PANoptosis-based molecular subtype and prognostic model predict survival and immune landscape in esophageal cancer

Abstract

Abstract Purpose To establish a prognostic model to predict the survival of patients with esophageal cancer (EC). Methods We extracted the expression profiles of prognostic-related genes and clinicopathological data from TCGA and GEO databases. Subsequently, a comprehensive bioinformatics analysis was conducted to construct a prognostic model utilizing LASSO and multivariate Cox regression. The stability of the risk signature was validated through Kaplan-Meier and ROC curve analyses on the training, internal testing, and external testing sets. Furthermore, we developed a nomogram that incorporates the risk score and clinical features to predict the suvival. Additionally, a nomogram incorporating the risk score and relevant clinical parameters was developed to enhance survivorship prediction. Furthermore, we delved into exploring the correlation between the risk score and immune cell abundance, expression of cancer checkpoints, as well as responses to immunotherapy and chemotherapeutic agents. Results In this study, we successfully identified 19 prognosis-related genes out of a pool of 65 PANoptosis-related genes (PRGs) sourced from existing literature. Through consensus clustering analysis, we classified patients into two distinct groups as PANcluster A and B. Furthermore, the risk score derived from the five PANoptosis-related signatures emerged as an independent prognostic factor among patients with EC. To enhance the prognostic accuracy, we devised a nomogram integrating the risk score with clinical risk characteristics, enabling the prediction of 1-year, 2-year, and 3-year overall survival (OS) rates. Notably, individuals classified in the high-risk group demonstrated poorer prognoses compared to their low-risk counterparts. Furthermore, the risk score displayed substantial correlations with immune cell abundance, expression levels of cancer checkpoints, and responses to immunotherapy and chemotherapeutic agents. These pivotal findings underscore the significance of considering PANoptosis-related patterns in improving prognostic assessment and predicting treatment responses in patients diagnosed with esophageal cancer. Conclusion We constructed a reliable prognostic risk model for EC utilizing five PRGs. The developed nomogram serves as a valuable tool in predicting patient outcomes, offering crucial insights that can inform and guide treatment decisions for individuals diagnosed with EC.