Affiliation:
1. Department of Surgery, the First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
2. Department of Urology, Urology Research Institute, the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
Abstract
Background:
Emerging evidence indicate that long noncoding RNA (lncRNA) plays an important biological role in clear cell renal cell carcinoma (ccRCC), however the clinical value of tumor mutation burden related lncRNA in ccRCC patients is unknown yet.
Method:
Somatic mutation profiles and lncRNA expression data of ccRCC was downloaded from TCGA database. We retrospectively analyzed lncRNA expression data and survival information from 116 patients with ccRCC between January 2013 to January 2014. Univariate and multivariate Cox regression analysis were performed to construct lncRNA signature, and the prognosis value was determined by Kaplan-Mayer and receiver operating characteristic curve (ROC) analysis.
Results:
Based on 160 differentially expressed TMB-related lncRNAs, two TMB-related molecular clusters with distinct immune checkpoints expression and immune cells infiltration were established for ccRCC patients. Moreover, a novel TMB-related lncRNA signature was constructed based on five lncRNAs for individualized prognosis assessment. High-risk group represents significantly worse overall survival in all cohorts. The area under ROC curve were 0.716, 0.775 and 0.744 in training cohort, testing cohort and TCGA cohort. Results of qRT-PCR successfully validated the expression levels of AP002360.3, LINC00460, AL590094.1, LINC00944 and LINC01843 in HK-2, 786-O, 769-P and ACHN cells. More importantly, the predictive performance of TMB-related lncRNA signature was successfully validated in an independent cohort of 116 ccRCC patients at our institution.
Conclusion:
This study successfully developed and validated a novel TMB-related lncRNA signature for individualized prognosis assessment of ccRCC patients.
Publisher
Bentham Science Publishers Ltd.
Subject
Organic Chemistry,Computer Science Applications,Drug Discovery,General Medicine