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Journal of Cancer Research and Clinical Oncology logoLink to Journal of Cancer Research and Clinical Oncology
. 2023 Jul 15;149(14):12677–12690. doi: 10.1007/s00432-023-05018-0

Identification of pyroptosis-related signature and development of a novel prognostic model in diffuse large B-cell lymphoma

Liemei Lv 1, Yu Zhang 2, Ran Kong 1, Cong Wang 2, Xin Wang 1,2,3,4,✉, Xiangxiang Zhou 1,2,3,4,✉
PMCID: PMC11797697  PMID: 37452851

Abstract

Purpose

Emerging evidence suggests that pyroptosis plays an essential role in the development and progression of multiple cancers. However, the role of pyroptosis remains elusive in diffuse large B-cell lymphoma (DLBCL).

Methods

The expression profile data of DLBCL and normal samples of pyroptosis-related genes (PRGs) were analyzed, and the clinical characteristics of DLBCL patients were further investigated. A prognostic model was established using LASSO-Cox regression analysis. The expression of these PRGs was validated by qRT-PCR in DLBCL cell lines. Cell proliferation assay and flow cytometry were utilized to explore the impact of pyroptosis inhibitor (disulfiram, DSF) combined with PD1/PD-L1 inhibitor (BMS1166) on DLBCL cell proliferation.

Results

Most PRGs were dysregulated in DLBCL samples and associated with overall survival (OS). Six PRGs were selected to construct a prognostic risk score model. The qRT-PCR analysis revealed that CASP8, CASP9, NLRP1, NLRP6, and TIRAP are downregulated, while SCAF11 was significantly upregulated in DLBCL cell lines. This prognostic model divided DLBCL patients into low-risk and high-risk groups. Patients in the low-risk group exhibited lower mortality and longer OS than those in the high-risk group. The ROC curve and nomogram demonstrated this model's excellent predictive performance. GO and KEGG enrichment indicated that the differentially expressed genes (DEGs) between subgroups were associated with cellular protein modification processes and JAK-STAT signaling pathway regulation. Moreover, the risk score was correlated with the immune profile. Cell proliferation assay and flow cytometry further validated the synergistic anti-tumor effects of DSF and BMS1166 on DLBCL cells.

Conclusion

In summary, we developed a comprehensive prognostic model based on PRGs characteristics, which accurately predicted the prognosis of DLBCL patients. Pyroptosis-targeting coupled with immunotherapies would be a promising therapeutic strategy for DLBCL.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00432-023-05018-0.

Keywords: Pyroptosis, Prognostic model, Combination therapy, Immune profile, DLBCL

Introduction

Diffuse large B-cell lymphoma (DLBCL), the most common subtype of lymphoma is highly invasive and heterogeneous (Swerdlow et al. 2016, Miao et al. 2019). Significant progress has been made due to the emergence of immunochemotherapy drugs based on rituximab in DLBCL treatment. A 60% or higher recovery rate can be expected with the R-CHOP regimen for most patients with DLBCL (Sehn and Salles 2021). Unfortunately, about a third of patients developed drug resistance or relapsed, making it difficult to achieve an ideal survival time (Carpio et al. 2020). Currently, with the refinement of genotyping, the traditional international prognostic index (IPI) score is no longer adequate for accurate prognostic stratification of DLBCL. Therefore, it is imperative to develop novel predictive models for stratifying DLBCL patients to provide more accurate diagnosis and treatment.

An increasing number of studies have shown that programmed cell death (PCD) plays an imperative role in anti-tumor therapy (Wu et al. 2021). Pyroptosis is a novel form of PCD that has been discovered and demonstrated in recent years, characterized by dependence on caspase-1 and accompanied by the extensive release of proinflammatory mediators. Its morphological features, occurrence, and regulatory mechanisms are different from other forms of cell death such as apoptosis and necrosis (Vande Walle and Lamkanfi 2016). Among the most important executors of pyroptosis are the gasdermin family members, including gasdermin A (GSDMA), Gasdermin E (GSDME), and Pejvakin (PJVK), etc (Kovacs and Miao 2017). Gasdermins are cleaved by pyroptosis caspase in various microorganisms and endogenous stimuli, and the N-terminal domains of gasdermins are inserted into membranes to trigger pyroptosis (Aglietti and Dueber 2017). There is evidence that pyroptosis contributes to the development of infectious diseases, neurological disorders, and atherosclerosis (Jorgensen and Miao 2015; Tang et al. 2020). Moreover, increasing studies have specifically linked pyroptosis to malignant tumors, including lung cancer (Zhang et al. 2019), ovarian cancer (Qiao et al. 2019), and colon cancer (Yu et al. 2019). Intriguingly, pyroptosis may exert a dual role in tumorigenesis. On the one hand, pyroptosis inhibits tumor growth as a form of cell death. Researchers uncovered a potential regulatory relationship linking pyroptosis to the tumor immune microenvironment. GSDME-mediated pyroptosis inhibits tumor growth by increasing the ability of tumor-infiltrating natural killer (NK) cells and CD8+ T cells to kill lymphocytes (Zhang et al. 2020). A promising treatment option for cancer may involve the induction of pyroptosis in the development and progression of multiple cancers (Wei et al. 2013, Wang et al. 2019). In one sense, various signaling pathways and inflammatory mediators released during pyroptosis create a favorable environment for growth that contribute to cancer development (Karki and Kanneganti 2019). For example, the release of IL-1 β and IL-18 may facilitate the occurrence and progression of tumors (Hu et al. 2010, Salcedo et al. 2010).

Overall, different forms of pyroptosis are closely associated with tumors, and targeting pyroptosis may serve as an effective option for cancer treatment. Despite this, little study has been conducted to assess how pyroptosis affects the development and progression of DLBCL. In this study, we aimed to evaluate the expression profile and clinical value of PRGs in DLBCL, construct and validate a prognostic model, and analyze the immune infiltration characteristics between subgroups.

Materials and methods

Cell culture and reagents

Health volunteers were recruited to obtain peripheral blood mononuclear cells (PBMCs). Each volunteer provided written informed consent following the Declaration of Helsinki. To protect participant privacy, all data is anonymous. CD19 + magnetic microbeads kit (Miltenyi Biotec, Bergisch Gladbach, Germany) was used to purify CD19 + B cells from isolated PBMCs. The OCI-LY1, OCI-LY3, OCI-LY8, and U2932 DLBCL cell lines were cultured in IMDM (Gibco, MD, USA) and mixed with 10% FBS (Gibco). Short tandem repeat (STR) and mycoplasma infection were regularly detected in all cells. BMS1166 (PD1/PDL1 inhibitor, S8859) was obtained from Selleckchem (TX, USA), and disulfiram (DSF, 86,720) was purchased from Sigma-Aldrich (Darmstadt, Germany).

Total RNA extraction and quantitative reverse-transcription polymerase chain reaction (qRT-PCR)

The qRT-PCR assay was carried out to test the mRNA expression profiles of risk PRGs. TRIzol agent (Accurate Biotechnology, Hunan, China) was used to purify the total RNA of risk PRGs. Reverse transcription reactions were undertaken by reverse transcription agents (Accurate Biotechnology). The qRT-PCR was conducted to detect the expression profiles on a Light Cycler 480II system (Roche, Basel, Switzerland) using SYBR (Accurate Biotechnology). Normalization was performed based on the expression of GAPDH. As shown in Table S1, all primers used in this study were outlined.

Cell proliferation assay

The Cell Counting Kit-8 (CCK-8) assay (Dojindo, CK04, MD, USA) was applied to detect cell proliferation. Cells were inoculated at 1 × 104 cells/well in 96-well plates, stained with 10 μl CCK-8/well at a given time, and incubated for 4 h. The Multiskan GO microplate reader (Thermo Scientific, Rockford, IL, USA) was used to detect optical density at 450 nm.

Flow cytometry analysis

DLBCL cell apoptosis was assessed by flow cytometry on Navios Flow Cytometer (Beckman Coulter, CA, USA), as previously described (Zhou et al. 2020).

Data acquisition

This research flowchart can be seen in Fig. S1. The mRNA expression matrix information for DLBCL patients and normal samples was obtained from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/projects/TCGA-PAAD/) and Genotype-Tissue Expression Project (GTEx, https://www.gtexportal.org/). Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) data was utilized to extract raw CEL data for GSE10846 and GSE53786. The Human Protein Atlas (HPA, https://www.proteinatlas.org/) database was used to validate the protein levels of six PRGs in normal lymph node tissues and non-Hodgkin lymphoma (NHL) tissues.

Identification of differentially expressed PRGs

33 genes associated with pyroptosis were obtained from the published literature (Ye et al. 2021). More detailed references were available in Table S2. To explore and visualize gene transcription features from the TCGA and GTEx databases, the "ggplot2" package was utilized. A tool for assessing protein–protein interaction (PPI) information is available at the STRING website (http://string-db.org/cgi/). PPI networks were generated via the STRING online tool to analyze PRGs interaction, with interaction scores of over 0.4 deemed significant.

Development and validation of prognostic model for PRGs

To evaluate the predictive value of PRGs, a univariate Cox analysis was performed on training cohort GSE53786 using the "survival" R packet. LASSO-Cox regression analysis was carried out to develop a prognostic model. The expression of selected risk genes was analyzed to determine individual risk scores. The following formula was applied to compute the risk score of every DLBCL patient: Risk Score = ∑7iXi × Yi (X: coefficients, Y: gene expression level). According to the median risk score, Kaplan–Meier analysis was used to compare OS times among subgroups. R packages "survival", "survminer" and "timeROC" were used to draw ROC curves. GSE10846 was utilized as the validation queue and the risk score was calculated in the same way.

Analysis of the association between pyroptosis and DLBCL clinical characteristics

For confirming independent OS predictors, univariate and multivariate Cox regression analyses were conducted. The nomogram generated from multiple independent predictors was applied to forecast patient outcomes. One-, three-, and five-year OS calibration curves were applied to evaluate the reliability of the nomogram.

Immune infiltration signatures of subgroups

Select DEGs from subgroups based on particular criteria (| log2FC |> 1 and p adjust < 0.05). GO and KEGG analysis was conducted using the "clusterProfiler" package. The "gsva" package was used to compute the score of infiltrating immune cells and immune-related pathways activation. To assess immune microenvironment variances between patients, the estimate algorithm was applied to compute estimate, immune, and matrix scores for every patient (Yoshihara et al. 2013). Correlation matrix diagram was used to analyze the association between immune checkpoint expression and risk score.

Statistical analysis

All statistical analyses were carried out with R software (version 3.6.3) and GraphPad Prism (version 8.0). The significance of a p-value was determined by the following criteria: *p < 0.05; **p < 0.01; ***p < 0.001; and ****p < 0.0001.

Results

Identification of aberrant expression of PRGs in DLBCL

To explore the expression profile characteristics of PRGs in DLBCL, the mRNA expression levels of PRGs were evaluated in DLBCL and normal samples from the TCGA and GTEx databases. The results revealed that most PRGs were dysregulated in DLBCL samples (Fig. 1A–C). Subsequently, 8 hub genes, including PJVK, TNF, CASP1, CASP3, CASP5, CASP8, NLRP3, and IL18, were screened (Fig. 1D). In DLBCL, the aberrant expression of PRGs prompts us to explore further the potential role of PRGs.

Fig. 1.

Fig. 1

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Expression distributions and interaction landscape of PRGs in DLBCL. A–C The relative expression of PRGs in DLBCL samples and normal samples from TCGA and GTEx databases. (DLBCL samples, red box; normal samples, blue box. ns, not statistically significant. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001.) D The PPI network showed the interaction of PRGs

Construction of a prognostic model based on PRGs

To investigate the prognostic value of PRGs, we performed univariate Cox regression analysis in the training cohort (GSE53786). We found that high expression of AIM2, CASP3, CASP5, CASP6, IL18, and SCAF11 was linked to an adverse prognosis in DLBCL patients, based on Hazard ratio (HR) > 1 (Fig. 2A). The LASSO regression analysis was used to establish a prognostic model based on 19 prognostic PRGs (Fig. 2B–C). Risk score was derived from: risk score = (− 1.971* CASP8 exp.) + ( − 0.300* CASP9 exp.) + ( − 0.340* NLRP1 exp.) + ( − 0.124* NLRP6 exp.) + (6.139* SCAF11 exp.) + ( − 0.900* TIRAP exp.). In addition, the risk score was calculated in the same way using GSE10846 as the validation queue. In both cohorts, low-risk and high-risk groups were categorized by the median risk score. The low-risk group exhibited lower mortality rates and longer survival times than the high-risk group in both queues (Fig. 2D–E). Heatmap presented that SCAF11 was markedly elevated in the high-risk group, while CASP8, CASP9, NLRP1, NLRP6, and TIRAP were highly expressed in the low-risk group in both queues (Fig. 2D–E). Consistently, SCAF11 displayed a positive correlation with risk score, whereas other risk genes displayed a negatively correlated (Fig. 2F–G). Our results suggest that our prognostic model accurately stratifies DLBCL patients.

Fig. 2.

Fig. 2

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Construction of a prognostic model based on PRGs in DLBCL. A Univariate Cox regression analysis of OS for each PRG, and 19 PRGs with p-value < 0.05. B–C LASSO regression of the 6 PRGs and cross-validation for tuning parameter selection in LASSO regression. D–E Distribution of patients in the training and validation cohort based on risk score, survival status of each patient (low-risk group: left side of the dotted line; high-risk group: right side of dotted line), and heatmap of expression of CASP8, CASP9, NLRP1, NLRP6, SCAF11, and TIRAP. TIRAP. (F-G) Correlation analysis of the six PRGs with risk scores in the training and validation cohort

Evaluation of mRNA expression levels of risk PRGs in DLBCL cell lines

Considering the above model has a patient stratification effect, we further investigated whether these PRGs have prognostic significance in DLBCL patients. It was found that SCAF11 expressed at high levels was linked to adverse outcomes (Fig. 3A). In contrast, high expressions of CASP8, CASP9, NLRP1, NLRP6, and TIRAP were protective factors (Fig. 3B–F). Additionally, qRT-PCR results showed a significant increase in SCAF11 expression in DLBCL cell lines compared to CD19 + B cells (Fig. 3G). However, the mRNA expression levels of CASP8 and CASP9, NLRP1, NLRP6, and TIRAP were notably decreased in DLBCL cell lines (Fig. 3H–L). Correspondingly, the protein expression patterns of CASP8, CASP9, NLRP1, and NLRP6 from the HPA database exhibited that these regulators were low expressed in NHL tissues compared to normal lymph node tissues, while SCAF11 and TIRAP were not significantly different (Fig. S2). This evidence presents a strong correlation between these risk PRGs and patient outcomes.

Fig. 3.

Fig. 3

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Evaluation of mRNA expression levels of six risk PRGs in DLBCL. A Kaplan–Meier survival analysis of SCAF11 in the training cohort. B–F Kaplan–Meier survival analysis of TIRAP, CASP8, CASP9, NLRP1, and NLRP6 in the training cohort. G The relative mRNA expression of SCAF11 in CD19 + B cells and DLBCL cell lines. H–L The relative mRNA expression of TIRAP, CASP8, CASP9, NLRP1, and NLRP6 in CD19 + B cells and DLBCL cell lines

Verification of predictive value of PRGs-based prognostic model

The above results prompted us to validate the practical application of the prognostic model in the training and verification queue. As shown in the Kaplan–Meier curve, the high-risk group was featured with an adverse survival prognosis both in the training and verification queue (Fig. 4A–B). ROC analysis verified the reasonable predictive power of this model in both the training (AUC = 0.787 for 1-year, 0.848 for 3-year, and 0.849 for 5-year survival) and validation queues (AUC = 0.695 for 1-year, 0.725 for 3-year, and 0.779 for 5-year survival) (Fig. 4C-D). Moreover, we analyzed the subgroups regarding pathological staging, chemotherapy regimen, and stages and visualized them using Sankey diagrams in the training cohort (Fig. 4E). Consistently, the majority of DLBCL patients derived from GCB were in the low-risk group. In conclusion, this prognostic model exhibits outstanding predictive accuracy and superiority.

Fig. 4.

Fig. 4

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Validation of the prognostic model for DLBCL. A–B Kaplan–Meier method in the training and validating cohort was used to compare OS between low-risk and high-risk groups. C–D ROC curve to evaluate the prediction efficiency of the risk score in the training and validation cohort. E Alluvial plots of groups of clinical characteristics in the training queue

Building and validating a predictive nomogram

Based on the above findings, we advanced in exploring whether the risk score can be applied as a prognostic indicator for DLBCL patients. Both univariate and multivariate Cox regression analyses demonstrated that the risk score could be served as a prognostic indicator (Fig. 5A–B). To explore a reliable and quantifiable method, we constructed an innovative prognostic nomogram. This nomogram can forecast survival rates of DLBCL patients with risk scores and clinical characteristics, including age, sex, stage, chemotherapy, and Eastern Cooperative Oncology Group (ECOG) performance score. The nomogram efficiently predicted the probability of 1, 3, and 5-year OS in DLBCL patients (Fig. 5C). The calibration curve confirmed the consistency between predicted and observed OS (Fig. 5D), with the area under the ROC curve (AUC) of 0.794, 0.847, and 0.849 for 1, 3, and 5-year OS correspondingly (Fig. 5E).

Fig. 5.

Fig. 5

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Establishment of prognostic nomogram for DLBCL based on risk score. A–B Univariate and multivariate Cox regression analyses for the risk score model as an independent prognostic factor. C Nomogram to predict the 1-year, 3-year, and 5-year overall survival rates of patients with DLBCL. D The calibration curve of the nomogram. E ROC curves for prediction of survival by the risk score

Comparison of immunological activation among subgroups

The potential biological role and the mechanism of PRGs causing different risks were further explored. To investigate the differences in gene function and pathways between subgroups, DEGs were identified (| log2FC |> 1, P < 0.05). Based on these DEGs, GO and KEGG enrichment illuminated that these DRGs were involved in cellular protein modification processes and regulation of the JAK-STAT signaling pathway (Fig. S3A-B). Given the strongly correlated JAK-STAT signaling pathway, pyroptosis, and tumor immune environment, we analyzed the immune status of subgroups. The immune score was significantly improved in the low-risk group, while tumor purity, estimate score, and matrix score did not differ substantially between the subgroups (Fig. 6A–D). Subsequently, we compared the enrichment scores of 16 immune cell types and the activity of 13 immune-related pathways. According to our results, the low-risk group showed notably elevated levels of immune infiltration. Moreover, the activities of plasma cell-like dendritic cells (pDCs), follicular helper T cells (Tfh), T helper 1 cells (Th1), and tumor-infiltrating lymphocytes (TIL) in the low-risk group were stronger than those in the high-risk group, while NK cells and T helper 2 cells (Th2) were on the contrary. Furthermore, the low-risk group displayed the enhanced activity of immune pathways involved in APC-co-inhibition, T-co-stimulation, and human leukocyte antigen transmission (HLA) (Fig. 6E–F). Consequently, our findings highlight that the PRGs risk score is correlated with the immune profile.

Fig. 6.

Fig. 6

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Comparison of immunological activation among subgroups. A–D Comparison of Immune Score, Stromal Score, Estimate Score, and Tumor Purity between high and low-risk groups. E–F The enrichment scores of 16 kinds of immune cells and 13 immune-related pathways were compared between low-risk (blue box) and high-risk (red box) groups in the training cohort. P-value showed that ns was not significant, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001

Pyroptosis inhibitor and PD1/PD-L1 inhibitor exert synergistic antitumor effects in DLBCL

Considering the significant differences in checkpoint pathways between subgroups as well as the critical role of checkpoints in immunotherapy, we investigated the correlation between risk score and checkpoint expression. Common checkpoints, including PD1, lymphocyte activation gene-3 (LAG-3), and CD40 were significantly correlated with risk scores in DLBCL patients (Fig. S4A). CD40 (Fig. S4B), LAG3 (Fig. S4C), and PD1 (Fig. 7A) were all negatively correlated with risk scores, while CTLA4 was not (Fig. S4D).

Fig. 7.

Fig. 7

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Pyroptosis inhibitor and PD1/PD-L1 inhibitor exert synergistic antitumor effects in DLBCL. A The scatter chart showed the correlation between risk score and PD1expression. B–C Cell proliferation of OCI-LY1 cells treated with DSF (0, 1.25, 2.5, 5, 10, 20 μM) and BMS1166 (0, 3, 6, 9, 12, 18 μM) for 24 or 48 h. D OCI-LY1 cells were simultaneously treated with a 5 (μM): 12 (μM) ratio of DSF and BMS1166. After 24 h of incubation, cell proliferation was determined by CCK-8 assay and applied to calculate CI values. E Representative dot plots generated by flow cytometry analysis of OCI-LY1 cells treated with 5 μM DSF, 12 μM BMS1166, and a 5 (μM):12 (μM) ratio of DSF and BMS1166 simultaneously for 24 h respectively. P-value showed that ns was not significant, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001

Furthermore, PD-1/PD-L1 inhibitors were only beneficial to a small proportion of patients due to a markedly different response to immunotherapy in DLBCL patients (Lesokhin et al. 2016). Growing evidence showed that inhibiting pyroptosis-regulated cell death was a promising therapeutic approach for the treatment of cancer (Du et al. 2021). To address the limitations of PD-1/PD-L1 inhibitors treatment for DLBCL, we examined the role of pyroptosis inhibitor (disulfiram, DSF) and PD1/PDL1 inhibitor (BMS1166) in DLBCL. CCK-8 assay showed that DSF and BMS1166 treatment significantly reduced the cell proliferation of DLBCL cells in vitro in a time- and dose-dependent manner (Fig. 7B–C). Notably, the drug combination significantly reduced OCI-LY1 cell proliferation compared to in vitro monotherapy (Fig. 7D). Furthermore, the CI values for the different concentration combinations were all less than 1.0, indicating an effective synergistic effect (Table S3). In addition, flow cytometry was used to assess apoptosis in OCI-LY1 cells treated with DSF, BMS1166, or both for 24 h. Compared to cells administered with the single agent, DLBCL cells treated with the combination of DSF and BMS1166 showed robust apoptosis (Fig. 7E). Given these findings, pyroptosis-targeting in conjunction with immunotherapies might offer potential therapeutic benefits for DLBCL patients.

Discussion

There is accumulating evidence that pyroptosis exerts double action in the development of tumors (Xia et al. 2019). On the one hand, pyroptosis weakens the immune system's defenses against tumor cells. Excessive inflammation is not advantageous to the host and can aggravate the disease, especially tumor progression (Ahechu et al. 2018, Tang et al. 2020; Tsuchiya 2021). On the other hand, pyroptosis creates an environment that suppresses tumors by releasing inflammatory cytokines (Zhang et al. 2020). Inducing pyroptosis may be a promising treatment (Karki and Kanneganti 2019). For example, chemotherapy drugs turns apoptosis into pyroptosis in tumor cells in vitro by up-regulating GSDME (Wang et al. 2017). BRAF and MEK inhibitors facilitate GSDME-mediated pyroptosis and activate anti-tumor immunity in melanoma (Erkes et al. 2020). However, it remains unclear whether PRGs are related to the development and progression of DLBCL patients. In this study, we identified pyroptosis-related signatures and developed a novel prognostic model that provides insights for DLBCL studies and facilitates clinical treatment. Moreover, our research suggested that pyroptosis-targeting combined with immunotherapy would be a promising therapeutic strategy for DLBCL.

The common dysregulation of PRGs in DLBCL prompted us to investigate their prognostic value. We developed a prognostic model based on SCAF11, TIRAP, CASP8, CASP9, NLRP1, and NLRP6 genes and found substantial correlations between PRGs and clinical features. Moreover, this model enables us to precisely comprehend the prognostic, diagnostic, and therapeutic value of PRGs expression patterns. CASP8 is a molecular switch that controls three distinct forms of cell death, necrotic apoptosis, and pyroptosis (Tummers and Green 2017, Fritsch et al. 2019). Interestingly, CASP8 is also involved in promoting survival by inhibiting necrotizing apoptosis (Mandal et al. 2020). CASP9 is the initial caspase of apoptosis and a critical therapeutic target for various related diseases (Kemper et al. 2012). As a sensor component of NLRP1 inflammatory bodies, NLRP1 mediates inflammatory body activation in response to a variety of pathogen-related signals, resulting in subsequent pyroptosis (Finger et al. 2012). NLRP6 has been proven to play a significant role in the occurrence and development of gastric cancer and colorectal cancer (Normand et al. 2011, Wang et al. 2020b). It is reported that as a member of the TLR/IL-1R family, the abnormal expression of TIRAP can induce tumorigenesis of gastric cancer, colorectal cancer, and leukemia (Kennedy et al. 2014). Moreover, there is evidence for TIRAP is associated with the prognosis of myeloma (Bagratuni et al. 2016). According to our findings, high expression of SCAF11 in DLBCL patients was linked to unfavorable outcomes. Consistently, recent research revealed that increased SCAF11 expression was linked to adverse prognosis in HCC patients (Fu and Song 2021). This evidence highlights that DLBCL patients may benefit from targeting SCAF11.

Additionally, the risk score emerged as an independent variable both in univariate and multivariate Cox analyses. Taking into account our findings above, we subsequently explored the possible biological function and the mechanism of PRGs contributing to differing risks in DLBCL patients. According to GO and KEGG enrichment analyses, DRGs between risk subgroups were associated with JAK-STAT signaling pathway and cellular protein modification process regulation. Interestingly, studies have suggested that pyroptosis is linked to JAK-STAT signaling (Lee et al. 2018, Gao et al. 2020). Given the strong correlation between the JAK-STAT signaling, pyroptosis, and the tumor immune environment, we evaluated the immune profile of different risk groups. These two groups differed significantly in the immune score, whereas no notable distinction was found in tumor purity, estimate score, or matrix score. Despite this, it appears that the poor survival rates for the high-risk group are associated with impaired immunity (Pages et al. 2010). This may be related to the inhibition of tumor immunity by pyroptosis (Wang et al. 2020a). Additionally, neutrophils, plasma cell-like dendritic cells, follicular helper T cells, and tumor-infiltrating lymphocytes were all more active in the low-risk group, suggesting that the immune function was stronger there. This may explain why a high survival rate can be attributed to immune pathway activation through APC-co-inhibition, T-co-stimulation, and HLA.

With the emergence of drugs to block immune checkpoints, immunotherapy has become a hot spot in oncology research (Topalian et al. 2016). Our analysis revealed that PD1, LAG3, and CD40 were all negatively correlated with risk scores in DLBCL, while CTLA4 was not. Combining immunotherapy with PD1 has proven to be effective when treating DLBCL (Pascual et al. 2019, Liu et al. 2021). Furthermore, PD-L1 increases pyroptosis by regulating Gasdermin C expression in cancer cells (Blasco and Gomis 2020, Hou et al. 2020). Selective delivery of gasdermin protein to cancer cells via antibody–drug couples may have a synergistic effect with immune checkpoint-blocking therapy (Wang et al. 2020a, Chu et al. 2021). Malignant B cells in DLBCL usually express LAG3 along with PD1 and TIM-3 (Keane et al. 2020). Further research is necessary on the combination of blocking PD1 and LAG3 immune checkpoints in DLBCL. Additionally, previous studies have pointed out that CD40 may be a therapeutic target for DLBCL (de Vos et al. 2014). Moreover, our results indicated that DSF combined with BMS1166 had a better anti-tumor effect, compared to monotherapy. Taken as a whole, combined with immunotherapy, targeting pyroptosis could be a promising treatment strategy for DLBCL.

However, there were still several limitations in our research, and it was not designed prospectively. Testing our model in vitro and in vivo is necessary to gain an in-depth comprehension of the interaction between pyroptosis and risk score. As well as the need for further validation of our predictive model in a larger cohort of patients with longer follow-ups.

Conclusion

In summary, our study demonstrated that the aberrant expression of PRGs is an influential contributor to the development and progression of DLBCL. Moreover, our prognostic model predicts the prognosis of DLBCL patients successfully by analyzing PRGs characteristics. The combination of pyroptosis-targeting and immunotherapies could be a promising therapeutic strategy for DLBCL.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 1232 KB) (1.2MB, docx)

Acknowledgements

Not applicable.

Abbreviations

DLBCL

Diffuse large B-cell lymphoma

PRGs

Pyroptosis-related genes

OS

Overall survival

DEGs

Differentially expressed genes

IPI

International prognostic index

PCD

Programmed cell death

GSDMA

Gasdermin A

GSDME

Gasdermin E

PJVK

Pejvakin

NK cells

Natural killer cells

PBMCs

Peripheral blood mononuclear cells

STR

Short tandem repeat

qRT-PCR

Quantitative reverse-transcription polymerase chain reaction

CCK-8

Cell Counting Kit-8

TCGA

The cancer genome atlas

GTEx

Genotype-tissue expression project

GEO

Gene expression omnibus

HPA

The human protein atlas

PPI

Protein–protein interaction

NHL

Non-Hodgkin lymphoma

HR

Hazard ratio

ECOG

Eastern cooperative oncology group

AUC

The area under the ROC curve

pDCs

Plasma cell-like dendritic cells

Tfh

Follicular helper T cells

Th1 cells

T helper 1 cells

TIL

Tumor-infiltrating lymphocytes

Th2

T helper 2 cells

HLA

Human leukocyte antigen transmission

LAG-3

Lymphocyte activation gene-3

Author contributions

XZ, XW, and LL designed the study. LL, YZ, RK, and CW collected data. LL conducted the experiments and data analysis. LL wrote and edited this manuscript and created figures and tables. XZ, XW, and LL reviewed and revised the manuscript. All authors read and approved the final manuscript.

Funding

This study was supported by National Natural Science Foundation (No.82270200, No.82170189, No.82070203, No.81800194, No.81770210); Key Research and Development Program of Shandong Province (No.2018CXGC1213); China Postdoctoral Science Foundation (No. 2021T1404223); Translational Research Grant of NCRCH (No.2021WWB02, No.2020ZKMB01); Shandong Provincial Natural Science Foundation (ZR2021YQ51); Taishan Scholars Program of Shandong Province; Technology Development Project of Jinan City (No. 202134034); Shandong Provincial Engineering Research Center of Lymphoma; Academic Promotion Programme of Shandong First Medical University (No. 2019QL018).

Data availability

Publicly available datasets and online tools were utilized in this study. These resources could be found here: https://portal.gdc.cancer.gov/, https://www.proteinatlas.org/, https://www.ncbi.nlm.nih.gov/geo/.

Declarations

Conflict of interest

The authors have no relevant conflicts.

Ethics approval and consent to participate

The study was performed with the approval of the Medical Ethical Committee of Shandong Provincial Hospital (reference NSFC: NO.2021–217). All samples were collected with informed consent according to the Declaration of Helsinki.

Consent for publication

Not applicable.

Footnotes

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Contributor Information

Xin Wang, Email: xinw007@126.com.

Xiangxiang Zhou, Email: xiangxiangzhou@sdu.edu.cn.

References

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary file1 (DOCX 1232 KB) (1.2MB, docx)

Data Availability Statement

Publicly available datasets and online tools were utilized in this study. These resources could be found here: https://portal.gdc.cancer.gov/, https://www.proteinatlas.org/, https://www.ncbi.nlm.nih.gov/geo/.

Articles from Journal of Cancer Research and Clinical Oncology are provided here courtesy of Springer

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