A novel pyroptosis-related model for prognostic prediction in esophageal squamous cell carcinoma: a bioinformatics analysis.

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) has a poor prognosis, and the 5-year survival rate is less than 30%. Better differentiation of patients at high risk of recurrence or metastasis could guide clinical treatment. The close relationship between pyroptosis and ESCC has been recently reported. Herein, we aimed to identify genes associated with pyroptosis in ESCC and construct a prognostic risk model.

METHODS

RNA-seq data of ESCC was obtained from the The Cancer Genome Atlas (TCGA) database. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were used to calculate the pyroptosis-related pathway score (Pys). Weighted gene co-expression network analysis (WGCNA) and univariate Cox regression were used to screen for pyroptotic genes associated with prognosis, and Lasso regression was used to establish a risk score. Finally, the T test was used to compare the relationship between the model and tumor-node-metastasis (TNM) stage. Furthermore, we compared the difference of immune infiltrating cells and immune checkpoints between the low- and high-risk groups.

RESULTS

Using WGCNA, 283 genes were significantly associated with N staging and Pys. Among them, univariate Cox analysis suggested that 83 genes were associated with prognosis of ESCC patients. After that, , , and were identified as prognostic signatures separating high- and low-risk groups. Patients in the high- and low-risk groups had significantly different distributions of T (P=0.018) and N staging (P<0.05). Moreover, the 2 groups had remarkably different immune infiltrating cell scores and immune checkpoint expressions.

CONCLUSIONS

Our study identified 3 prognosis pyroptosis-related genes in the ESCC and successfully build a prognostic model. , , and 3 may serve as promising therapeutic targets in ESCC.