Background: Long non-coding RNAs (lncRNAs) participate in the regulation of genomic stability. Understanding their biological functions can help us identify the mechanisms of the occurrence and progression of cancers and can provide theoretical guidance and the basis for treatment.
Results: Based on the mutation hypothesis, we proposed a computational framework to identify genomic instability-related lncRNAs. Based on the differentially-expressed lncRNAs (DElncRNAs), we constructed a genomic instability-derived lncRNA signature (GILncSig) to calculate and stratify outcomes in patients with prostate cancer. It is an independent predictor of overall survival. The area under the curve = 0.805. This value may be more significant than the classic prognostic markers TP53 and Speckle-type POZ protein (SPOP) in terms of outcome prediction.
Conclusions: In summary, we conducted a computation approach and resource for mining genome instability-related lncRNAs. It may turn out to be highly significant for genomic instability and customized decision-making for patients with prostate cancer. It also may lead to effective methods and resources to study the molecular mechanism of genomic instability-related lncRNAs.
Keywords: Genomic stability; Long non-coding RNAs; Prostate cancer.
Copyright © 2021 Elsevier Inc. All rights reserved.