Characterization of Novel Anoikis-Related Genes as Prognostic Biomarkers and Key Determinants of the Immune Microenvironment in Esophageal Cancer

Yani Su¹Peng Xu²Ming Zhang²Pengfei Wen²Ke Xu²Xie Jiale²Xianjie Wan²Lin Liu²Zhi Yang²Mingyi Yang^2*

• ¹Tangdu Hospital, Air Force Medical University, Xi'an, Shaanxi Province, China
• ²Xi'an Honghui Hospital, Xi'an, China

Esophageal cancer (EC) ranks among the most prevalent malignancies globally and represents a significant and growing public health burden. This study aimed to construct a prognostic model leveraging anoikis-related genes (ARGs) to predict patient survival and elucidate the immunological microenvironment in EC. A comprehensive analysis was conducted using 11 control samples and 159 EC samples obtained from TCGA database, alongside associated clinical features. A total of 794 ARGs were curated from GeneCards database. Functional enrichment analyses of EC-related differentially expressed ARGs. Prognostic differential ARGs associated with EC were identified through univariate Cox regression analysis, while LASSO regression was employed to minimize overfitting and construct a robust risk prognostic model. The EC cohort was stratified into training and testing groups for model development and verification. Model performance was evaluated. A nomogram with high predictive accuracy was also developed to estimate the prognosis of EC patients. To assess the impact of the risk prognosis model on the immune microenvironment of EC, analyses included tumor microenvironment analysis, single-sample gene set enrichment analysis (ssGSEA), immune cell infiltration correlation analysis, and differential analysis of immune checkpoint expression. Drug sensitivity profiling was conducted to identify potential therapeutic agents for EC. Finally, the expression of selected ARGs was validated at the mRNA level in EC cell lines using real-time quantitative PCR (RT-qPCR). The ARG-based risk prognostic model was constructed incorporating four high-risk ARGs (CDK1, IL17A, FOXC2, and OLFM3) and two low-risk ARGs (PIP5K1C and MAPK1). Immune correlation analyses revealed that the highrisk group exhibited significantly lower immunological scores compared to the lowrisk group. Eight immune checkpoint-related genes displayed distinct expression differences between high-and low-risk groups. The nomogram developed from this model demonstrated high efficacy in predicting EC patient prognosis. Furthermore, six potential therapeutic agents for EC were identified: BIRB.0796, Camptothecin, CHIR.99021, Methotrexate, PF.4708671, and Vorinostat. Finally, the mRNA expression levels of ARGs were validated using RT-qPCR in EC cell lines. Compared to normal esophageal epithelial cells (NE-2), CDK1 and MAPK1 were significantly upregulated in two EC cell lines .This study provides valuable insights into the prognostic outcomes and immune microenvironment of EC through the analysis of ARGs.