Establishing a Prognostic Model Based on Three Genomic Instability-related LncRNAs for Clear Cell Renal Cell Cancer.

BACKGROUND

Among all types of renal cell cancer (RCC), clear cell renal cell cancer (ccRCC) is the most common and aggressive one. Emerging evidence uncovers that long non-coding RNAs (lncRNAs) are involved in genomic instability, which correlates to the clinical outcomes of patients who suffer from various kinds of cancers.

METHODS

We gathered expression profiles of transcriptome RNA and clinical information about ccRCC from The Cancer Genome Atlas (TCGA) and The Gene Expression Omnibus (GEO) database. The lncRNA expression profiles and somatic mutation data were combined to identify genome instability-related lncRNAs (GILncRs) by significance analysis of T test. By means of univariate and multivariate cox regression analyses, 3 GILncRs strongly associated with patient prognosis were screened out to build a genomic instability-related risk score (GIRS) model. We use R-version 4.0.4 to draw Kaplan-Meier plots and ROC curves for survival prediction.

RESULTS

The somatic mutation count was higher in genomic unstable group. PBRM1 showed lower expression in genomic unstable group. Three lncRNAs such as LINC00460, AC156455.1, LINC01606 were included in the GIRS model. Patients had poorer prognosis with higher risk score of GIRS model. The somatic mutation count was higher in patients with higher risk score while PBRM1 expression was lower. The GIRS model was independent from other clinical factors. The GIRS model was superior to other 2 published lncRNA signatures in survival prediction.