Prognostic and immunological implications of protein kinases in gastric cancer: a focus on hub gene ABL2 and its impact on the polarization of M2 macrophages

Abstract

Abstract Background Protein kinases are essential cellular signal modulators involved in tumorigenesis, metastasis, immune response, and drug resistance. However, the comprehensive features and clinical significance of protein kinases in gastric cancer (GC) remain inconclusive. Methods We analyzed the transcriptional profiles of protein kinases in GC patients from the GEO and TCGA databases. Based on differentially expressed kinase genes (DE-KGs), a novel cluster was identified to assess its association with patient survival and the tumor microenvironment (TME) in GC. Subsequently, an optimal DE-KGs-based model (DE-KGsM) was determined using 101 machine-learning algorithm combinations. This model was evaluated using multi-omics data to investigate its associations with patient prognosis, clinical features, tumor microenvironment, tumor-infiltrating immune cells (TIICs), and immunotherapy response. Furthermore, scRNA-seq analysis and TIMER algorithm were applied to determine the correlation between the hub gene (ABL2) in the DE-KGsM and Macrophages. Finally, in vitro experiments were performed to explore the immune-related mechanisms of ABL2 in GC. Results We identified two molecular subtypes of GC patients based on 64 DE-KGs expression. Significant differences were observed in overall survival and TIIC characteristics between Cluster 1 and Cluster 2. Among these 64 DE-KGs, we identified an optimal DE-KGsM that could be a prognostic indicator in GC. TIICs and TIDE analyses exhibited that GC patients in the high-DE-KGsM score group had a higher proportion of M2 macrophages and lower response rates to ICI treatment. scRNA-seq analysis indicated that ABL2 might play an indispensable role in tumor immunity. Furthermore, in vitro experiments demonstrated that ABL2 accelerated the proliferation, migration, and invasion of GC cells, as well as the polarization of M2 macrophages. Conclusions The DE-KGsM could be a powerful predictor of GC patients’ survival and might facilitate the development of personalized therapy. Furthermore, as a hub gene in the DE-KGsM, ABL2 could be an immunological biomarker that modulates the polarization of M2 macrophages, thereby promoting GC progression. Clinical trial number Not applicable.

Keywords

• Protein kinases
• scRNA-seq analysis
• Gastric cancer
• Prognosis
• Machine learning
• M2 macrophages