A prognostic index based on a fourteen long non-coding RNA signature to predict the recurrence-free survival for muscle-
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RESEARCH
Open Access
A prognostic index based on a fourteen long non-coding RNA signature to predict the recurrence-free survival for muscleinvasive bladder cancer patients Xiaolong Zhang1,2,3†, Meng Zhang3,4†, Xuanping Zhang1, Xiaoyan Zhu1 and Jiayin Wang1* From 5th China Health Information Processing Conference Guangzhou, China. 22-24 November 2019
Abstract Background: Bladder cancer (BC) is regarded as one of the most fatal cancer around the world. Nevertheless, there still lack of sufficient markers to predict the prognosis of BC patients. Herein, we aim to establish a prognosis predicting signature based on long-noncoding RNA (lncRNA) for the invasive BC patients. Methods: The lncRNA expression profile was downloaded from The Cancer Genome Atlas (TCGA) database, along with the correlated clinicopathological information. The univariate Cox regression test was employed to screen out the recurrence-free survival (RFS)-related lncRNAs. Then, the LASSO method was conducted to construct the signature based on these RFS-related lncRNA candidates. Genes correlated with these fourteen lncRNAs were extracted from the mRNA expression profile, with the Pearson correlation coefficient > 0.60 or < − 0.40. Subsequently, the Proteomap pathway enrichment analyses were conducted to classify the function of these correlated genes. Furthermore, the multivariate analyses were executed to reveal the independent role of the proposed signature with the clinicopathological features. (Continued on next page)
* Correspondence: [email protected] † Xiaolong Zhang and Meng Zhang contributed equally to this work. 1 School of Computer Science and Technology, Shaanxi Engineering Research Center of Medical and Health Big Data, Xi’an Jiaotong University, Xi’an, China Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Zhang et al. BMC Medical Informatics and Decision Making 2020, 20(Suppl 3):136
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