AUTHOR=Hu Jie , Liu Qilong , Feng Bi , Lu Yanling , Chen Kai 

TITLE=Deciphering the Hypoxia-immune interface in esophageal squamous carcinoma: a prognostic network model

JOURNAL=Frontiers in Oncology

VOLUME=Volume 13 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.1296814

DOI=10.3389/fonc.2023.1296814

ISSN=2234-943X

ABSTRACT=Esophageal squamous cell carcinoma (ESCA), characterized by aggressive progression and poor prognosis, poses significant therapeutic challenges. Current treatment modalities are often compromised by late-stage diagnosis and resistance to conventional therapies, underscoring the urgent need for novel targeted approaches. Hypoxia within the tumor microenvironment has emerged as a pivotal factor in cancer pathogenesis, yet hypoxia-targeted therapies in ESCA remain underexplored due to the absence of clearly defined therapeutic targets. Bridging this critical gap, our investigation mined extensive gene expression profiles and clinical data on ESCA from public databases. Through weighted gene co-expression network analysis (WGCNA), we identified and categorized hypoxia-associated genes. Functional enrichment analyses further elucidated the mechanisms by which hypoxia influences the ESCA landscape. Our research delved into the nexus between hypoxia and apoptotic cell interactions, unveiling their significant role in driving tumor progression. Utilizing LASSO-Cox regression, we devised a robust prognostic risk score, which was subsequently validated within the GSE53625 cohort, as reflected by a commendable area under the receiver operating characteristic (ROC) curve. This risk score was independently validated as a significant predictor for ESCA outcomes. Moreover, comprehensive analyses of immune cell infiltration and the tumor microenvironment were conducted, employing cutting-edge computational tools, which revealed the profound influence of immune cell dynamics on tumor evolution. Overall, our study presents a pioneering hypoxia-centered gene signature for prognostication in ESCA, providing valuable prognostic insights that could potentially revolutionize patient stratification and therapeutic management in clinical practice.