Conclusions
Our findings present a validated risk scoring model and prognostic nomogram as valuable tools for predicting patient outcomes and guiding personalized treatments in EAC. This study underscores the potential of molecular clustering and PANoptosis-based prognostic features in predicting patient survival and understanding the tumor microenvironment's complexity, especially the metabolic and immune profiles, in EAC. These insights enhance our understanding of PANoptosis in EAC and provide new avenues for its diagnosis and therapy.
Methods
We conducted a comprehensive bioinformatics analysis utilizing TCGA, GTEx, and GEO datasets to identify PANoptosis-related genes (PRGs) associated with EAC. From this analysis, we developed a prognostic risk score model based on 8 prognostically significant differentially expressed PRGs. This model was externally validated and compared with traditional staging methods. Functional analyses, including gene expression profiling, pathway enrichment analysis, and immune infiltration assessment, were conducted to elucidate the biological mechanisms influencing prognosis. To identify PANoptosis-related hub genes, we employed Weighted Gene Co-expression Network Analysis (WGCNA). The expression profiles of the hub gene were examined using reverse transcription-quantitative PCR (RT-qPCR) and western blotting. Furthermore, the effects of the hub genes knockdown or overexpression on EAC cell behavior were verified through in vitro experiments, including cell counting kit (CCK)-8, transwell and wound healing assay.
Results
The prognostic risk score model effectively predicts patient outcomes, supported by principal component analysis (PCA) and receiver operating characteristic (ROC) curves. The resulting prognostic nomogram, which integrates clinical features and the risk score, outperforms traditional staging systems, offering enhanced predictive accuracy. WGCNA identified gene modules significantly correlated with EAC clinical traits, highlighting the biological relevance of these genes to disease progression. Functional enrichment analyses shed light on significant biological processes and pathways associated with high-risk EAC, including lipid metabolism and hormone transport. Immune infiltration analysis revealed distinct immune profiles between risk groups, pinpointing potential immunotherapeutic targets. Furthermore, drug sensitivity analysis indicated specific compounds that may be more effective in high-risk groups. Notably, MMP12 emerged as a key mediator and further experimental results revealed that the lower the degree of cell differentiation, the higher the expression level of MMP12 in EAC. The knockdown of MMP12 significantly inhibited cell proliferation and migration. Conclusions: Our findings present a validated risk scoring model and prognostic nomogram as valuable tools for predicting patient outcomes and guiding personalized treatments in EAC. This study underscores the potential of molecular clustering and PANoptosis-based prognostic features in predicting patient survival and understanding the tumor microenvironment's complexity, especially the metabolic and immune profiles, in EAC. These insights enhance our understanding of PANoptosis in EAC and provide new avenues for its diagnosis and therapy.