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. 2022 Jul 18:13:963565.
doi: 10.3389/fgene.2022.963565. eCollection 2022.

Identification of pyroptosis-related subtypes, development of a prognostic model, and characterization of tumour microenvironment infiltration in gastric cancer

Feng Cao  1 , Jingtao Hu  2 , Hongtao Yuan  1 , Pengwei Cao  3 , Yunsheng Cheng  1 , Yong Wang  1
Affiliations

Identification of pyroptosis-related subtypes, development of a prognostic model, and characterization of tumour microenvironment infiltration in gastric cancer

Feng Cao et al. Front Genet. .

Abstract

As a new programmed death mode, pyroptosis plays an indispensable role in gastric cancer (GC) and has strong immunotherapy potential, but the specific pathogenic mechanism and antitumor function remain unclear. We comprehensively analysed the overall changes of pyroptosis-related genes (PRGs) at the genomic and epigenetic levels in 886 GC patients. We identified two molecular subtypes by consensus unsupervised clustering analysis. Then, we calculated the risk score and constructed the risk model for predicting prognostic and selected nine PRGs related genes (IL18RAP, CTLA4, SLC2A3, IL1A, KRT7,PEG10, IGFBP2, GPA33, and DES) through LASSO and COX regression analyses in the training cohorts and were verified in the test cohorts. Consequently, a highly accurate nomogram for improving the clinical applicability of the risk score was constructed. Besides, we found that multi-layer PRGs alterations were correlated with patient clinicopathological features, prognosis, immune infiltration and TME characteristics. The low risk group mainly characterized by increased microsatellite hyperinstability, tumour mutational burden and immune infiltration. The group had lower stromal cell content, higher immune cell content and lower tumour purity. Moreover, risk score was positively correlated with T regulatory cells, M1 and M2 macrophages. In addition, the risk score was significantly associated with the cancer stem cell index and chemotherapeutic drug sensitivity. This study revealed the genomic, transcriptional and TME multiomics features of PRGs and deeply explored the potential role of pyroptosis in the TME, clinicopathological features and prognosis in GC. This study provides a new immune strategy and prediction model for clinical treatment and prognosis evaluation.

Keywords: gastric cancer; immunity; programmed death; pyroptosis; tumour microenvironment.

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Conflict of interest statement

Author JH was employed by the company China Eastern Airlines Co., Ltd. The remaining 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
Genetic and transcriptional alterations of PRGs in GC (A) Mutation frequencies and types of PRGs in 433 samples in TCGA (B) CNV of PRGs in 433 samples in TCGA (C) Chromosomal localization of PRGs with CNV. PRGs: pyroptosis-related genes. CNV: copy number variation.
FIGURE 2
FIGURE 2
Identification of pyroptosis-related subtypes (A) Differential expression of PRGs between tumour tissues and normal tissues (B) PRG interaction network (C) Consensus unsupervised clustering defines two clusters (k = 2) and their correlation area (D) PCA divided GC samples into two subtypes. PCA: principal Component Analysis.
FIGURE 3
FIGURE 3
Difference analysis between two PRG-related subtypes (A) Kaplan-Meier survival curves between the two subtypes (B) Differences in clinical characteristics in distinct subtypes (C) GSVA enrichment analysis (D) Differences in immune cell infiltration between the two subtypes (E,F) GO and KEGG enrichment analyses of pyroptosis-related DEGs. DEGs: differentially expressed genes. GO: Gene Ontology. KEGG: Kyoto Encyclopedia of Genes and Genomes.
FIGURE 4
FIGURE 4
Identification of DEG subtypes and construction of the prognostic model (A) Consensus unsupervised clustering defines two clusters (k = 3) and their correlation area (B) Kaplan-Meier survival curves between the three DEG-related subtypes (C) Differences in clinical characteristics between the three DEG-related subtypes (D) Expression differences of PRGs between the three DEG-related subtypes (E) Alluvial diagram of subtype distributions in groups with different PRG scores and survival outcomes (F,G) Differences in RS with respect to different phenotypes (H) Differential expression of PRGs in the high-and low-risk groups. RS: risk score.
FIGURE 5
FIGURE 5
Prognostic value of the pyroptosis-associated DEG signature in the training and test cohorts (A,B) RS distribution (C,D) Survival status of GC patients (E,F) Heatmap of the 7 DEGs.
FIGURE 6
FIGURE 6
Prediction model (A,B) Kaplan-Meier survival curves between the high- and low-risk groups in the training and test cohorts (C,D) ROC curves predicting 1-, three- and 5-years OS in the training and test cohorts of GC patients (E) Nomogram for predicting the 1-, three- and 5-years OS of GC patients (F) Calibration curves of the nomogram. ROC: receiver operating characteristic.
FIGURE 7
FIGURE 7
Analysis of TME characteristics between the high- and low-risk groups (A) Correlation between immune cells and RS (B) Correlations between the abundance of immune cells and 9 DEGs (C) Differences in the StromalScore and ImmuneScore between the high- and low-risk groups (D,E) Relationships between RS and MSI (F) Correlation of RS with CSC. TME: tumour microenvironment. MSI: microsatellite instability. CSC: cancer stem cells.
FIGURE 8
FIGURE 8
Mutation and drug susceptibility analysis (A,B) Mutation and drug susceptibility analysis (C,D) Relationships between RS and TBM (E) Relationships between RS and chemotherapeutic sensitivity. TMB: tumour mutational burden.
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