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NFAT 家族成员对泛癌的预后价值及基于 NFAT2 在膀胱癌中的预测模型。

Prognostic value of members of NFAT family for pan-cancer and a prediction model based on NFAT2 in bladder cancer.

机构信息

Institute of Biology and Medicine, College of Life and Health Sciences, Wuhan University of Science and Technology, Wuhan 430081, Hubei, P.R. China.

Department of Medical Laboratory, Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430014, Hubei, P.R. China.

出版信息

Aging (Albany NY). 2021 May 7;13(10):13876-13897. doi: 10.18632/aging.202982.

Abstract

Bladder cancer (BLCA) is one of the common malignant tumors of the urinary system. The poor prognosis of BLCA patients is due to the lack of early diagnosis and disease recurrence after treatment. Increasing evidence suggests that gene products of the nuclear factor of activated T-cells (NFAT) family are involved in BLCA progression and subsequent interaction(s) with immune surveillance. In this study, we carried out a pan-cancer analysis of the NFAT family and found that NFAT2 is an independent prognostic factor for BLCA. We then screened for differentially expressed genes (DEGs) and further analyzed such candidate gene loci using gene ontology enrichment to curate the KEGG database. We then used Lasso and multivariate Cox regression to identify 4 gene loci (FER1L4, RNF128, EPHB6, and FN1) which were screened together with NFAT2 to construct a prognostic model based on using Kaplan-Meier analysis to predict the overall survival of BLCA patients. Moreover, the accuracy of our proposed model is supported by deposited datasets in the Gene Expression Omnibus (GEO) database. Finally, a nomogram of this prognosis model for BLCA was established which could help to provide better disease management and treatment.

摘要

膀胱癌(BLCA)是泌尿系统常见的恶性肿瘤之一。BLCA 患者预后不良的原因是缺乏早期诊断和治疗后疾病复发。越来越多的证据表明,激活 T 细胞核因子(NFAT)家族的基因产物参与了 BLCA 的进展,并与免疫监视相互作用。在这项研究中,我们对 NFAT 家族进行了泛癌症分析,发现 NFAT2 是 BLCA 的一个独立预后因素。然后,我们筛选了差异表达基因(DEGs),并进一步使用基因本体论富集分析候选基因座,以整理 KEGG 数据库。然后,我们使用 Lasso 和多变量 Cox 回归来识别 4 个基因座(FER1L4、RNF128、EPHB6 和 FN1),这些基因座与 NFAT2 一起筛选,以构建基于 Kaplan-Meier 分析的预后模型,预测 BLCA 患者的总生存率。此外,我们提出的模型的准确性得到了 Gene Expression Omnibus(GEO)数据库中已存储数据集的支持。最后,建立了该预后模型的 BLCA 列线图,有助于提供更好的疾病管理和治疗。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e6c/8202856/fb86ba7d594a/aging-13-202982-g001.jpg

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