Prognostic feature based on androgen-responsive genes in bladder cancer and screening for potential targeted drugs.

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Jiang Zhao, Qian Zhang, Cunle Zhu, Wu Yuqi, Guohui Zhang, Qianliang Wang, Xingyou Dong, Benyi Li, Xiangwei Wang
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引用次数: 0

Abstract

Objectives: Bladder cancer (BLCA) is a tumor that affects men more than women. The biological function and prognostic value of androgen-responsive genes (ARGs) in BLCA are currently unknown. To address this, we established an androgen signature to determine the prognosis of BLCA.

Methods: Sequencing data for BLCA from the TCGA and GEO datasets were used for research. The tumor microenvironment (TME) was measured using Cibersort and ssGSEA. Prognosis-related genes were identified and a risk score model was constructed using univariate Cox regression, LASSO regression, and multivariate Cox regression. Drug sensitivity analysis was performed using Genomics of drug sensitivity in cancer (GDSC). Real-time quantitative PCR was performed to assess the expression of representative genes in clinical samples.

Results: ARGs (especially the CDK6, FADS1, PGM3, SCD, PTK2B, and TPD52) might regulate the progression of BLCA. The different expression patterns of ARGs may lead to different immune cell infiltration. The risk model indicates that patients with higher risk scores have a poorer prognosis, more stromal infiltration, and an enrichment of biological functions. Single-cell RNA analysis, bulk RNA data, and PCR analysis support the reliability of this risk model, and a nomogram was also established for clinical use. Drug prediction analysis showed that high-risk patients had a better response to fludarabine, AZD8186, and carmustine.

Conclusion: ARGs played an important role in the progression, immune infiltration, and prognosis of BLCA. The ARGs model has high accuracy in predicting the prognosis of BLCA patients and provides more effective medication guidelines.

基于雄激素反应基因的膀胱癌预后特征及潜在靶向药物筛选。
目的:膀胱癌(BLCA)是一种男性多于女性的肿瘤。雄激素反应基因(ARGs)在BLCA中的生物学功能和预后价值目前尚不清楚。为了解决这个问题,我们建立了雄激素标记来确定BLCA的预后。方法:利用TCGA和GEO数据集的BLCA测序数据进行研究。采用Cibersort和ssGSEA检测肿瘤微环境(TME)。鉴定预后相关基因,并采用单因素Cox回归、LASSO回归和多因素Cox回归构建风险评分模型。采用肿瘤药物敏感性基因组学(GDSC)进行药物敏感性分析。采用实时荧光定量PCR检测临床样品中代表性基因的表达情况。结果:ARGs(特别是CDK6、FADS1、PGM3、SCD、PTK2B和TPD52)可能调节BLCA的进展。不同的ARGs表达模式可能导致不同的免疫细胞浸润。风险模型提示,风险评分越高的患者预后越差,间质浸润越多,生物功能越丰富。单细胞RNA分析、大量RNA数据和PCR分析支持该风险模型的可靠性,并建立了用于临床的nomogram。药物预测分析显示,高危患者对氟达拉滨、AZD8186和卡莫司汀的反应较好。结论:ARGs在BLCA的进展、免疫浸润及预后中起重要作用。ARGs模型在预测BLCA患者预后方面具有较高的准确性,为BLCA患者提供更有效的用药指导。
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来源期刊
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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