Research Paper Volume 13, Issue 10 pp 13876—13897

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

Zhou-Tong Dai1, *, , Yuan Xiang1,2, *, , Yundan Wang1, *, , Le-Yuan Bao1, , Jun Wang1, , Jia-Peng Li1, , Hui-Min Zhang1, , Zhongxin Lu2, &, , Sreenivasan Ponnambalam3, , Xing-Hua Liao1, ,

  • 1 Institute of Biology and Medicine, College of Life and Health Sciences, Wuhan University of Science and Technology, Wuhan 430081, Hubei, P.R. China
  • 2 Department of Medical Laboratory, Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430014, Hubei, P.R. China
  • 3 School of Molecular and Cellular Biology, University of Leeds, Leeds LS2 9JT, United Kingdom
* Equal contribution

Received: June 10, 2020       Accepted: March 26, 2021       Published: May 7, 2021
How to Cite

Copyright: © 2021 Dai et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


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.


OS: Overall survival; DFS: Disease-free survival; KEGG: Kyoto Encyclopedia of Genes and Genomes; GSEA: Gene Set Enrichment Analysis; GO: Gene Ontology; LUAD: lung adenocarcinoma.