背景:尽管免疫疗法在治疗膀胱癌(BLCA)方面显示出巨大的潜力,BLCA的总体预后和免疫治疗应答率仍不理想.
方法:我们通过分析210种糖基转移酶相关基因,对BLCA患者的糖基转移酶表达模式进行了广泛的评估。随后,我们建立了这些糖基转移酶模式之间的相关性,预后,和肿瘤微环境(TME)表型。为了提供个性化的患者评估,我们开发了一个准确预测预后的糖基转移酶风险评分,TME表型,和分子亚型。重要的是,我们开发了一个RNA-seq队列,命名为湘雅队列,来验证我们的结果。
结果:确定了两种不同的糖基转移酶表达模式,对应于发炎和非发炎的TME表型,并证明了预测预后的潜力。我们开发并验证了在TCGA-BLCA队列中准确预测个体患者预后的综合风险评分。此外,我们构建了一个列线图,将风险评分与几个关键临床因素相结合.重要的是,此风险评分已在外部队列中成功验证,包括湘雅队列和GSE48075。此外,在TCGA-BLCA和湘雅队列中,我们发现该风险评分与肿瘤浸润淋巴细胞呈正相关,提示风险评分较高的患者表现出发炎的TME表型,并且对免疫治疗的反应更敏感.最后,我们观察到高和低风险评分组与BLCA的腔和基底亚型一致,分别,根据分子亚型,进一步验证风险评分在TME中的作用。
结论:糖基转移酶模式在BLCA中表现出不同的TME表型。我们的综合风险评分为预后预测和评估免疫治疗疗效提供了一种有希望的方法。为精准医学提供有价值的指导。
BACKGROUND: Although immunotherapy shows tremendous potential in the treatment of bladder cancer (BLCA), the overall prognosis and response rates to immunotherapy in BLCA remain suboptimal.
METHODS: We performed an extensive evaluation of
glycosyltransferase expression patterns in BLCA patients by analyzing 210
glycosyltransferase-related genes. Subsequently, we established correlations between these
glycosyltransferase patterns, prognosis, and tumor microenvironment (TME) phenotypes. To offer personalized patient assessments, we developed a
glycosyltransferase risk score that accurately predicts prognosis, TME phenotypes, and molecular subtypes. Importantly, we developed a RNA-seq cohort, named Xiangya cohort, to validate our results.
RESULTS: Two distinct patterns of glycosyltransferase expression were identified, corresponding to inflamed and noninflamed TME phenotypes, and demonstrated the potential to predict prognosis. We developed and validated a comprehensive risk score that accurately predicted individual patient prognosis in the TCGA-BLCA cohort. Additionally, we constructed a nomogram that integrated the risk score with several key clinical factors. Importantly, this risk score was successfully validated in external cohorts, including the Xiangya cohort and GSE48075. Furthermore, we discovered a positive correlation between this risk score and tumor-infiltrating lymphocytes in both the TCGA-BLCA and Xiangya cohorts, suggesting that patients with a higher risk score exhibited an inflamed TME phenotype and were more responsive to immunotherapy. Finally, we observed that the high and low risk score groups were consistent with the luminal and basal subtypes of BLCA, respectively, providing further validation of the risk score\'s role in the TME in terms of molecular subtypes.
CONCLUSIONS: Glycosyltransferase patterns exhibit distinct TME phenotypes in BLCA. Our comprehensive risk score provides a promising approach for prognostic prediction and assessment of immunotherapy efficacy, offering valuable guidance for precision medicine.