Artificial intelligence-driven integration of single-cell RNA sequencing and transcriptome analysis to decipher APOE’s role in gastric cancer prognosis and therapy

Abstract

Abstract Background Gastric cancer (GC) remains a global health challenge due to its high mortality rate and the lack of specific diagnostic methods. Tumor heterogeneity significantly hinders effective treatment, necessitating advanced techniques to dissect its complexity. Artificial intelligence (AI) offers transformative potential in uncovering intricate cellular dynamics and gene regulatory networks. This study leverages single-cell RNA sequencing (scRNA-seq) combined with transcriptome analysis, enhanced by AI-driven analytics, to explore the tumor microenvironment and identify novel prognostic markers and therapeutic targets in GC. Methods scRNA-seq and transcriptome datasets of GC patients were analyzed using AI-enhanced methodologies to unravel tumor heterogeneity and microenvironmental dynamics. Macrophage subsets were identified as critical components within the GC microenvironment. High-variance gene screening in these subsets pinpointed apolipoprotein E (APOE) as a hub gene. Experimental validation of APOE expression in GC samples and functional studies in GC cell lines were conducted. Results Bioinformatics and AI-enabled analyses confirmed the immunosuppressive role of APOE in GC. An immune-related survival model was developed to predict immunotherapy responses and patient prognoses. Mechanistically, APOE was found to induce immunosuppression through M2 macrophage polarization, promoting tumor progression and leading to poorer outcomes in GC patients. Conclusion This study highlights the potential of AI-driven approaches in elucidating the role of APOE in GC progression. APOE’s regulatory effects on M2 macrophages underscore its value as a prognostic marker and therapeutic target, paving the way for precision medicine in GC management.

Keywords

• Artificial intelligence
• APOE
• Gastric cancer
• Tumor immune microenvironment