Comprehensive Analysis of Anoikis-related Genes in Prognosis and Immune Infiltration of Gastric Cancer Based on Bulk and Single-cell RNA Sequencing Data

Background: Accumulating evidence suggests that anoikis resistance is a key process in cancer cell metastasis, making it an attractive therapeutic target. Therefore, anoikis may become a new treatment for gastric cancer.

Methods: We used the univariate Cox regression method to screen gastric cancer-related anoikis genes, and a prognostic risk model was established. We analyzed differences between high- and low-risk groups in terms of tumor infiltrating immune cells, gene mutation signatures, and treatment of gastric cancer. Analysis of model associated genes at single-cell resolution was performed.

Results: We filtered to 12 anoikis-related genes and built a prognostic risk model using seven of them, which performed well in multiple datasets. Patients with CCDC178 mutations had a worse prognosis. We also found that patients at low risk were more likely to benefit from chemotherapy and immunotherapy. ERBB2 was found to be highly expressed in epithelial cells and fibroblasts. Our analysis also indicated that gastric cancer samples with high infiltration of iCAFs had a worse prognosis.

Conclusion: Seven anoikis-related genes were selected to establish a risk model. The model can be used to predict the prognosis of patients and guide the drug treatment, which provides a new idea for the evaluation and treatment of gastric cancer patients.