Identification of pyroptosis-related subtypes and comprehensive analysis of characteristics of the tumor microenvironment infiltration in clear cell renal cell carcinoma

Pyroptosis is a kind of programmed cell death triggered by the inflammasome. Growing evidence has revealed the crucial utility of pyroptosis in tumors. However, the potential mechanism of pyroptosis in clear cell renal cell carcinoma (ccRCC) is still unclear. In this research, we systematically analyze the genetic and transcriptional alterations of pyroptosis-related genes (PRGs) in ccRCC, identify pyroptosis-related subtypes, analyze the clinical and microenvironmental differences among different subtypes, develop a corresponding prognostic model to predict the prognosis of patients, and interpret the effect of pyroptosis on ccRCC microenvironment. This study provides a new perspective for a comprehensive understanding of the role of pyroptosis in ccRCC and its impact on the immune microenvironment, and a reliable scoring system was established to predict patients’ prognosis.


Identification of differentially expressed PRGs
We retrieved 52 pyroptosis-related genes (PRGs) from the MSigDB database (REACTOME_PYROPTOSIS) (http:// www.broad.mit.edu/ gsea/ msigdb/) and the previous publication 26 .The differentially expressed PRGs between normal kidney tissues and tumor tissues was identified by the R package "limma" with the set threshold was |log2FC|> 0 and false discovery rate (FDR) < 0.05.Then, the protein-protein interaction (PPI) networks of these PRGs was constructed through the Search Tool for the Retrieval of Interacting Genes (STRING) to further explore the interactions of these PRGs with the minimum required interaction score was set at 0.9 (the highest confidence) and removed the isolated genes.At the same time, the scoring data of nodes were downloaded and the hub genes were identified by weighting algorithm in the Cytoscape.Next, univariate Cox regression analysis was run to judge the prognostic value of PRGs and P < 0.05 is considered to be a meaningful result.The R package "igraph", "reshape2" and "RColorBrewer" were used to construct a prognostic network.

Identification of DEGs and functional analysis in different pyroptosis-related subtypes
The R package "limma" was used to identify of differentially expressed genes(DEGs) among different subtypes with the threshold |log2FC|> 0 and the adjusted P-value < 0.001.To further explore the functional pathways and related potential functions of these DEGs, we executed the functional enrichment analysis by the R package "clusterprofiler", including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG).

Construction and validation of a pyroptosis-related gene prognostic PRGP_score
We firstly removed lncRNAs from the previously obtained DEGs and performed the univariate Cox regression analysis on the remaining DEGs to identify prognosis-related genes.Then, the "limma" package was executed to identify the differential expression of these prognostic-relevant genes between normal kidney tissues and tumor tissues, with the threshold set as the |log2FC|> 1 and the adjusted P-value < 0. 001.The LASSO Cox regression analysis was conducted on the resulting genes to further reduce the number of genes.Next, multivariate Cox regression analysis was executed and 4 genes (ENGASE, LRFN1, CDKL2, IFI44) were finally selected to construct the PRGP_score in the training cohort.The performance of the PRGP_score was validated in the testing cohort, the TCGA entire cohort, and the E-MTAB-1980 cohort.
The PRGP_score was calculated by the following formula: where Xi and Yi represented the risk coefficient and expression levels of each gene, respectively.On the basis of the median value of the PRGP_score, patients were divided into low-and high-risk group.The Kaplan-Meier curves was used to compare the OS difference in the two groups.The efficiency of the scoring model was evaluated according to the corresponding ROC curve.Then, we performed the PCA to assess whether the model could distinguish patients into different groups via the R package "ggplot2".The same analysis were performed in both internal and external datasets to judge the effectiveness of the model.

Clinical correlation, independent prognostic analysis and stratification analysis of PRGP_score
The chi-square test was conducted to estimate the correlation between PRGP_score and these clinical features.Univariate and multivariate Cox regression analyses were executed on clinical characteristics and PRGP_score To confirm whether PRGP_score could be independent of other clinical characteristics.Furthermore, we also executed a stratification analysis to judge whether the scoring model maintains its good predictive power in different subgroups.

Transcriptome sequencing
We collected 18 tumor samples and 6 paracancerous samples from 6 ccRCC patients who treated in Guangdong Provincial people's Hospital.According to the manufacturer's instructions, each case of ccRCC tissue and matched paracancerous samples were sequenced at the end of the pair on the NovaSeq 6000 high-throughput sequencing platform (Illumina,USA).After removing the sequencing reads containing aptamer sequences and low-quality reads and low-quality bases, HISAT2 (v2.1.1)is used to align the high-quality paired reads with the human genome GRCh38 and generate the BAM file.Using samtools (v1.15.1) tool to sort the BAM files, and

TME and immune checkpoints
The ESTIMATE algorithm was used to estimate the immune score and stromal score of samples so that we can investigate the differences in TME among the low-and high-risk group.Then, the correlation coefficient was calculated by the Spearman correlation analysis.In addition, the infiltration fractions of 22 human immune cells in two groups were estimated via the CIBERSORT algorithm and the difference in infiltration were showed by radar map.Furthermore, we obtained the Tumor Immune Dysfunction and Exclusion (TIDE) score of ccRCC patients from the TIDE website (http:// tide.dfci.harva rd.edu/).The differences of TIDE scores in two groups were compared to further forecast the potential efficacy of immunotherapy in different groups.Finally, the expression levels of common immune checkpoint genes were also investigated among two groups.

Mutation and drug susceptibility analysis
The mutation distribution between low-and high-risk group were visualized via the waterfall plot generated by the "maftools" packet in R. We extracted the tumor mutational burden (TMB) of each ccRCC patient.They were divided into low-and high-TMB group according to the median value of TMB, and the survival differences of two groups was showed by Kaplan-Meier curves.Then, the correlation of PRGP_score with TMB was calculated through the Spearman correlation analysis, and the TMB differences in two groups were compared.Finally, we calculated the semi-inhibitory concentration (IC50) values of some anticancer drugs using the "pRRophetic" package to compare the sensitivity of two groups to common drugs.

Development of a nomogram
We developed a nomogram via the "rms" package based on PRGP_scores and some clinical information with independent prognostic value to predict the probability of 1-, 3-and 5-year OS.The predictive accuracy of the nomogram was assessed by the ROC curves.Calibration plots of the nomogram were used to depict the predictive value between the predicted survival events and the virtually observed outcomes, and the 45°slash represents the best prediction performance.

Statistical analyses
All the above statistical analyses and R packages were executed in the R 4.0.4versions (http:// www.R-proje ct.org).All the results of statistical analyses are two-way, and P-value < 0.05 is considered to be meaningful result.
As shown in Fig. 1C, the PPI network shows the possible interactions of these PRGs, CASP1, PYCARD, NLRP3, NLRC4, AIM2, GSDMD, NLRP1, and CASP5 were considered to be the hub genes.As shown in Fig. 1D, pyroptosis gene prognosis network distinctly reveals the correlation of these PRGs and their relationship with prognosis.Lines between circles indicate correlations between pyroptotic genes, blue indicates negative correlations, pink indicates positive correlations.The right half of the circle represents the risk effect of the gene on the patient, with green representing protective factors and red representing risk factors.The size of the circle represents the p value.At the genetic level, the Top 10 somatic mutations of PRGs in ccRCC are shown in Fig. 1E.Of the 336 ccRCC samples, only 24 samples(7.14%)had mutations in the PRGs, and the overall mutation rate was low.We found that TP53 had the highest mutation frequency, followed by NOD2.Missense mutation is the most common mutation type, followed by nonsense mutation.Next, we explored the CNV of all PRGs, and found CNV in 37 PRGs.IRF1, TP63, AIM2, GSDMC, and GSDMD had widespread CNV increases, while CASP9, CHMP2B, IRF2, CASP3, and HMGB1 had CNV loss (Fig. 1F).The changes of 37 PRGs with characteristics of CNV on their respective chromosomes are shown in Fig. 1G.Compared with normal tissues, the expression levels of PRGs with increased CNV such as IRF1, AIM2 and GSDMD, were significantly increased in ccRCC samples.While genes with CNV loss such as CASP9 and CHMP2B, their expression levels were significantly decreased in ccRCC samples, indicating that CNV may positively related to the expression levels of PRGs.We speculate that there may be a regulatory relationship between CNV and the expression of PRGs.
However, we also noticed that TP63 with increased CNV, had lower mRNA levels in ccRCC samples.While for some genes with CNV loss, their mRNA levels were not significantly different in normal kidney tissue and ccRCC samples.Therefore, CNV may not be the only factor in regulating gene expression.The results of our analysis show that the genetic and transcriptional alterations of PRGs between normal kidney tissues and ccRCC samples were momentous difference, and PRGs may have a potential function in the occurrence and progression of ccRCC.

Identification of pyroptosis-related subtypes in ccRCC
In order to explore the relationship between these 41 differentially expressed PRGs and ccRCC subtypes, consensus clustering analysis was executed on 530 ccRCC patients of the TCGA cohort and three pyroptosis-related subtypes were identified named C1, C2 and C3 (Fig. 2A-C).PCA was then performed to evaluate the stability of clustering.As shown in Fig. 2D, distinct differences in gene expression profiles among the three subtypes were observed.We further analyzed the prognosis and clinical characteristics in different subtypes.Obvious differences in OS and PFS was found among the three subtypes via Kaplan-Meier curves.Among them, the subtype C3 had the best prognosis, subtype C1 followed, and subtype C2 had the worst prognosis (Fig. 2E,F).Besides, there are obvious differences in the expression of PRGs and clinical characteristics among different subtypes.Strong differences in survival status, grade, AJCC stage, and TNM stage were found among three subtypes, while no statistical differences in age and gender (Fig. 2G).Specifically, the subtype C2 was associated with higher grade, AJCC stage, and TNM stage, and the overall expression level of PRGs of was higher with the highest proportion of deaths compared to the other two subtypes.

Differences in TME among different subtypes
The Gene set variation analysis (GSVA) showed that subtype C2 was significantly enriched in immunedeficiency, while subtype C3 was enriched in metabolism, such as insulin signal pathway, adipocytokine signal pathway, TCA cycle, amino acid degradation, and so on (Fig. 3A).As shown in Fig. 3B, most immune cells show differences among three subtypes.Among them, plasma cells, T cells CD8, T cells CD4 memory activated, T cells follicular helper, T cells regulatory (Tregs), T cells gamma delta, and NK cells activated have the highest proportion in the subtype C2, while B cells naive, Monocytes, Macrophages M0, Macrophages M2 and Mast cells resting have the highest proportion in the subtype C3.Then, the expression levels of 2 important immune checkpoint genes were also compared among three subtypes.As shown in Fig. 3C,D, PD-1 and PD-L1 were highly expressed in subtype The TME score of subtype C2 was the highest, suggesting the relative high content of stromal cells and immune cells in subtype C2 compared to other two subtypes (Fig. 3E).Finally, the TIDE score of different subtypes was used to judge the potential efficacy of immunotherapy for ccRCC patients.The higher TIDE score, the higher potential for immune escape, suggesting that patients are less likely to benefit from ICIs treatment.The results revealed that the subtype C2 had the highest TIDE score, while the subtype C1 had the lowest TIDE score (Fig. 3F), which may explain their prognosis.

Identification of gene subtypes based on DEGs
In order to explore the differences in potential biological functions among different pyroptosis-related subtypes, we used the "limma" package to identify DEGs among them, with the threshold set as |log2FC|> 1 and adjusted p-value < 0.001.2041 DEGs were identified and functional enrichment analysis was executed on them.GO enrichment analysis revealed that immune-related biological processes were strongly enriched, such as T cell activation, proliferation and regulation, lymphocyte activation, and proliferation regulation (Fig. 4A).KEGG enrichment analysis showed that immune and cancer-related pathways were obviously enriched, such as T cell receptor signaling pathway, NOD-like receptor signaling pathway, PD-L1 expression and PD-1 checkpoint pathway in cancer, and NF-kappa B signaling pathway (Fig. 4B).It shows that PRGs may play an vital role in the immune regulation of TME in ccRCC.
In order to further explore the potential value of these DEGs, we removed the lncRNAs and obtained 1310 genes with protein-coding functions, and performed an univariate Cox regression analysis to screen out 790 genes related to prognosis.Then, patients were classified into gene subtype A and B using the consensus cluster analysis (Fig. 4C).PCA showed that these two gene subtypes were very ideal (Fig. 4D).Kaplan-Meier curves revealed the evident difference in OS and PFS between two gene subtypes, and patients in the gene subtype A group had a good prognosis (Fig. 4E,F).Furthermore, the gene subtype B group was related to higher grade, AJCC stage, and TNM stage (Fig. 4G).And momentous difference in the expression of PRGs was also found in two subtypes (Fig. 4H), indicating PRGs may participate in the occurrence and progression of ccRCC.

Construction of the PRGP_score
Based on the DEGs between different pyroptosis-related subtypes obtained earlier, we constructed the PRGP_ score.Sankey diagram shows the distribution of patients in three pyroptosis-related subtypes, two gene subtypes, and two PRGP_score groups (Fig. 5A).First, we identified 790 genes associated with prognosis in the previously obtained DEGs.The "limma" package was run to identify the differential expression of these 790 genes in normal and tumor tissues.The threshold was set as |log2FC|> 1 and adjusted p-value < 0.001, and 221 genes were identified.Then, LASSO Cox regression analysis was executed to further reduce the number of genes.Finally,4 genes were selected to construct PRGP_score in the training cohort through multivariate Cox regression analysis (Fig. S1).Among them, ENGASE, LRFN1, and IFI44 are risk genes, while CDKL2 is a protective gene (Fig. S2).
Evidential difference in PRGP_score were found among three pyroptosis-related subtypes.The subtype C2 had the highest PRGP_score, and the subtype C3 had the lowest PRGP_score, which indicates that a high PRGP_score may be associated with immunodeficiency and microenvironmental suppression, while a low PRGP_score is related to substance metabolism (Fig. 5B).However, statistical difference in PRGP_score was not observed among two gene subtypes (Fig. 5C).We divided patients into a high-and low-risk group in the training cohort according to the median value of PRGP_score.The risk distribution curve and survival status demonstrated that the survival time of patients decreased and the death toll increased with the increase of PRGP_scores (Fig. 5D,E).PCA shows a obvious distinction between two groups (Fig. 5F).Kaplan-Meier curve showed distinct difference in OS between two groups, and low-risk group had a good prognosis (Fig. 5G).The AUC values of 1-, 3-, and 5-year survival rates of PRGP_score were 0.791, 0.759 and 0.769, respectively, which shows a satisfactory prediction efficiency (Fig. 5H).In addition, univariate and multivariate Cox regression analysis demonstrated that the PRGP_score was an independent prognostic factor for ccRCC patients (Fig. 5I,J).

Internal and external validation of the PRGP_score
In order to validate the prognostic performance of PRGP_score in different patients, we verified it in internal and external cohorts.In testing cohort, Kaplan-Meier analysis showed a worse prognosis in high-risk group (Fig. 6A).The AUC values of the PRGP_score to predict OS of 1-, 3-, and 5-year were 0.725, 0.691 and 0.763, respectively (Fig. 6B).In TCGA entire cohort, a worse prognosis was also observed in high-risk group (Fig. 6C).The PRGP_score still shows good prediction performance.The 1-, 3-, and 5-year OS of PRGP_score were represented by AUC values of 0.748, 0.718 and 0.759, respectively (Fig. 6D).Furthermore, we conducted external validation in the E-MTAB-1980 cohort to further evaluate the predictive reliability and accuracy of PRGP_score.Similar results were obtained in E-MTAB-1980 external cohort, high-risk group had a worse survival compared to low-risk group (Fig. 6E).The AUC values of the PRGP_score to predict OS of 1-, 3-, and 5-year were 0.704, 0.756 and 0.764, respectively (Fig. 6F).It shows that PRGP_score has good stability and prediction efficiency in different cohorts.The risk distribution curves, survival status, PCA analysis results, and results of univariate and multivariate Cox prognostic analysis of the three cohorts are presented in Fig. S3.

Verification of the expression levels of 4 genes constructing PRGP_score
The transcription levels of 4 genes were verified by transcriptome sequencing of the collected 18 tumor samples and 6 paracancerous samples (Fig. S4).The results of sequencing were similar to TCGA database, the mRNA levels of ENGASE, LRFN1, and IFI44 were obvious higher in ccRCC tissues than that in corresponding paracancerous tissues.The expression of CDKL2 was consistent with our expectations, its mRNA levels was lower in ccRCC than that in paracancerous tissues.

Mutation and drug sensitivity analysis
Compared with low TMB group, the high TMB group had worse OS (Fig. 8A), and the PRGP_score was positively related to TMB (Fig. 8B).Interestingly, statistical difference in TMB between high-and low-risk group was no observed (Fig. 8C).Then, we further analyzed the distribution variations of somatic mutations between two groups in TCGA-KIRC cohort.Missense mutation is the most usual type of mutation, followed by frame shift deletions and nonsense mutation.The largest differences in mutations between two groups were PBRM1 and BAP1 mutations.Specifically, BAP1 mutations were more common in high-risk group (16% vs.4%), while PBRM1 mutations were more common in low-risk group (42% vs.30%) (Fig. 8D,E).Further exploration found that both PBRM1 and BAP1 mutations affect the response and prognosis of ccRCC patients to immunotherapy 27 .Furthermore, we also assessed the susceptibility of two groups to some drugs via the R package "pRRophetic".The IC50 of these drugs was strongly different between two groups (Fig. 8F), such as Veliparib, rucaparib, Acadesine and Ponatini.

Development of a nomogram
In the TCGA entire cohort, univariate and multivariate Cox regression analysis demonstrated that age, grade, and AJCC were independent prognostic factors for ccRCC.Considering the importance of these clinical features, we built a nomogram combining the PRGP_score and these clinical parameters to predict patients' OS (Fig. 9A).
The AUC values of the nomogram to predict OS of 1-, 3-, and 5-year were 0.871, 0.816 and 0.784, respectively (Fig. 9B), which were better than those of any single clinicopathological parameter (Fig. 9C-E), showing a better survival prediction ability.The calibration plots also revealed the stable performance of the nomogram (Fig. 9F-H), which is helpful for clinical application.

Discussion
Pyroptosis is a novel programmed and inflammatory death discovered following apoptosis and necrosis 16 .Studies have found that pyroptosis is strongly associated with various diseases [28][29][30][31] , especially malignant tumors 32 .However, the conclusions about the correlation between pyroptosis and tumors are not completely consistent.Awad et al. found that NLRP1 can regulate the caspase-1-dependant secretion of pro-inflammatory interleukin (IL)1β and IL18 cytokines, thereby promoting the development of skin cancer 33 .This suggests that inflammatory factors released by pyroptosis can form a microenvironment suitable for tumor cell growth and cancer progression.Conversely, Gao et al. found that down-regulation of the pyroptotic gene GSDMD inhibited tumor proliferation through the intrinsic mitochondrial apoptosis pathway and inhibition of EGFR/Akt signaling pathway 34 .It suggests that pyroptosis may play a dual role in the pathogenesis of tumors, and also shows the heterogeneity of tumors and the complexity of the immune microenvironment.
In the study, we aim to explore the expression pattern and genetic variation of PRGs in ccRCC as well as their effect on TME and prognostic value.Compared with normal renal tissue, 32 PRGs were up-regulated and 9 PRGs were down-regulated in ccRCC tissue.Prognostic analysis showed that 21 PRGs were related to the prognosis of ccRCC, indicating that PRGs may play a vital role in the occurrence and progression of ccRCC.In addition, PRGs in ccRCC have low somatic mutations and CNV, showing a stable genetic pattern.We run consensus clustering analysis on patients in TCGA-KIRC cohort based on 41 differentially expressed PRGs and obtained three pyroptosis-related subtypes, namely C1, C2, and C3.Obvious differences in PRG expression, OS, PFS, clinical features, and tumor immune microenvironment infiltration were observed among three subtypes.The PRGs of the subtype C2 are highly expressed with the worst prognosis and are strongly associated with worse grade, AJCC stage, and TNM stage.Functional enrichment analysis and GSEA analysis were performed to investigate  www.nature.com/scientificreports/degradation, TCA cycle, and adipocytokine signaling pathway.Combining with previous studies 44 , we speculated that the metabolism and function of immune cells in subtype C3 microenvironment are more active.While immune cells in subtype C2 microenvironment may have metabolic dysfunction and glucose utilization disorder, thus enriching immune deficiency in GSVA analysis.Since there are few studies on pyroptosis in ccRCC, we speculated that pyroptosis may affect the occurrence and progression of tumors by affecting the immune-related metabolism and immune cell metabolism of TME.However, the specific mechanisms and their regulation may need to be explored in further experimental research.
Patients with ccRCC are insensitive to traditional chemotherapy and radiotherapy.Despite recent promising advances in immunotherapy, the prognosis of patients is heterogeneous and the response to immunotherapy is unsatisfactory, revealing the crucial impact of TME in tumorigenesis and progression of ccRCC.The TME consists of cellular components, extracellular matrix (ECM), and interstitial fluid.The cellular components of TME include tumor cells, stromal cells (such as fibroblasts), endothelial cells of blood vessels and lymphatic vessels, neurons, and infiltrating immune cells 45 .Among them, immune cells are the main cellular components of TME.They can drive or prevent tumor progression by participating in various immune responses and activities 46 .Increasing evidence shows the central role of TME in tumorigenesis, immune escape, progression, and metastasis 47 .In this study, compared with the other two subtypes, we found that subtype C2 had a higher level of immune cell infiltration, such as T cells CD8, T cells gamma delta, and NK cells activated.And these cells are considered to be effector cells that can kill tumor cells [48][49][50] .The ESTIMATE algorithm showed that subtype C2 had the highest immune score and matrix score, suggesting its higher content of immune cells and stromal cells.However, the subtype C2 has the worst prognosis, indicating the complexity of the TME in ccRCC.Recent studies have found that in more advanced and metastatic diseases, CD 8 tumor-infiltrating lymphocytes (TIL) in ccRCC transform to terminal depletion phenotype, express multiple immune checkpoint molecules, and the diversity of T cell receptor (TCR) is limited 51 .Further researches have demonstrated that poor prognosis was strongly related to depleted polyclonal CD8 T cells expressing immune checkpoints such as PD-1, TIM-3, and LAG-3 and displaying decreased cytotoxic functionality 52,53 .Prinz et al. found that kidney tumor cells may induce NK cell dysfunction via various mechanisms including the diacylglycerol kinase, mitogen-activated protein kinase (MAPK/WEK), and TGF-β/SMAD signaling pathways 54 .These reasons may make it difficult for T cells CD8, T cells gamma delta, and NK cells to exert their anti-tumor effect in TME.In addition, Daniel Chen et al. believe that although some tumor tissues with more immune cells, these immune cells cannot penetrate into the inner core of tumor cells and are confined to the peripheral matrix of tumor cells, which is an immunophenotype known as immune-exclude tumor 55 and is very similar to subtype C2.We speculate that the activation of the matrix in the microenvironment will lead to immunosuppression and prevent the related effector cells from killing the tumor.The effects of these comprehensive factors eventually result in a poor prognosis of subtype C2.We were surprised to find that high-risk group had a similar TME landscape to those with subtype C2 after a similar analysis in two groups based on the risk prognostic score model.Compared to low-risk group, high-risk group had a higher infiltration levels of T cells CD8 and NK cells activated, and the highest immune and stromal scores, but like the subtype C2 with the worst prognosis.Furthermore, we noted that both the subtype C2 and high-risk group of patients had a higher infiltration level of Tregs and T cells follicular helper.We already know that Tregs in TME can inhibit anti-tumor immunity via multiple mechanisms, which is related to poor prognosis.Based on this evidence, we speculate that pyroptosis can affect immune cells infiltration in TME and usually leads to immunosuppression.This suggests that targeting pyroptosis may reverse the immunosuppression of the microenvironment and enhance the efficacy of immunotherapy.
ICIs targeting PD-1/PD-L1 has become the mainstay treatments of many cancer.Now many additional immune checkpoints have also become the main targets of research, including CTLA-4, PD-L2, LAG-3, TIM-3, and so on 56 .High expression levels of immune checkpoints such as PD-1 were found in both the subtype C2 and the high-risk group.Generally, the higher the level of gene expression in immune checkpoints, the higher its response to ICIs.However, the opposite result was found in our study.Patients with subtype C2 and high risk groups had a higher TIDE scores, suggesting that they had less benefit from ICIs.Based on previous study 57 , we speculate that pyroptosis may recruit functionally restricted immune cells, thus weakening the efficacy of ICIs and promoting the progress of ccRCC.This again suggests that targeting pyroptosis may be a new direction for improving the efficacy of immunotherapy.In addition, we also assessed the susceptibility of high-and low-risk group to some anticancer drugs.According to IC50, high-risk group patients may be more sensitive to Ponatini, Veliparib, rucaparib and Acadesine, while the low-risk group may be more sensitive to AS601245 and AKT inhibitor.As the third generation kinase inhibitor, Ponatinib is an effective drug for hematological malignancies 58 .Garner et al. found that Ponatinib can inhibit polyclonal drug-resistant KIT oncoproteins and reveals therapeutic potential in heavily pretreated gastrointestinal stromal tumor (GIST) patients 59 .Pletcher et al. found that Veliparib and rucaparib can induce apoptosis of RCC cell and reduce tumor cell growth and proliferation 60 .Woodard et al. found that Acadesine could inhibit the growth and promote apoptosis of RCC cells via inducing AMPK activity and inhibiting mTOR and its effectors 61 .Recently, Liang et al. found that Acadesine combined with rapamycin can reduce the proliferation of ccRCC cells, increase apoptosis, and significantly reduce the expression of p-Akt, HIF-2 α and vascular endothelial growth factor in mouse kidney tumor tissue 62 .There are many similar studies 63,64 .Therefore, the drugs we found may have potential therapeutic effects on ccRCC.
In the present study, we also constructed a risk prognosis score mode.The PRGP_score based on 4 genes can accurately predict the prognosis of ccRCC patients and effectively divide patients into high-and low-risk group.Compared to low-risk group, high-risk group had a poor OS and was related to worse T stage, M stage, grade, and AJCC stage.We verified the stability and effectiveness of the model in testing cohort , TCGA entire cohort and E-MTAB-1980 external cohort.In addition, stratified analyses showed that PRGP_score was applicable in different clinical subgroups.Univariate and multivariate Cox prognostic analysis demonstrated that the PRGP_score www.nature.com/scientificreports/ was an independent prognostic factor for ccRCC.Subsequently, integrating PRGP_score with age, grade, and AJCC stage, we constructed a nomogram to further improve the practical application of PRGP_score in clinic.
We have to admit that there are some deficiencies in our research.all analyses used the data from public databases, requiring prospective and larger trials to provide high-level evidence for clinical application.Then, our research suggests that PRGs have potential roles in the TME, clinicopathological features and prognosis of ccRCC, but the specific mechanisms of their effects and how they are regulated need to be further studied.

Conclusion
We systematically analyzed the genetic variation and expression profiles of PRGs in ccRCC.Based on the differentially expressed PRGs, we identified three pyroptosis-related subtypes, and developed and verified a risk model to predict the prognosis of patients.Remarkable differences in clinical features and TME among different subtypes were observed.Our study demonstrates that PRGs may play a crucial role in the TME, clinicopathological features and prognosis of ccRCC, which provides a new idea for development of effective immunotherapy strategies. https://doi.org/10.1038/s41598-023-43023-ywww.nature.com/scientificreports/

Figure 4 .
Figure 4. Identification of different gene subtypes.(A,B) GO and KEGG enrichment analyses of DEGs among three pyroptosis-related subtypes.(C) Consensus cluster analysis defining two gene subtypes.(D) PCA analysis shows obvious difference in gene expression profiles among two gene subtypes.(E,F) Kaplan-Meier curves of OS (E) and PFS (F) for patients with ccRCC between two gene subtypes.(G) Correlation between clinical characteristics and two gene subtypes.(H) Difference in the expression levels of PRGs among two gene subtypes.*P < 0.05, **P < 0.01, ***P < 0.001.

Figure 5 .
Figure 5. Construction of the PRGP_score in the training cohort.(A) Sankey diagram shows the distribution of patients in three pyroptosis-related subtypes, two gene subtypes, and two PRGP_score groups.(B) Differences in PRGP_score among the three pyroptosis-related subtypes.(C) Difference in PRGP_score among two gene subtypes.(D,E) The risk distribution curve and survival status of patients.(F) PCA shows an evident distinction between two groups.(G) Kaplan-Meier curves of OS for patients with ccRCC between low-and high-risk group.(H) The ROC curves of the PRGP_score to predict the survival rates of 1-, 3-, and 5-year.(I,J) Univariate and multivariate Cox regression analysis of PRGP_score and clinicopathologic features.

Figure 7 .
Figure 7. Evaluation of the TME and immune checkpoints between high-and low-risk group.(A) Radar map shows the difference of immune cell infiltration abundance between two groups.(B) The ESTIMATE assessment results show the differences of TME scores between two groups.(C,D) Correlations between PRGP_score and both immune and stromal scores.(E) The mRNA levels of immune checkpoints in two groups.(F,G) The differences of TIDE score between two groups.

Figure 8 .
Figure 8. Mutation and drug sensitivity analysis.(A) Kaplan-Meier curves between low-and high-TMB groups.(B) Correlations between PRGP_score and TMB.(C) The difference of TMB in two groups.(D,E) The distribution variations of somatic mutations between two groups.(F) The difference of IC50 of some drugs between two groups. https://doi.org/10.1038/s41598-023-43023-y