岭南现代临床外科2025，Vol.25Issue(2)：91-100,10.DOI:10.3969/j.issn.1009-976X.2025.02.003
基于机器学习构建中性粒细胞胞外诱捕网基因评分预测非肌层浸润性膀胱癌复发的临床价值
Machine learning-based NETs gene signature predicts recurrence in non-muscle-invasive bladder cancer
摘要
Abstract
Objective Neutrophil extracellular traps(NETs)can be stimulated by various factors,including drug perfusion and tumor cell stimulation,thereby influencing the prognosis of cancer patients.However,the prognostic impact and key functional genes of NETs in the recurrence of non-muscle-inva-sive bladder cancer(NMIBC)remain unclear.This study aims to identify critical NETs-related genes associated with NMIBC recurrence and provide a reliable predictive tool for clinical recurrence assessment.Methods Transcriptomic data and clinical information from bladder cancer patients were obtained from the GEO database(GSE13507,GSE128959,GSE19423,GSE154261,GSE31684,GSE169455),and somatic copy number variation(CNV)data were retrieved from TCGA.Using machine learning algorithms and weighted gene co-expression network analysis(WGCNA),we identified 153 NETs-related genes and constructed a recurrence prediction score(NRG),which was validated in a training cohort.We compre-hensively analyzed the impact of this score on gene expression,the immune microenvironment,and func-tional pathways in bladder cancer.Additionally,we explored potential sensitivities to NRG-associated small-molecule compounds to identify therapeutic targets for clinical intervention.Results This study identified three NETs-related genes(G0S2,CCL5,and CLEC7A)as independent prognostic predictors for postoperative recurrence in NMIBC patients.The NRG score effectively predicted recurrence outcomes in the training cohort,demonstrating diagnostic AUC values of 0.671 and 0.645 in two independent NMIBC datasets,with significant prognostic stratification(P=0.039).Genomic and immune infiltration analyses revealed that high-NRG patients exhibited more frequent PIK3CA mutations and increased infiltration of immunosuppressive cell subsets.Functional enrichment indicated hyperactivation of immune checkpoint pathways in high-NRG cases.Drug sensitivity analysis suggested that targeting NRG may reduce recur-rence risk by inhibiting the PI3K-mTOR and ERK signaling axes,providing potential therapeutic strate-gies for NMIBC.Conclusion This study established a NETs-derived recurrence prediction signature(NRG)for NMIBC and elucidated its immunomodulatory effects within the tumor microenvironment,functional pathway alterations,and potential small-molecule therapeutic targets.关键词
膀胱癌/机器学习/中性粒细胞胞外诱捕网/药物靶点Key words
bladder cancer/machine learning/neutrophil extracellular traps/therapeutic targets分类
临床医学引用本文复制引用
黄孝东,王博,黄健..基于机器学习构建中性粒细胞胞外诱捕网基因评分预测非肌层浸润性膀胱癌复发的临床价值[J].岭南现代临床外科,2025,25(2):91-100,10.基金项目
国家自然科学基金(82173230) （82173230）
