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锌指蛋白基因特征可实现膀胱癌治疗分层。

A zinc finger protein gene signature enables bladder cancer treatment stratification.

机构信息

Beijing Chaoyang Hospital Affiliated Capital Medical University, Beijing 100020, China.

Shanxi Bethune Hospital Affiliated Shanxi Academy of Medical Sciences, Taiyuan 030032, China.

出版信息

Aging (Albany NY). 2021 May 7;13(9):13023-13038. doi: 10.18632/aging.202984.

DOI:10.18632/aging.202984
PMID:33962398
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8148496/
Abstract

Bladder cancer (BC) is a commonly occurring malignant tumor affecting the urinary tract. Zinc finger proteins (ZNFs) constitute the largest transcription factor family in the human genome and are therefore attractive biomarker candidates for BC prognosis. In this study, we profiled the expression of ZNFs in The Cancer Genome Atlas (TCGA) BC cohort and developed a novel prognostic signature based on 7 ZNF-coding genes. After external validation of the model in the GSE48276 dataset, we integrated the 7-ZNF-gene signature with patient clinicopathological data to construct a nomogram that forecasted 1-, 2-, and 3-year OS with good predictive accuracy. We then accessed The Genomics of Drug Sensitivity in Cancer database to predict the therapeutic drug responses of signature-defined high- and low-risk BC patients in the TCGA cohort. Greater sensitivity to chemotherapy was revealed in the low-risk group. Finally, we conducted gene set enrichment analysis of the signature genes and established, by applying the ESTIMATE algorithm, distinct correlations between the two risk groups and the presence of stromal and immune cell types in the tumor microenvironment. By allowing effective risk stratification of BC patients, our novel ZNF gene signature may enable tailoring more intensive treatment for high-risk patients.

摘要

膀胱癌(BC)是一种常见的影响尿路的恶性肿瘤。锌指蛋白(ZNFs)构成了人类基因组中最大的转录因子家族,因此是 BC 预后的有吸引力的生物标志物候选者。在本研究中,我们对癌症基因组图谱(TCGA)BC 队列中的 ZNFs 表达进行了分析,并基于 7 个 ZNF 编码基因开发了一个新的预后签名。在 GSE48276 数据集对模型进行外部验证后,我们将 7-ZNF 基因签名与患者临床病理数据相结合,构建了一个列线图,可准确预测 1、2 和 3 年的 OS。然后,我们访问了癌症药物敏感性基因组学数据库,以预测 TCGA 队列中签名定义的高风险和低风险 BC 患者的治疗药物反应。低风险组显示出对化疗的更高敏感性。最后,我们对签名基因进行了基因集富集分析,并通过应用 ESTIMATE 算法,在两个风险组之间以及肿瘤微环境中基质和免疫细胞类型的存在之间建立了明确的相关性。通过有效对 BC 患者进行风险分层,我们的新型 ZNF 基因签名可以为高危患者提供更强化的治疗。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b351/8148496/4094738440ac/aging-13-202984-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b351/8148496/418bc213892c/aging-13-202984-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b351/8148496/3b4f7e7917f3/aging-13-202984-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b351/8148496/bcefe4da2e21/aging-13-202984-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b351/8148496/399535d060d0/aging-13-202984-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b351/8148496/0c1c166567b5/aging-13-202984-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b351/8148496/4d0e3161f689/aging-13-202984-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b351/8148496/cb01b80d12d9/aging-13-202984-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b351/8148496/f2ab01e152af/aging-13-202984-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b351/8148496/4094738440ac/aging-13-202984-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b351/8148496/418bc213892c/aging-13-202984-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b351/8148496/c6f0b7bdb0c7/aging-13-202984-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b351/8148496/3b4f7e7917f3/aging-13-202984-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b351/8148496/bcefe4da2e21/aging-13-202984-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b351/8148496/399535d060d0/aging-13-202984-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b351/8148496/0c1c166567b5/aging-13-202984-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b351/8148496/4d0e3161f689/aging-13-202984-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b351/8148496/cb01b80d12d9/aging-13-202984-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b351/8148496/f2ab01e152af/aging-13-202984-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b351/8148496/4094738440ac/aging-13-202984-g010.jpg

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