Calculated identification of mutator-derived lncRNA signatures of genomic instability to predict the clinical outcome of muscle-invasive bladder cancer

Abstract

Abstract Background Muscle-invasive bladder cancer (MIBC) is one of the most important type of bladder cancer, with a high morbidity and mortality rate. Studies have found that long non-coding RNA (lncRNA) plays a key role in maintaining genomic instability. However, Identification of lncRNAs related to genomic instability (GIlncRNAs) and their clinical significance in cancers have not been extensively studied yet. Methods Here, we downloaded the lncRNA expression profiles, somatic mutation profiles and clinical related data in MIBC patients from The Cancer Genome Atlas (TCGA) database. A lncRNA computational framework was used to find differentially expressed GIlncRNAs. Multivariate Cox regression analysis was used to construct a genomic instability-related lncRNA signature (GIlncSig). Univariate and multivariate Cox analyses were used to assess the independent prognostic for the GIlncSig and other key clinical factors. Results We found 43 differentially expressed GIlncRNAs and constructed the GIlncSig with 6 GIlncRNAs in the training cohort. The patients were divided into two risk groups. The overall survival of patients in the high-risk group was lower than that in the low-risk group (P < 0.001), which were further verified in the testing cohort and the entire TCGA cohort. Univariate and multivariate Cox regression showed that the GIlncSig was an independent prognostic factor. In addition, the GIlncSig correlated with the genomic mutation rate of MIBC, indicating its potential as a measure of the degree of genomic instability. The GIlncSig was able to divide FGFR3 wild- and mutant-type patients into two risk groups, and effectively enhanced the prediction effect. Conclusion Our study introduced an important reference for further research on the role of GIlncRNAs, and provided prognostic indicators and potential biological therapy targets for MIBC.