Tan Zhiyong, Huang Yinglong, Fu Shi, Li Haihao, Gong Chen, Lv Dihao, Yang Chadanfeng, Wang Jiansong, Ding Mingxia, Wang Haifeng
Department of Urology, The Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, People's Republic of China.
Urological Disease Clinical Medical Center of Yunnan Province, The Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, People's Republic of China.
Ann Med. 2025 Dec;57(1):2553220. doi: 10.1080/07853890.2025.2553220. Epub 2025 Sep 5.
Bladder cancer (BLCA) is a prevalent malignancy with substantial consequences for patient health. This study aimed to elucidate the underlying mechanisms of BLCA through integrated multi-omics analysis.
Tumor and adjacent tissues from BLCA patients underwent transcriptomic, whole-exome sequencing, metabolomic, and intratumoral microbiome analyses. These data were integrated with public datasets to identify key genes, metabolites, and microorganisms. Molecular subtypes were defined by key gene expression and compared for pathways, immune profiles, mutations, immunotherapy response, and drug sensitivity. Prognostic relevance was validated in external cohorts. Single-cell sequencing was applied to reveal cellular localization of key genes.
Three key genes (), 90 metabolites, and two microbes () were identified. Key genes negatively correlated with metabolites but not with microbes. BLCA samples were classified into two molecular clusters with distinct ECM organization, metabolic features, immune checkpoint expression, and therapeutic sensitivity. NCAM1 correlated positively with γδ T cells and negatively with M0 macrophages. Single-cell analysis revealed nine major cell types, with fibroblasts displaying the highest expression of key genes, particularly elevated in specific fibroblast subtypes. Drug prediction and docking identified candidate compounds targeting these genes with stable binding potential.
This comprehensive multi-omics analysis links key genes, metabolites, and microbes to BLCA pathogenesis. Fibroblasts emerge as central regulators, while identified gene-metabolite interactions and microbial associations provide novel insights into tumor heterogeneity. These findings highlight potential biomarkers and therapeutic targets to support precision treatment in BLCA.
膀胱癌（BLCA）是一种常见的恶性肿瘤，对患者健康有重大影响。本研究旨在通过综合多组学分析阐明BLCA的潜在机制。
对BLCA患者的肿瘤组织和癌旁组织进行转录组学、全外显子测序、代谢组学和肿瘤内微生物组分析。将这些数据与公共数据集整合，以识别关键基因、代谢物和微生物。通过关键基因表达定义分子亚型，并比较其通路、免疫谱、突变、免疫治疗反应和药物敏感性。在外部队列中验证预后相关性。应用单细胞测序揭示关键基因的细胞定位。
鉴定出三个关键基因、90种代谢物和两种微生物。关键基因与代谢物呈负相关，但与微生物无相关性。BLCA样本被分为两个分子簇，具有不同的细胞外基质组织、代谢特征、免疫检查点表达和治疗敏感性。NCAM1与γδT细胞呈正相关，与M0巨噬细胞呈负相关。单细胞分析揭示了九种主要细胞类型，其中成纤维细胞显示关键基因的表达最高，特别是在特定的成纤维细胞亚型中升高。药物预测和对接确定了靶向这些基因且具有稳定结合潜力的候选化合物。
这种全面的多组学分析将关键基因、代谢物和微生物与BLCA发病机制联系起来。成纤维细胞成为核心调节因子，而鉴定出的基因-代谢物相互作用和微生物关联为肿瘤异质性提供了新的见解。这些发现突出了潜在的生物标志物和治疗靶点，以支持BLCA的精准治疗。
