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. 2023 Jul 12:13:1173181.
doi: 10.3389/fonc.2023.1173181. eCollection 2023.

A pyroptosis-related gene signature that predicts immune infiltration and prognosis in colon cancer

Mingjian Wu  1 , Shuai Hao  2 , Xiaoxiang Wang  3 , Shuguang Su  4 , Siyuan Du  4 , Sitong Zhou  5 , Ronghua Yang  6 , Hanpeng Du  1
Affiliations

A pyroptosis-related gene signature that predicts immune infiltration and prognosis in colon cancer

Mingjian Wu et al. Front Oncol. .

Abstract

Background: Colon cancer (CC) is a highly heterogeneous malignancy associated with high morbidity and mortality. Pyroptosis is a type of programmed cell death characterized by an inflammatory response that can affect the tumor immune microenvironment and has potential prognostic and therapeutic value. The aim of this study was to evaluate the association between pyroptosis-related gene (PRG) expression and CC.

Methods: Based on the expression profiles of PRGs, we classified CC samples from The Cancer Gene Atlas and Gene Expression Omnibus databases into different clusters by unsupervised clustering analysis. The best prognostic signature was screened and established using least absolute shrinkage and selection operator (LASSO) and multivariate COX regression analyses. Subsequently, a nomogram was established based on multivariate COX regression analysis. Next, gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were performed to explore the potential molecular mechanisms between the high- and low-risk groups and to explore the differences in clinicopathological characteristics, gene mutation characteristics, abundance of infiltrating immune cells, and immune microenvironment between the two groups. We also evaluated the association between common immune checkpoints and drug sensitivity using risk scores. The immunohistochemistry staining was utilized to confirm the expression of the selected genes in the prognostic model in CC.

Results: The 1163 CC samples were divided into two clusters (clusters A and B) based on the expression profiles of the 33 PRGs. Genes with prognostic value were screened from the DEGs between the two clusters, and an eight PRGs prognostic model was constructed. GSEA and GSVA of the high- and low-risk groups revealed that they were mainly enriched in inflammatory response-related pathways. Compared to those in the low-risk group, patients in the high-risk group had worse overall survival, an immunosuppressive microenvironment, and worse sensitivity to immunotherapy and drug treatment.

Conclusion: Our findings provide a foundation for future research targeting pyroptosis and new insights into prognosis and immunotherapy from the perspective of pyroptosis in CC.

Keywords: colon cancer; prognosis; pyroptosis; risk signature; tumor immune microenvironment.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Overview of the study flowchart.
Figure 2
Figure 2
Characterization of pyroptosis subgroups in colon cancer and screening of subtype-associated genes. (A) CDF plot when k takes different values. (B) Heat map of sample clustering at k = 2. (C) Principal component analysis (PCA) of two pyroptosis subgroups. (D) Expression of pyroptosis genes in different subgroups. (E) Volcano plot of differentially expressed genes; red and green indicate upregulated and downregulated genes, respectively, in the cluster 2 group. (F) Differentially expressed genes in heat map; red and green indicate high and low expression, respectively. (G, H) Gene Ontology (GO) analysis of bubble and heat maps. (I, J) Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of bubble and heat maps.
Figure 3
Figure 3
Characterization of differentially expressed gene subgroups in colon cancer. (A) Forest plot of univariate COX proportional regression analysis. (B) CDF plot when k takes different values. (C) Heat map of samples clustering at k = 2. (D) Box plot of differential expression of pyroptosis-related genes among different subgroups. *P<0.05, **P<0.01, ***P<0.001. (E) Principal component analysis (PCA) of two differentially expressed gene subgroups. (F) Heat map of differential expression of 16 genes between different subgroups.
Figure 4
Figure 4
Clinical characteristics of colon cancer. (A) Number and coefficients of enrolled characteristics at different λ states during least absolute shrinkage and selection operator (LASSO) model building. (B) Optimal λ values for LASSO model. (C) Survival curves for patients in the high- and low-risk groups. (D) Forest plot of multivariate COX regression analysis combining clinical characteristics. (E) Risk factor triplot with risk score in the upper panel and survival outcome in the middle panel. The lower panel shows the molecular expression in the prognostic model. (F) Sankey plots between high- and low-risk groups with pyroptosis regulatory subgroups, pyroptosis differentially expressed gene subgroups, and survival status. *p < 0.05, **p < 0.01, and ***p < 0.001.
Figure 5
Figure 5
Prognostic models and evaluation of colon cancer. (A) Nomogram constructed based on multivariate COX regression results. (B) Time-dependent ROC curves for prognostic models. (C-E) Calibration curves for prognostic models at 1, 3, and 5 years. (F) Comparison of age in high- and low-risk groups. (G) Comparison of sex in high- and low-risk groups. (H) Comparison of clinical stage in high- and low-risk groups. (I) Comparison of high- and low-risk groups in survival status. *p < 0.05, **p < 0.01, and ***p < 0.001. ns means no significance.
Figure 6
Figure 6
Mutation characteristics and copy number variation (CNV) analysis of high- and low-risk groups. (A) Waterfall plot of the top 30 mutated genes in the high-risk group. (B) Waterfall plot of the top 30 mutated genes in the low-risk group. (C) Tumor mutation burden (TMB) distribution characteristics of patients in the high-risk group. (D) TMB distribution characteristics of patients in the low-risk group. (E) CNV distribution characteristics of patients in the high-risk group. (F) CNV distribution characteristics of patients in the low-risk group.
Figure 7
Figure 7
Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) for high- and low-risk groups. GSEA pathways were significantly enriched in high- and low-risk groups, including (A) Kyoto Encyclopedia of Genes and Genomes (KEGG) ECM receptor interaction. (B) KEGG focal adhesion. (C) KEGG cell cycle analysis. (D) KEGG chemokine signaling pathway. (E) KEGG autoimmune thyroid disease. (F) KEGG dilated cardiomyopathy. ES and NES > 0 indicate that the pathway was enriched in the high-risk group, whereas ES and NES < 0 indicate that the pathway was enriched in the low-risk group. (G) Heat map of 48 pathways obtained from GSVA analysis.
Figure 8
Figure 8
Differences in immune infiltration in high- and low-risk groups. (A) Panorama of 22 immune cell infiltrations calculated using the CIBERSORTX algorithm. (B, C) Heat map of association between 22 immune cells in the high- and low-risk groups. (D-O) Violin plot of differential numbers of 12 immune cells between high- and low-risk groups. *P<0.05, **P<0.01, ***P<0.001.
Figure 9
Figure 9
Immunotherapy and drug sensitivity analysis. Differences in (A) CTLA4 neg PD1 neg, (B) CTLA4 neg PD1 pos, (C) CTLA4 pos PD1 neg, and (D) CTLA4 pos PD1 pos between the high- and low-risk groups. Based on the GDSC database, differences in drug sensitivity of common antineoplastic drugs between the high- and low-risk groups were determined: (E) RDEA119, (F) BMS.536924, (G) Z.LLNle.CHO, (H) WZ.1.84, (I) bicalutamide, (J) PD.0325901, (K) OSI.906, (L) BMS.754807, (M) bortezomib, (N) cyclopamine, (O) erlotinib, and (P) WH.4.023. Differences in (Q) TIDE, (R) exclusive, (S) CD8, and (T) CD274 scores were calculated using the TIDE algorithm in the high- and low-risk groups.
Figure 10
Figure 10
Validation analysis of the GSE17537 dataset. (A) Plot of survival curves for patients in the high and low risk groups of the GSE17537 dataset. (B) Time-dependent ROC curves for the prognostic model of the GSE17537 dataset. (C-E) Plots of 1-year (C), 3-year (D), and 5-year (E) calibration curves for the prognostic model.
Figure 11
Figure 11
Validation of the selected genes in the prognostic model at the protein level. IHC staining identified the gene expression in clinical specimens. High expressions of MMP3 (A), CXCL10 (B), MMP12 (C), KRT23 (D), TNFAIP6 (E) and CCL8 (F) were presented in colon cancer tissues (n=25) by IHC staining. IHC: Immunohistochemistry. IHC: Immunohistochemistry. IHC stain, DAB, original magnification x 100 (inset, IHC stain, DAB, original magnification x 400).
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