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综合分析揭示表观遗传调控因子在膀胱癌中的预后作用。

Integrative Analysis Reveals the Prognostic Effects of Epigenetic Regulators in Bladder Cancer.

作者信息

Mekala Venugopalareddy, Lin Yupei, Wang Xiang, Chowdhury Naail, Li Jianrong, Cheng Chao

机构信息

Department of Medicine, Baylor College of Medicine, Houston, Texas, USA.

Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA.

出版信息

Cancer Med. 2025 Jul;14(14):e71057. doi: 10.1002/cam4.71057.

DOI:10.1002/cam4.71057
PMID:40693457
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12281021/
Abstract

BACKGROUND

Epigenetic regulatory genes (epiRG) are pivotal in the epigenetic regulation of the human genome, primarily through DNA and histone modifications. These genes are frequently mutated in human cancers, particularly bladder cancer (BC). However, the functional impact of epiRG mutations on patient outcomes remains poorly understood.

METHODS

In this study, we developed gene signatures for the most frequent genomic aberrations of epiRG using The Cancer Genome Atlas Bladder Carcinoma (TCGA-BLCA) dataset and validated these signatures with independent tumor expression profiles for prognostic relevance. Furthermore, we evaluated the role of these signature scores in the immune system within the tumor microenvironment (TME). Finally, we assessed the correlation between epiRG and global DNA methylation.

RESULTS

Our results indicated that the inferred aberration-specific signature scores were more predictive of patient stratification than the genomic aberrations. Notably, certain signature scores were significantly associated with patient progression, whereas others correlated with the tumor immune microenvironment via interactions with the immune system. Patients with mutations had high signature scores in CREBBP-mut and EP300-mut, which revealed poor overall survival. Conversely, KDM6A-mut signatures showed an opposite trend, with low scores linking to favorable prognosis through enhanced immune activity. Also, other epiRG signature scores were strongly correlated with the immune system in TME and successfully predicted patients who responded to immunotherapy. Global methylation analysis revealed that high signature scores of KDM6A-mut are associated with hypomethylation.

CONCLUSIONS

These findings collectively establish epiRG signature scores as powerful biomarkers that integrate genomic, epigenetic, and immune microenvironment features for improved prognostic prediction in bladder cancer. This integrative approach not only advances our understanding of epigenetic mechanisms in BC but also offers potential for developing innovative prognostic tools and therapeutic strategies tailored to personalized medicine.

摘要

背景

表观遗传调控基因(epiRG)在人类基因组的表观遗传调控中起关键作用,主要通过DNA和组蛋白修饰。这些基因在人类癌症中经常发生突变,尤其是膀胱癌(BC)。然而,epiRG突变对患者预后的功能影响仍知之甚少。

方法

在本研究中,我们使用癌症基因组图谱膀胱癌(TCGA-BLCA)数据集开发了epiRG最常见基因组畸变的基因特征,并通过独立的肿瘤表达谱验证这些特征与预后的相关性。此外,我们评估了这些特征分数在肿瘤微环境(TME)免疫系统中的作用。最后,我们评估了epiRG与全基因组DNA甲基化之间的相关性。

结果

我们的结果表明,推断的畸变特异性特征分数比基因组畸变更能预测患者分层。值得注意的是,某些特征分数与患者进展显著相关,而其他特征分数则通过与免疫系统的相互作用与肿瘤免疫微环境相关。突变患者在CREBBP突变和EP300突变中具有高特征分数,这表明总体生存率较差。相反,KDM6A突变特征显示出相反的趋势,低分数通过增强免疫活性与良好预后相关。此外,其他epiRG特征分数与TME中的免疫系统密切相关,并成功预测了对免疫治疗有反应的患者。全基因组甲基化分析显示,KDM6A突变的高特征分数与低甲基化有关。

结论

这些发现共同确立了epiRG特征分数作为强大的生物标志物,整合了基因组、表观遗传和免疫微环境特征,以改善膀胱癌的预后预测。这种综合方法不仅推进了我们对BC表观遗传机制的理解,还为开发针对个性化医疗的创新预后工具和治疗策略提供了潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/100f/12281021/50f7e24d4b53/CAM4-14-e71057-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/100f/12281021/c973c12ef0ea/CAM4-14-e71057-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/100f/12281021/b42281881cbf/CAM4-14-e71057-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/100f/12281021/a67404b34fb3/CAM4-14-e71057-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/100f/12281021/304aedc0a535/CAM4-14-e71057-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/100f/12281021/92f1a977f817/CAM4-14-e71057-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/100f/12281021/50f7e24d4b53/CAM4-14-e71057-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/100f/12281021/c973c12ef0ea/CAM4-14-e71057-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/100f/12281021/b42281881cbf/CAM4-14-e71057-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/100f/12281021/a67404b34fb3/CAM4-14-e71057-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/100f/12281021/304aedc0a535/CAM4-14-e71057-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/100f/12281021/92f1a977f817/CAM4-14-e71057-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/100f/12281021/50f7e24d4b53/CAM4-14-e71057-g001.jpg

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