Paraptosis-related classification and risk signature for prognosis prediction and immunotherapy assessment in gastric cancer

Abstract

Background: Gastric cancer (GC) poses a significant health threat due to its prevalence and poor prognosis. To improve outcomes, there is an urgent need for novel biomarkers. Paraptosis, a recently discovered form of programmed cell death, remains uninvestigated in GC, and understanding its mechanisms could offer new insights.

Materials and methods: In our study, we utilized the TCGA-STAD dataset as the training cohort and GSE84433 as the validation cohort to explore the association between paraptosis-related genes and the clinical risk of gastric cancer (GC). Our goals were to analyze the prognostic value and potential biological mechanisms of these genes. We conducted various analyses, including consistent clustering, differential gene expression analysis, enrichment analysis, and immune infiltration analysis. Ultimately, we developed a paraptosis-related risk signature (PRRS) to assess survival prognosis, drug sensitivity, and immune infiltration based on risk classification. The reliability of our findings was further verified through immunohistochemical staining.

Results: Our results revealed distinct subgroups (C1, C2, and C3) among gastric cancer patients through consensus clustering based on 65 paraptosis-related genes. These subgroups exhibited significant variations in survival rates, immunity scores, and immune cell infiltration. We then developed the Paraptosis-Related Risk Score (PRRS) using cox-lasso regression analysis, incorporating genes such as SLCO2A1, VCAN, RAMP1, and MANEAL. The PRRS effectively distinguished between high-risk and low-risk populations. Validation in an independent dataset and immunohistochemical staining confirmed the accuracy of the PRRS. These findings highlight the close relationship between paraptosis and the immune microenvironment of gastric cancer tumors, and demonstrate the PRRS's robust performance in predicting patient survival.

Conclusion: This study underscores the link between paraptosis subtypes and changes in the gastric cancer immunotumour microenvironment. We developed and validated the Paraptosis-Related Risk Score (PRRS), which effectively predicts survival, immune infiltration, and drug sensitivity in gastric cancer patients. Our findings enhance the understanding of paraptosis and suggest potential new therapeutic strategies for gastric cancer.

Keywords: Classification; Gastric Cancer; Immunotherapy; Paraptosis; Prognosis.

Fig. 1

Identification of paraptosis-related subtypes by consistent clustering.(A) Heatmap of expression of 65 paraptosis-related genes in GC samples, including tumor and normal samples; (B, C) Cluster analysis of the cumulative distribution function (CDF) indicating the area under the curve and Delta-decreasing trend when k = 2–10 area under the curve and Delta decreasing trend for k = 2–10; (D) Example cluster consistency plot showing that the consensus value is optimal when k = 3; (E) Heatmap of paraptosis-related gene expression in the three subtypes; (F) Consensus matrix for optimal k = 3; (G) Kaplan-Meier curves of overall survival for the three subtypes (p = 0.01)

Fig. 2

Identification of differentially expressed genes (DEGs) and biological pathways between the three paraptosis subtypes. (A) Venn diagram showing overlapping paraptosis-related DEGs; (B) heatmap showing the expression of 382 overlapping DEGs between the three isoforms; (C, D) bubble plots showing the enrichment of GO and KEGG potential signaling pathways

Fig. 3

Somatic mutations and immune infiltration in the three paraptosis subtypes. (A-C) Mutation map waterfall plot showing the 15 most common mutated genes in GC differing between groups C1, C2, and C3; (D) ESTIMATE box-and-line plot showing the IMMUNE SCORE, STOMAL SCORE, and ESTIMATE SCORE of the infiltrating different subtypes; (E) CIBERSORT box-and-line plot illustrating the differences in immune cell infiltration among the paraptosis subtypes; (F) box-and-line plot showing differences in the expression of selected immune checkpoints in the three paraptosis subtypes. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001

Fig. 4

Construction and validation of the paraptosis-related risk signature. (A, B) Lasso-Cox analysis of four paraptosis-related prognostic genes extracted; (C) Prognostic heatmap showing the relationship between different risk scores, patient survival and gene expression changes in the paraptosis-related risk model; (D, E) K-M curves for MRRS in the training dataset TCGA-STAD and validation dataset GSE84433

Fig. 5

Association of PRRS with GC prognosis. (A) Multivariate Cox analysis to assess the independent prognostic value of MRRS in patients with GC; (B-D) nomo plots, calibration plots, and time-dependent ROC curve analyses used to predict 1-, 3-, and 5-year survival probabilities of patients

Fig. 6

Association of PRRS with tumor microenvironment. Scatterplot showing the correlation between risk score and training set naive B cells, activated CD4 + memory T cells, resting CD4 + memory T cells, follicular helper T cells, resting NK cells, M0 Macrophages, M2 Macrophages, M2 Macrophages, and resting Mast cells

Fig. 7

Validation of PRRS association with tumor microenvironment. Shows the validation of the correlation of the risk score with the corresponding immune cell infiltration in the validation set GSE84433 cohort

Fig. 8

Box line plot showing the results of drug sensitivity analysis for GC patients in the high risk score group and low risk score group

Fig. 9

Validation of paraptosis-related prognostic genes at the protein expression level. (A): Immunohistochemical staining of paraptosis-related prognostic genes in Human Protein Atlas database (Staining degree: high, medium, low, and not detected); (B): Kaplan-Meier curve of overall survival of paraptosis-related prognostic genes in Human Protein Atlas database. (B) Kaplan-Meier curves for overall survival of paraptosis-related prognostic genes in the Human Protein Atlas database