The rapid progress and poor prognosis of the exercise of esophageal squamous cell carcinoma (ESCA) bring great challenges to the treatment. Hypoxia in the tumor microenvironment has become a key factor in the pathogenesis of tumors. However, due to the lack of clear therapeutic targets, hypoxia targeted therapy of ESCA is still in the exploratory stage.
To bridge this critical gap, we mined a large number of gene expression profiles and clinical data on ESCA from public databases. First, weighted gene co-expression network analysis (WGCNA) and functional enrichment analysis were performed. We next delved into the relationship between hypoxia and apoptotic cell interactions. Meanwhile, using LASAS-Cox regression, we designed a robust prognostic risk score, which was subsequently validated in the GSE53625 cohort. In addition, we performed a comprehensive analysis of immune cell infiltration and tumor microenvironment using cutting-edge computational tools.
Hypoxia-related genes were identified and classified by WGCNA. Functional enrichment analysis further elucidated the mechanism by which hypoxia affected the ESCA landscape. The results of the interaction analysis of hypoxia and apoptotic cells revealed their important roles in driving tumor progression. The validation results of the prognostic risk score model in the GSE53625 cohort obtained a good area under the receiver operating characteristic (ROC) curve, and the risk score was independently verified as a significant predictor of ESCA outcome. The results of immune cell infiltration and tumor microenvironment analysis reveal the profound impact of immune cell dynamics on tumor evolution.
Overall, our study presents a pioneering hypoxiacentered gene signature for prognostication in ESCA, providing valuable prognostic insights that could potentially revolutionize patient stratification and therapeutic management in clinical practice.