Development and validation of a prognostic model related to pyroptosis-related genes for esophageal squamous cell carcinoma using bioinformatics analysis.

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is one of the most lethal malignant tumors worldwide, and a larger number of ESCC patients have unsatisfactory overall survival (OS) rates. While pyroptosis participates in the development of a variety of malignancies, the function of pyroptosis-related genes (PRGs) in ESCC is still obscure. The aim of this study was to construct the pyroptosis-related prognostic model for ESCC, which will be developed to stratify the risk hazards of ESCC patients and to provide theoretical evidence for individualized treatment.

METHODS

RNA-seq data of ESCC were download from the NCBI Gene Expression Omnibus (GEO) database. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to explore the potential biological functions or pathways. OS was considered as the primary prognosis outcome in this study. The riskscore was constructed by Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis. The pyroptosis-related prognostic model was constructed based on all independent prognostic factors and verified by C-index, Receiver operating characteristic (ROC) curves, and Calibration curves, and the role of the riskscore in ESCC immunotherapy was evaluated by the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm.

RESULTS

The current study found 31 differentially expressed PRGs (P<0.001), and functional enrichment analysis showed these PRGs were enriched in positive regulation of cytokine production, interleukin-1 beta production. Univariate and multivariate Cox regression analysis were applied to validate that the riskscore based on four prognostic PRGs (HMGB1, IL-18, NLRP7, and PLCG1) was an independent prognostic factor for ESCC, and the C-index of prognostic model related to the riskscore (C-index =0.705) was higher than that of tumor node metastasis (TNM) stage (0.620). The low-risk group showed a better efficacy of immune checkpoint inhibitors.

CONCLUSIONS

The riskscore related to PRGs was one of the independent prognostic factors for ESCC. Moreover, the prognostic model related to the riskscore could be used to predict the OS of ESCC patients effectively. However, there still were several limitations in this study, such as no external validation sample. In summary, our data provides a novel perspective in exploring the potential prognostic biomarkers of ESCC.