Comprehensive multi-omics analysis of pyroptosis for optimizing neoadjuvant immunotherapy in patients with gastric cancer

Abstract

Background: Pyroptosis plays a crucial role in immune responses. However, the effects of pyroptosis on tumor microenvironment remodeling and immunotherapy in gastric cancer (GC) remain unclear. Patients and Methods: Large-sample GEO data (GSE15459, GSE54129, and GSE62254) were used to explore the immunoregulatory roles of pyroptosis. TCGA cohort was used to elucidate multiple molecular events associated with pyroptosis, and a pyroptosis risk score (PRS) was constructed. The prognostic performance of the PRS was validated using postoperative GC samples from three public databases (n=925) and four independent Chinese medical cohorts (n=978). Single-cell sequencing and multiplex immunofluorescence were used to elucidate the immune cell infiltration landscape associated with PRS. Patients with GC who received neoadjuvant immunotherapy (n=48) and those with GC who received neoadjuvant chemotherapy (n=49) were enrolled to explore the value of PRS in neoadjuvant immunotherapy. Results: GC pyroptosis participates in immune activation in the tumor microenvironment and plays a powerful role in immune regulation. PRS, composed of four pyroptosis-related differentially expressed genes (BATF2, PTPRJ, RGS1, and VCAN), is a reliable and independent biomarker for GC. PRS^low is associated with an activated pyroptosis pathway and greater infiltration of anti-tumor immune cells, including more effector and CD4+ T cells, and with the polarization of tumor-associated macrophages in the tumor center. Importantly, PRS^low marks the effectiveness of neoadjuvant immunotherapy and enables screening of GC patients with combined positive score ≥1 who benefit from neoadjuvant immunotherapy. Conclusion: Our study demonstrated that pyroptosis activates immune processes in the tumor microenvironment. A low PRS correlates with enhanced infiltration of anti-tumor immune cells at the tumor site, increased pyroptotic activity, and improved patient outcomes. The constructed PRS can be used as an effective quantitative tool for pyroptosis analysis to guide more effective immunotherapeutic strategies for patients with GC.

Figure 1

The overall design of the study. GC, gastric cancer; DEGs, differentially expressed genes; IHC, immunohistochemistry; mIHC, multiplex immunohistochemistry staining; PRS, pyroptosis risk score.

Figure 2

Comprehensive analysis of gastric cancer cell pyroptosis on the characteristics of the tumor microenvironment and the establishment of pyroptosis risk score (PRS). (A) Integrated heatmap showed the frequency and immunoscore of the tumor microenvironment (TME)-infiltrating cells in the three pyroptosis phenotypes. (B) The Tracking Tumor Immunophenotype (TIP) algorithm was used to visualize the anti-tumor immune status of pyroptosis using an integrated heatmap. (C) The comprehensive heatmap showed the differential molecular events related to different degrees of pyroptosis in TCGA-STAD, including long noncoding RNA (lncRNA), protein-coding RNA expression, microRNA (miRNA) expression, methylated CpG sites, and differentially expressed genes in methylated regions. (D) Representative images of pathological hematoxylin & eosin (HE) staining of high- and low-degree pyroptosis. (E) Deep learning was used to identify the data of tumor-infiltrating lymphocytes from the HE pathological images of The Cancer Genome Atlas stomach adenocarcinoma (TCGA-STAD). (F) Least absolute shrinkage and selection operator (LASSO) Cox regression were used to determine the optimal lambda and corresponding coefficients of the four indicators. (G) The heatmap showed the expression of four key genes in the PRS in TCGA-STAD. (H) The stepwise multivariate Cox proportional regression risk application model was used to obtain the risk score of each patient with gastric cancer in TCGA, and the patients were classified according to the median. *, p <0.05; **, p <0.01; ***, p <0.001.

Figure 3

Data from five cohorts of four independent medical centers confirmed the prognostic power of the pyroptosis risk score (PRS) in patients with gastric cancer. (A) The diagnostic receiver operating characteristic (ROC) curve and time-related ROC curve confirmed the accuracy and stability of PRS in predicting the prognosis of patients with gastric cancer. (B) Kaplan-Meier curves for overall survival (OS) according to PRS in the five cohorts. (C) Univariate and multivariate Cox regression analyses were performed to explore the prognostic value of PRS. Variables that were statistically significant in univariate analyses were integrated into multivariate Cox regression analyses. The results of the analyses of other clinicopathological variables are shown in Tables S6-S10. *, p <0.05; **, p <0.01; ***, p <0.001. P-values for all survival analyses were calculated using the log-rank test.

Figure 4

Pyroptosis risk score (PRS)-specific landscape of the tumor immune microenvironment. (A) The differences of PRS among the three immunophenotypes (inflamed/excluded/desert) were compared. Data are presented as the mean ± SD and were analyzed using the Kruskal-Wallis test. (B) The immunophenotype composition of PRS^low and PRS^high were compared. Data were analyzed using Chi-square test. (C) The t-SNE plot demonstrates the expression patterns of 14 specific cell clusters. (D) Bar graphs show the proportion of each cell cluster in eight samples, comprising four with the highest PRS (Sample01T, PRS = 74.321; Sample03T, PRS = 77.978; Sample06T, PRS = 88.530; Sample08T, PRS = 76.615) and four with the lowest PRS (Sample02T, PRS = -48.94; Sample04T, PRS = -49.564; Sample05T, PRS = -40.841; Sample07T, PRS = -42.742). (E) The percentage of effector and CD4⁺ T cells were compared between PRS^low (n = 4) and PRS^high (n = 4). Data are presented as the mean ± SD and were analyzed using Student's t-test. (F) Representative images show the expression of effector (GZMB⁺CD8⁺) and CD4⁺ T cells in the tumor center (CT) and invasive margin (IM) in both PRS^high and PRS^low groups on multiple immunofluorescence staining (CD8-red, GZMB-cyan, CD4-green, panCK-white, and DAPI-blue; n = 26; Scale bar = 100 μm). (G) Box plots show the densities of effector (Teffs) and CD4+ T cells in PRS^low and PRS^high groups, as well as their distribution at different sites in the tumor nest and stroma. (Data were analyzed using Student's t-test; The upper and lower ends of the box indicate the interquartile range of values. The lines in the box indicate the median and each dot signifies the corresponding value obtained from individual samples; PRS^low: n = 13, PRS^high: n = 13). (H) Representative images show the expression of tumor-associated macrophages in the tumor center (CT) in both PRS^high and PRS^low groups with multiple immunofluorescence staining (CD68-green, CD206-red, iNOS-yellow, panCK-white, and DAPI-blue; n = 26; Scale bar = 100 μm). **, p <0.05; **, p <0.01; ***, p <0.001.

Figure 5

The pyroptosis risk score (PRS) can effectively predict the treatment benefit of neoadjuvant immunotherapy in patients with gastric cancer. (A) Typical representative images show PRS in multiple immunofluorescence staining (BATF2- red, PTPRJ- yellow, RGS1- cyan, VCAN- green, panCK- white, DAPI- blue). In addition, the white field of panCK stained by immunohistochemistry (IHC) on gastroscopic specimens is shown. Scale bar = 50 μm. (B) Typical representative images show the tumor regression grade of postoperative pathological tissues of PRS^low and PRS^high patients, and the CT imaging changes of PRS^low and PRS^high patients before and after neoadjuvant immunotherapy are shown in (C). (D, E) Tumor regression grade (TRG) composition and objective response rate after neoadjuvant immunotherapy were compared between PRS^low and PRS^high patients. In addition, PRS was compared between patients who benefited from neoadjuvant immunotherapy and those who did not. Data were analyzed using Student's t-test. The upper and lower ends of the box indicate the interquartile range of values. The lines in the box indicate the median. (F, G) The receiver operating characteristic (ROC) curve was used to compare the accuracy of biomarkers (PRS, CPS, and inflammatory phenotype) in predicting response to neoadjuvant immunotherapy. (H, I) Univariate and multivariate logistic regression analyses were performed to confirm the predictive value of biomarkers (PRS, CPS, and inflammatory phenotype) for neoadjuvant immunotherapy (Results: TRG1a/1b). OR: odds ratio. (J) Kaplan-Meier survival analysis compared recurrence-free survival in PRS^low patients compared with PRS^high patients. p-value survival analyses were performed using the log-rank test. (K, L) Comparison of benefits between the group receiving neoadjuvant immunotherapy and the group receiving neoadjuvant chemotherapy alone. *, p <0.05; **, p <0.01; ***, p <0.001; ICI, immune checkpoint inhibitor; CT, chemotherapy.

Figure 6

The schematic illustration depicts the attributes linked to the pyroptosis risk score (PRS) in this study.