Phosphorus Metabolism-Related Genes Serve as Novel Biomarkers for Predicting Prognosis in Bladder Cancer: A Bioinformatics Analysis.

IF 1.3 4区 医学 Q4 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Yang He, Abai Xu, Li Xiao, Ying Yang, Boping Li, Zhe Liu, Peng Rao, Yicheng Wang, Li Ruan, Tao Zhang
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引用次数: 0

Abstract

Background: Phosphorus metabolism might be associated with tumor initiation and progression. We aimed to screen out the phosphorus metabolism genes related to bladder cancer and construct a clinical prognosis model.

Methods: The dataset used for the analysis was obtained from TCGA database. GO and KEGG enrichment analyses were subsequently applied to differentially expressed genes. Consensus clustering was utilized, and different clusters of the tumor immune microenvironment and other features were compared. The phosphorus metabolism-related genes involved in prognosis were screened out by univariate Cox regression, LASSO regression and multivariate Cox regression analysis, and a nomogram was constructed. The performance of the nomogram was validated using TCGA dataset and the GEO dataset, respectively.

Results: Overall, 405 phosphorus metabolism-related differentially expressed genes from TCGA database were identified, which were associated with phosphorylation, cell proliferation, leukocyte activation, and signaling pathways. Two clusters were obtained by consistent clustering. After tumor immune microenvironment analysis, significant differences in immune cell infiltration between cluster 1 and cluster 2 were found. Four phosphorus metabolism-related genes (LIME1, LRP8, SPDYA, and MST1R) were associated with the prognosis of bladder cancer (BLCA) patients. We built a prognostic model and visualized the model as a nomogram. Calibration curves demonstrated the performance of this nomogram, in agreement with that shown by the ROC curves.

Conclusion: We successfully identified four phosphorus metabolism-related genes associated with prognosis, providing potential targets for biomarkers and therapeutics. A nomogram based on these genes was developed. Nevertheless, this study is based on bioinformatics, and experimental validation remains essential.

磷代谢相关基因是预测膀胱癌预后的新型生物标记物:生物信息学分析
背景:磷代谢可能与肿瘤的发生和发展有关。我们旨在筛选出与膀胱癌相关的磷代谢基因,并构建临床预后模型:方法:用于分析的数据集来自 TCGA 数据库。方法:用于分析的数据集来自 TCGA 数据库,随后对差异表达基因进行了 GO 和 KEGG 富集分析。利用共识聚类,比较了肿瘤免疫微环境和其他特征的不同聚类。通过单变量 Cox 回归、LASSO 回归和多变量 Cox 回归分析筛选出与预后有关的磷代谢相关基因,并构建了一个提名图。结果表明,该提名图的性能分别通过 TCGA 数据集和 GEO 数据集得到了验证:结果:总体而言,从TCGA数据库中发现了405个磷代谢相关的差异表达基因,这些基因与磷酸化、细胞增殖、白细胞活化和信号通路有关。通过一致聚类得到了两个集群。对肿瘤免疫微环境进行分析后发现,群组1和群组2在免疫细胞浸润方面存在显著差异。四个磷代谢相关基因(LIME1、LRP8、SPDYA和MST1R)与膀胱癌(BLCA)患者的预后相关。我们建立了一个预后模型,并将该模型可视化为一个提名图。校准曲线显示了该提名图的性能,与 ROC 曲线显示的性能一致:结论:我们成功鉴定了四个与预后相关的磷代谢相关基因,为生物标记物和疗法提供了潜在靶点。结论:我们成功鉴定了与预后相关的四个磷代谢相关基因,为生物标记物和治疗提供了潜在靶点,并根据这些基因绘制了一个提名图。尽管如此,这项研究仍以生物信息学为基础,实验验证仍然至关重要。
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来源期刊
Iranian Journal of Public Health
Iranian Journal of Public Health PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-
CiteScore
2.20
自引率
7.10%
发文量
300
审稿时长
3-8 weeks
期刊介绍: Iranian Journal of Public Health has been continuously published since 1971, as the only Journal in all health domains, with wide distribution (including WHO in Geneva and Cairo) in two languages (English and Persian). From 2001 issue, the Journal is published only in English language. During the last 41 years more than 2000 scientific research papers, results of health activities, surveys and services, have been published in this Journal. To meet the increasing demand of respected researchers, as of January 2012, the Journal is published monthly. I wish this will assist to promote the level of global knowledge. The main topics that the Journal would welcome are: Bioethics, Disaster and Health, Entomology, Epidemiology, Health and Environment, Health Economics, Health Services, Immunology, Medical Genetics, Mental Health, Microbiology, Nutrition and Food Safety, Occupational Health, Oral Health. We would be very delighted to receive your Original papers, Review Articles, Short communications, Case reports and Scientific Letters to the Editor on the above men­tioned research areas.
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