Abstract The phenotype of pyroptosis has been extensively studied in a variety of tumors, but the relationship between pyroptosis and esophageal squamous cell carcinoma (ESCC) remains unclear. Here, 22 pyroptosis genes were downloaded from the website of Gene Set Enrichment Analysis (GSEA), 79 esophageal squamous cell carcinoma samples and [26]GSE53625 containing 179 pairs of esophageal squamous cell carcinoma samples were collected from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO), respectively. Then, pyroptosis subtypes of esophageal squamous cell carcinoma were obtained by cluster analysis according to the expression difference of pyroptosis genes, and a pyroptosis scoring model was constructed by the pyroptosis-related genes screened from different pyroptosis subtypes. Time-dependent receiver operator characteristic (timeROC) curves and the area under the curve (AUC) values were used to evaluate the prognostic predictive accuracy of the pyroptosis scoring model. Kaplan-Meier method with log-rank test were conducted to analyze the impact of the pyroptosis scoring model on overall survival (OS) of patients with esophageal squamous cell carcinoma. Nomogram models and calibration curves were used to further confirm the effect of the pyroptosis scoring model on prognosis. Meanwhile, CIBERSORTx and ESTIMATE algorithm were applied to calculate the influence of the pyroptosis scoring model on esophageal squamous cell carcinoma immune microenvironment. Our findings revealed that the pyroptosis scoring model established by the pyroptosis-related genes was associated with the prognosis and immune microenvironment of esophageal squamous cell carcinoma, which can be used as a biomarker to predict the prognosis and act as a potential target for the treatment of esophageal squamous cell carcinoma. Keywords: pyroptosis scoring model, prognosis, immune microenvironment, esophageal squamous cell carcinoma, biomarker 1 Introduction Esophageal cancer (EC) includes esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC), it is one of the deadliest cancers in the world ([27]Lagergren et al., 2017; [28]Smyth et al., 2017). China has a high prevalence of EC that accounts for more than 50% of the global morbidity and mortality, and over 90% of patients with EC in China were ESCC ([29]Abnet et al., 2018; [30]Cao et al., 2021). However, due to the low early diagnosis rate, prone to invasion and metastasis, and insensitivity to radiotherapy and chemotherapy, ESCC patients still have a low 5-year overall survival (OS) rate despite multidisciplinary treatment including surgery, chemotherapy and radiotherapy ([31]Hirano and Kato, 2019; [32]Harada et al., 2020; [33]Yang et al., 2020). Therefore, there is an urgent need for a new strategy to improve the prognosis of ESCC patients. Pyroptosis is the programmed cell necrosis mediated by gasdermins ([34]Hou et al., 2021), which is an important innate immune response in the body and plays an important role in fighting infection ([35]Xia et al., 2019; [36]Li et al., 2021a). At present, it is known that pyroptosis is closely related to a variety of diseases, and is widely involved in the occurrence and development of infectious diseases ([37]Zhao et al., 2022), nervous system-related diseases ([38]Jin et al., 2022), atherosclerotic diseases ([39]Guo et al., 2022), tumors ([40]Deng et al., 2022; [41]Liu et al., 2022; [42]Niu et al., 2022) and other diseases ([43]Al Mamun et al., 2022; [44]Wen et al., 2022). Pyroptosis plays a dual role in the occurrence and development of tumors ([45]Yu et al., 2021), on one hand, as an innate immune mechanism, pyroptosis can inhibit the occurrence and development of tumors, on the other hand, as a way of pro-inflammatory cell death, pyroptosis provides a suitable microenvironment for tumor growth ([46]Du et al., 2021). Pyroptosis is divided into classical pathways and non-classical pathways. Inflammasomes, gasdermin proteins, and pro-inflammatory cytokines are all key components of the pyroptosis pathways. Various components of the pyroptosis pathways can be regulated by a variety of cell signaling pathways. Various components of the pyroptosis pathways can regulate cell morphology, proliferation, invasion, migration, chemotherapy resistance and other malignant phenotypes through a variety of cell signal pathways, thus affecting tumor progression, and may be related to the prognosis of patients ([47]Tan et al., 2021). At present, the research on pyroptosis and EC mainly focuses on promoting the sensitivity of EC cells to chemoradiotherapy through inducing pyroptosis of EC cells via various drugs or techniques ([48]Wu et al., 2019; [49]Fang et al., 2020; [50]Li et al., 2021b; [51]Li et al., 2022b), but there are still few studies on pyroptosis and ESCC, the roles and mechanisms of pyroptosis in ESCC are far from clear ([52]Jiang et al., 2021), and more studies are needed to elucidate the relationship between ESCC and the pyroptosis phenotype. This study found that there were different pyroptosis subtypes in ESCC, and the pyroptosis scoring model constructed by pyroptosis-related genes was related to the prognosis and immune microenvironment of ESCC. This study improved our understanding of ESCC and provided a new insight for the treatment of ESCC. The development of drugs targeting pyroptosis may be a new strategy for the treatment of ESCC, which has a good therapeutic prospect. 2 Materials and methods 2.1 Data acquisition The per million reads (TPM) format gene expression profile data of ESCC was downloaded from the Cancer Genome Atlas (TCGA, [53]https://portal.gdc.cancer.gov/) using the TCGAbiolinks ([54]Colaprico et al., 2016) R package, clinical information of ESCC was downloaded by GDC software, and 79 samples were finally obtained by matching gene expression profile with clinical data. [55]GSE53625, the largest ESCC dataset in the Gene Expression Omnibus (GEO, [56]https://www.ncbi.nlm.nih.gov/gds) so far, contains 358 samples, including 179 tumor samples and 179 matched normal samples, all of which contain clinical information, and was downloaded by the GEOquery ([57]Davis and Meltzer, 2007) R package. The 22 pyroptosis genes were collected from the official website of Gene Set Enrichment Analysis (GSEA, [58]https://www.gsea-msigdb.org/gsea/index.jsp), including DHX9, GSDME, NLRP6, ELANE, NLRP1, GSDMA, GZMA, GZMB, NLRP9, NAIP, APIP, TREM2, GSDMB, GSDMC, NLRC4, GSDMD, ZBP1, CASP1, CASP4, CASP6, CASP8, AIM2. This study was in compliance with the published guidelines of TCGA and GEO, thus, ethical approval and informed consent of the patients were not required. 2.2 The expression difference of pyroptosis genes According to the tissue source, the samples in the [59]GSE53625 were divided into the normal group (n = 179) and the tumor group (n = 179). Next, the expression differences of 20 pyroptosis genes expressed in [60]GSE53625 were showed by boxplots using the ggplot2 R package. 2.3 Pyroptosis subtypes analysis and differential expression analysis According to the expression difference of pyroptosis genes in TCGA-ESCC, the samples were clustered into two clusters by ConsensusClusterPlus ([61]Wilkerson and Hayes, 2010) R package. The differentially expressed genes (DEGs) of the two subtypes were obtained by differential expression analysis using the DESeq2 ([62]Love et al., 2014) R package. The genes with logFC > 1 and adj.p < .05 were upregulated DEGs, and the genes with logFC < −1 and adj.p < .05 were downregulated DEGs, a heatmap of DEGs were displayed by pheatmap R package, and the DEGs were used for subsequent analysis. 2.4 Functional enrichment analysis The clusterProfiler ([63]Yu et al., 2012) R package was used to perform Gene Ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis based on DEGs, GO functional analysis including biological process (BP), cellular composition (CC) and molecular function (MF) analysis, and a cut-off value of false discovery rate (FDR) < .05 was considered statistically significant. GSEA was also conducted by clusterProfiler R package, the “c2.cp.kegg.v6.2.symbols” gene set was downloaded as the reference gene set from MSigDB database ([64]https://www.gsea-msigdb.org/gsea/msigdb/index.jsp), and FDR < .25 was considered significantly enriched. GSEA and Gene Set Variation Analysis (GSVA) were performed by the GSVA ([65]Hnzelmann et al., 2013) R package, “c2.cp.kegg.v6.2.symbols” and “h. all.v7.0.symbols” gene set were used as references.