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Research Paper|Volume 12, Issue 3|pp 2857—2879

A nomogram combining long non-coding RNA expression profiles and clinical factors predicts survival in patients with bladder cancer

Yifan Wang1, Lutao Du1,2, Xuemei Yang1, Juan Li1, Peilong Li1, Yinghui Zhao1, Weili Duan1, Yingjie Chen1, Yunshan Wang1, Haiting Mao1, Chuanxin Wang1,3,4
  • 1Department of Clinical Laboratory, The Second Hospital of Shandong University, Jinan, Shandong, China
  • 2Tumor Marker Detection Engineering Technology Research Center of Shandong Province, Jinan, Shandong, China
  • 3Tumor Marker Detection Engineering Laboratory of Shandong Province, Jinan, Shandong, China
  • 4The Clinical Research Center of Shandong Province for Clinical Laboratory, Jinan, Shandong, China
* Equal contribution
Received: September 3, 2019Accepted: January 19, 2020Published: February 12, 2020
https://doi.org/10.18632/aging.102782

Copyright: © 2020 Wang 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.

Abstract

Bladder cancer (BCa) is a heterogeneous disease with various tumorigenic mechanisms and clinical behaviors. The current tumor-node-metastasis (TNM) staging system is inadequate to predict overall survival (OS) in BCa patients. We developed a BCa-specific, long-non-coding-RNA (lncRNA)-based nomogram to improve survival prediction in BCa. We obtained the large-scale gene expression profiles of samples from 414 BCa patients in The Cancer Genome Atlas database. Using an lncRNA-mining computational framework, we identified three OS-related lncRNAs among 826 lncRNAs that were differentially expressed between BCa and normal samples. We then constructed a three-lncRNA signature, which efficiently distinguished high-risk from low-risk patients and was even viable in the TNM stage-II, TNM stage-III and ≥65-year-old subgroups (all P<0.05). Using clinical risk factors, we developed a signature-based nomogram, which performed better than the molecular signature or clinical factors alone for prognostic prediction. A bioinformatical analysis revealed that the three OS-related lncRNAs were co-expressed with genes involved in extracellular matrix organization. Functional assays demonstrated that RNF144A-AS1, one of the three OS-related lncRNAs, promoted BCa cell migration and invasion in vitro. Our three-lncRNA signature-based nomogram effectively predicts the prognosis of BCa patients, and could potentially be used for individualized management of such patients.