CELL INTELLIGENCE · 肿瘤细胞治疗研究

The study aims at investigating the function of macrophage-related genes (MRGs) within the bladder cancer immune microenvironment and exploring their potential value in prognosis prediction and therapeutic decision-making. METHOD: This study integrated bladder cancer transcriptomic data from the TCGA and GEO databases along with single-cell RNA sequencing (scRNA-seq) data to systematically identify key MRGs. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and single-cell sequencing analysis served for screening for core MRGs. The results from LASSO Cox regression analysis were used for constructing a survival risk prediction model, together with the evaluation of the model's predictive accuracy. Besides, core MRGs were subjected to immune cell infiltration and drug sensitivity analyses for the elucidation of their roles in immune regulation and therapeutic response. Furthermore, key genes in the prognostic model were validated using PCR, Western blot, and immunohistochemistry. RESULT: This study identified 11 core genes significantly associated with macrophages and developed a risk prediction model based on ANXA1, ST3GAL5, and VIM. The model demonstrated moderate predictive performance across all samples (AUC = 0.682), indicating potential utility for patient stratification. Immune analysis revealed that high-risk patients exhibited a distinctly immunosuppressive tumor microenvironment (TME), characterized by increased infiltration of M2 macrophages and neutrophils, along with a significant reduction in effector immune cells of CD8⁺ T cells and NK cells. Additionally, high-risk patients displayed greater sensitivity to targeted therapies but reduced sensitivity to conventional chemotherapy. According to in vitro and in vivo experiments, ST3GAL5 overexpression significantly promoted bladder cancer cell proliferation and tumor growth, underscoring its potential role in tumor progression.

This study highlights the crucial impact of MRGs on the TME of bladder cancer and constructs a risk prediction model with moderate predictive performance that may assist in patient stratification, although further validation in independent cohorts is required.