基于36基因标签的非肌层浸润性膀胱癌复发预测模型及中国队列验证

Development of a 36-gene signature for predicting recurrence in non-muscle-invasive bladder cancer and validation in a Chinese cohort

摘要

目的：构建基于 RNA 表达的非肌层浸润性膀胱癌（non-muscle-invasive bladder cancer，NMIBC）的复发预测模型，在中国队列中验证其有效性，并探讨潜在的生物学机制。方法：基于欧洲尿路上皮癌分子分型联盟（Urothelial Cancer Molecular Classification Consortium，UROMOL）多中心队列（n=85）的转录组数据，采用最小绝对收缩和选择算子（least absolute shrinkage and selection operator，LASSO）Cox 回归筛选预后基因并构建风险评分模型，并根据评分将患者分为高风险组和低风险组。通过 Bootstrap 重抽样和10折交叉验证进行内部验证，在北京大学人民医院独立的中国队列（0.782、0.811、0.799）中进行外部验证。采用基因集富集分析（gene set enrichment analysis，GSEA）探索高风险组和低风险组的生物学差异。结果：从12170个基因中筛选出36个构建模型，训练队列中，模型一致性指数（concordance index，C-index）为0.702（95％CI：0.664~0.740），1、3、5年曲线下面积（area under curve，AUC）分别为 35.2% ；高风险组与低风险组5年无复发生存率相比，差异有统计学意义（72.8% vs. P<0.001 ，Log-rank 95%CI:0.631∼0.739）。内部验证显示，模型稳定性良好（C-index 分别为 0.702 和 0.708），在独立的中国验证队列中，模型的 C-index 为 0.685 （0.705、0.743、0.665），1、3、5 年的 AUC 分别为 =0.652 ，预测效能与欧洲癌症研究与治疗组织（European Organization for Research and Treatment of Cancer，EORTC）评分（C-index  ^Δ）相当。GSEA 分析显示，低风险组富集于能量代谢和蛋白质稳态通路，高风险组富集于细胞外基质（extracellular matrix， ECM）重塑和细胞黏附通路。结论：本研究构建的 36 基因模型在独立的中国队列中显示出稳定的预测效能，与经典 EORTC 系统的预测效能相当，并揭示了能量代谢和 ECM 重塑等关键通路在 NMIBC 复发中的作用，为中国患者的个体化治疗提供了分子依据。

Abstract

Objective：To develop a transcriptomic-based recurrence prediction model for non-muscle- invasive bladder cancer（NMIBC），to comprehensively validate its efficacy in an independent Chinese cohort，and to explore the underlying biological mechanisms driving tumor recurrence. Methods：Tran- scriptome profiling data from the multicenter European Urothelial Cancer Molecular Classification Consor- tium（UROMOL）cohort n=85 were used as the training cohort.The least absolute shrinkage and selection operator（LASSO）Cox regression was employed to identify prognostic genes and construct a risk score model.Internal validation was strictly performed using Bootstrap resampling and 10-fold cross- validation.External validation was conducted in an independent Chinese cohort（0.782,0.811）from Peking University People＇s Hospital.Gene set enrichment analysis（GSEA）was applied to elucidate the distinct biological differences between high-and low-risk groups. Results：A 36-gene prognostic signature was identified from 12170 genes to construct the risk score model.In the training cohort，the model achieved a concordance index（C-index）of 0.702 （95％CI：0.664-0.740），with 1-，3-，and 5-year areas un- der the receiver operating characteristic curve（AUC）of P<0.001 ，and 0.799 ，respectively.The 5-year recurrence-free survival（RFS）rates for the high-and low-risk groups were 35.2％and 72.8％，respectively（Log-rank (C）.Internal validation via Bootstrap（C-index=0.702）and 10-fold cross-validation =0.708)-index 95%CI:0.631-0.739 demonstrated robust stability.In the independent Chinese validation cohort，the model maintained a favorable C-index of 0.685 （0.705,0.743），with 1-，3-，and 5-year AUC of =0.652 ，and 0.665 ，respectively.Its predictive performance was comparable to the traditional European Organization for Research and Treatment of Cancer（EORTC）risk score（C- index α⁺）.GSEA revealed that the low-risk group was enriched in energy metabolism and protein homeostasis pathways，while the high-risk group was associated with extracellular matrix（ECM）remode- ling and cell adhesion pathways. Conclusion：The 36-gene signature developed in this study demon- strates favorable predictive performance and stability across both the training and independent Chinese co- horts.While exhibiting comparable predictive efficacy to the traditional EORTC scoring system，the model serves as an independent prognostic factor that provides crucial supplementary molecular stratifica- tion.Furthermore，the model underscores the critical roles of energy metabolism and ECM remodeling in NMIBC recurrence，providing a valuable molecular basis for precise individualized management of NMIBC patients in China.

关键词

非肌层浸润性膀胱癌 / 肿瘤复发 / 预测 / 基因标签 / 队列研究

Key words

Non-muscle-invasive bladder cancer / Neoplasm recurrence / Prediction / Gene signatures / Cohort studies

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基金资助

科技创新2030—＂癌症、心脑血管、呼吸和代谢性疾病防治研究＂国家科技重大专项(2024ZD0525700)

国家自然科学基金(82471866)

国家自然科学基金(82472912)

北京市自然科学基金(L252190)