An Anoikis Resistance Phenotype Converged to Immune Dysfunction and Resistance to Immune Checkpoint Blockades in Gastric Cancer

ABSTRACT Background Gastric cancer (GC) continues to pose a significant global health challenge due to its high rates of incidence and mortality, with the majority of cases identified at advanced stages. Immunotherapy, particularly immune checkpoint blockades (ICBs), has demonstrated considerable therapeutic potential; however, many patients do not exhibit a favorable response. As a result, constructing a predictive model to assess ICBs' responsiveness is essential for enhancing treatment outcomes. Methods Using consensus clustering based on anoikis‐related gene expression, GC patients were stratified into two subclusters. Differences in tumor immune microenvironment, ICB resistance, genomic alterations, methylation profiles, and transcriptional networks were analyzed. A machine learning‐based strategy was employed to develop a consensus anoikis‐related gene signature (ARGS). Potential therapeutic targets were identified through single‐cell RNA sequencing (scRNA‐seq), and validation was conducted using multiplex immunofluorescence and immunohistochemistry in an in‐house cohort (n = 28), including 14 ICB responders and 14 nonresponders. Results The anoikis‐resistant cluster (Cluster A) was associated with poorer survival, immunosuppressive infiltration, lower tumor mutation burden, and ICB resistance. ScRNA‐seq revealed high fibroblast and endothelial infiltration, with GLI3 + cancer‐associated fibroblasts suggesting Hedgehog pathway involvement. The ARGS model effectively stratified patients, with elevated scores associated with immunotherapy resistance, enhanced AR characteristics, and poorer clinical outcomes. Conclusion The ARGS model offers a valuable tool for predicting prognosis and ICB response in GC, and may guide more precise and personalized immunotherapeutic strategies.