Analysis of CSF2RA Expression and Its Functional and Clinical Significance in Gastric Cancer Based on Bioinformatics

Objective: To investigate the expression characteristics, clinical significance, and immune regulatory mechanisms of colony stimulating factor receptor α (CSF2RA) in gastric cancer (GC) based on multi-omics data. Methods: The TCGA GC dataset was used for integration. Key modules were identified using differential analysis and weighted gene co-expression network analysis (WGCNA). Three machine learning algorithms—LASSO regression, Random Forest, and Gradient Boosting Machine (GBM)—were combined to identify core genes. The expression patterns were validated using the TIMER2 database. Kaplan-Meier survival analysis and a nomogram model were used to assess the prognostic value. Immune microenvironment characteristics were dissected using CIBERSORT and ESTIMATE algorithms. Potential functions were explored based on Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses and protein-protein interaction (PPI) network construction. Results: CSF2RA was the sole signature gene consistently identified and cross-validated using all three machine-learning algorithms. It was significantly overexpressed in GC tissues (P < 0.05). High CSF2RA expression was strongly associated with advanced TNM stage, poor prognosis (P < 0.01), and remodeling of the tumor immune microenvironment. Enrichment analysis suggested its involvement in regulating immune escape via the “Cytokine-cytokine receptor interaction” pathway, and it showed positive correlation with immune checkpoints such as PD-L1 (CD274). The constructed nomogram (integrating CSF2RA expression, TNM stage, etc.) accurately predicted 1-, 3-, and 5-year survival rates. Conclusion: CSF2RA promotes gastric cancer progression by driving an immunosuppressive microenvironment and is a potential prognostic biomarker and immunotherapeutic target.