed global alterations in PRGs at the transcriptional and genetic levels in gastric cancer. Somatic mutation can lead to the occurrence of gastric cancer [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Our study found that the somatic mutation rate in gastric cancer was 27.02%. Further studies on somatic mutations, such as gene interaction and mutation sites of frequent somatic cell mutation genes, may analyze the role of somatic mutations in the occurrence and development of gastric cancer from the level of tumor genomics, identify different \"subtypes\" of somatic mutations in gastric cancer and provide a theoretical basis for targeted drug therapy [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Collectively, these studies reveal that the PRGS involved may provide potential biomarkers for early diagnosis and anti-tumor therapy.\u003c/p\u003e \u003cp\u003eWe used the TCGA and GEO databases to clarify the prognostic value of PRGs in gastric cancer. The study generated a signature with 19 PRGs and found it could predict overall survival in patients with gastric cancer. Previous studies have also shown that PRGs can effectively predict the prognosis of patients with lung adenocarcinoma and ovarian cancer [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. A new signature of PRG (AIM2, PLCG1, ELANE, PJVK, CASP3, CASP6, and GSDMA) was identified to predict prognosis in ovarian cancer [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and found that it could predict overall survival. Wang Qingqing discovered that decreased NLRP6 expression is associated with a poor prognosis in gastric cancer [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Based on the above research, we postulate that PRGs predict the prognosis of tumor patients, which will be the direction of future research and exploration.\u003c/p\u003e \u003cp\u003eThe molecular mechanisms underlying the differential expression of PRGs and their potential prognostic impact in gastric cancer are still poorly understood. Unsupervised consensus clustering based on the expression of PRGs, clusters A and B associated with PRGs were identified. Furthermore, we found that patients in cluster A had worse OS compared to the cluster B cohort. No significant distribution difference was found in terms of gender, T stage and N stage among them. Compared to cluster B, cluster A has more patients under 65 years and more deaths. We boldly presume that pyroptosis may be more likely to occur in patients with gastric cancer\u0026thinsp;\u0026le;\u0026thinsp;65 years of age, as pyroptosis promotes gastric cancer progression and worsens the prognosis in these patients. This interesting phenomenon forces us to delve deeper into the reasons to design and implement in vivo and in vitro experiments for the molecular mechanisms behind it. First, differences in immune infiltration could be one of the reasons. Compared to cluster B, PRGs cluster A showed a lower infiltration level of T cell and greater activation of B cells and mast cells. This can be attributed to the regulation of immune micro-environment cells such as CD8 T cell in the immune system. The pyroptosis process is accompanied by the secretion of inflammatory factors and immune response. Some studies have found that GSDME increases phagocytosis of tumor related macrophages and the number and function of tumor-infiltrating NK cells and CD8\u0026thinsp;+\u0026thinsp;T lymphocytes through pyroptosis [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. This further confirms the correlation between the expression level of PRGs in gastric cancer and immune infiltration, providing a new approach for clinical immunotherapy. Deeper understanding of the role of pyroptosis in regulating the tumor immune micro-environment has expanded understanding of cancer, increased understanding of pyroptosis mechanisms and pathology, and revealed its role in tumor development and treatment. These findings will contribute to the future exploration of tumor immunotherapy based on pyroptosis.\u003c/p\u003e \u003cp\u003eWe also performed functional enrichment analysis of PRGs based on GSVA, which revealed that KEGG pathways were mainly different between cluster A and B. Activation and inhibition of different KEGG pathways lead to changes in clinical phenotype and prognosis in two groups of patients with gastric cancer. The results of the differential analysis of gene expression showed that there were 2601 DEGs between the two groups. Functional enrichment analysis of KEGG reveals that these 2601 DEGs were mainly involved in the PI3K-Akt signaling pathway, MAPK signaling pathway and focal adhesion KEGG pathways. These pathways have been correlated with the oncogenesis and progression of gastric cancer [\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. When the relevant signaling pathway is activated, it will affect tumor cell activities including cell proliferation, differentiation, cell vitality, cell stress and apoptosis. Significantly, our results using two different analyzes showed that they had many overlapping functional pathways such as DNA replication, RNA degradation, mismatch repair and the cell cycle. All these results do not further confirm the idea that these 33 PRGs play a vital role in the oncogenesis and progression of gastric cancer, but also reflect the consistency and stability of our research from a lateral perspective.\u003c/p\u003e \u003cp\u003eIn order to further explore the clinical application value of PRGs, we attempted to build a clinical prognosis model based on PRGs. Reliable and workable nomogram to predict gastric cancer overall survival is clinically valuable and difficult to create. Cox regression analysis was performed to build a prognostic genetic model based on five prognostic PRGs (CASP5, CASP1, CASP8 and GPX4), which could predict the overall survival of patients with gastric cancer with medium to high accuracy. Multivariate cox analyses revealed that CASP5 was the independent factor affecting the prognosis of patients with gastric cancer. The discrimination ability of the final model for overall survival was evaluated using the C statistics, 0.651 for overall survival. Although the accuracy and discrimination of a model with a biomarker may be limited, our result showed that the proposed nomogram provided a more accurate overall survival prediction for patients with gastric cancer than the AJCC TNM-based nomogram [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. A predictive nomogram suggested that the 3-year and 5-year overall survival rates could be predicted relatively well compared to an ideal model across the entire cohort. Considering the graphical results, the 3-year-old overall survival are more persuasive than 5-year overall survival. This suggests that PRGs can be used as biomarkers for assessing the prognosis of gastric cancer.\u003c/p\u003e \u003cp\u003eThe limitations of our study included the following: first, as retrospective study has inherent defects such as selection bias. Second, gastric cancer is a complex disease and all kinds of clinical factors such as race, histology and treatment details should be considered to clarify the key role of PRGs in gastric cancer development; however, this information is absent or inconsistently available in public databases. Third, our nomograms have been internally validated using bootstrap validation and lack external validation. Finally, the present study was based on TCGA and GEO data mining; therefore, the protein level of PRGs expression could not be evaluated directly, and the signaling pathways involved in PRGs clusters in patients with gastric cancer need to be verified by in vivo and in vitro experiments.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, our study demonstrated that most PRGs were abnormally up-regulated in gastric cancer. PRGs have been associated with tumor immunity. CASP5 was an independent risk factor for predicting overall survival in the TCGA and GEO cohorts. The overall survival risk for an individual patient can be estimated using PRGs-based nomograms, which can lead to individualized therapeutic choices.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not involve humans or animals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original data\u0026nbsp;of this study were downloaded from\u0026nbsp;TCGA-STAD dataset\u0026nbsp;(https://cancergenome.nih.gov/)\u0026nbsp;and GEO dataset\u0026nbsp;(https://www.ncbi.nlm.nih.gov/gds/GSE84437). The code and analysed data sets generated during the study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the 2021 Innovation Fund Project of Education Department of Gansu Province(2021B-014), the 2019 Hospital Fund of The First Hospital of Lanzhou University (ldyyyn2019-53) ,Gansu Province Natural Science Foundation(21JR11RA111) and Education and Teaching Reform Project of Lanzhou University(2022-011).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWang S, Li XC and Guan QL contributed to the study conception and design. Database searches, data extraction and analysis were performed by Wang S, Zhu JR and Li XC. The frist draft of the manuscript was written by Wang S, while Ran JT and Guan QL revised the manuscript. All authors approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThanks to TCGA and GEO database builders and participants, providing open access to gene expression and clinical phenotype data for authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL,Laversanne M, Soerjomataram I,Jemal A,Bray F. Global cancer statistics 2020:GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA. 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Gene. 2019;699:125\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.gene.2019.02.058\u003c/span\u003e\u003cspan address=\"10.1016/j.gene.2019.02.058\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-2993160/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2993160/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe prognosis of gastric cancer remains poor. Pyroptosis-related genes (PRGs) have been investigated as a potential biomarker in several types of cancer, including gastric cancer. This study aimed to investigate the expression, mutation and diagnostic and prognostic value of PRGs, analyzing data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eRNA-sequencing data (RNA-seq), somatic datasets, and copy number variation (CNV) data for gastric cancer were also collected from the TCGA. Gene expression matrix and clinical information of GSE84437 were obtained from GEO data. Bioinformatics analysis was performed to investigate expression profiles of PRGs and their infiltration of immune cells, as well as prognostic significance in gastric cancer.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 22 out of 33 PRGs were up-regulated, only one PRGs was down-regulated in GC compared to normal tissues, while 10 of them showed no difference between the two groups. A total of 117 out of 433 (27.02%) gastric cancer samples demonstrated genetic mutations, missense mutation was the most common variant classification. More than half of the 33 PRGs had copy number amplification. We performed unsupervised consensus clustering based on the expression of PRGs. Two clusters associated with PRGs named cluster A and cluster B were identified in gastric cancer. Compared with cluster B, cluster A not only had worse overall survival, more patients younger than 65 years, and more deaths, but also had a lower infiltration level of T cell and greater activation B cells and mast cells. According to Gene set variation analysis, cluster A showed greater enrichment of vascular smooth muscle contraction, ECM receptor interaction and KEGG pathways of dilated cardiomyopathy. PRGs cluster B was markedly enriched in cytosolic DNA sensing, non-homologous end joining, and basal transcription KEGG pathways. Multivariate cox analyses revealed that CASP5 was the independent factor affecting the prognosis of patients with gastric cancer. The discriminative ability of the final model for overall survival was assessed using the C statistics, 0.651 for overall survival. A predictive nomogram suggested that 3-year and 5-year overall survival rates could be predicted relatively well compared to an ideal model across the entire cohort.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePRGs was relatively up-regulated in gastric cancer, it was associated with worse overall survival. The overall survival risk for an individual patient can be estimated using PRGs-based nomograms, which can lead to individualized therapeutic choices.\u003c/p\u003e","manuscriptTitle":"Pyroptosis-related genes expression and nomogram predict overall survival of gastric cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-06-09 15:58:28","doi":"10.21203/rs.3.rs-2993160/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"120e08a7-01d3-4f10-9f32-2b914d1106e6","owner":[],"postedDate":"June 9th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-06-16T10:14:17+00:00","versionOfRecord":[],"versionCreatedAt":"2023-06-09 15:58:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2993160","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2993160","identity":"rs-2993160","version":["v1"]},"buildId":"ehx78VzkSd0WSzXnipQa-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}