Background: Accumulating research reports have indicated that long non-coding RNAs (lncRNAs) are abnormally expressed in many types of cancers. However, few lncRNA signatures for predicting cancer prognosis have been established. Our goal is to establish a lncRNA signature for predicting the prognosis of clear cell renal cell carcinoma (ccRCC).

Methods: We downloaded KIRC lncRNA FPKM (Fragments Per Kilobase of transcript per Million Fragments) standardized expression data from The Cancer Genome Atlas (TCGA) by using the TANRIC tool. We established an 11-lncRNA signature that was clearly linked to the overall survival (OS) rates in the training and test sets.

Results: The training set was divided into the high-risk and low-risk subgroups, between which the OS was disparate (HR = 1.51, 95%CI = 1.39-1.64, P < 0.0001). The accuracy of the 11-lncRNA signature for predicting prognosis was confirmed in the test set. Further analysis revealed that the prognostic value of this signature was independent of the neoplasm grade and TNM stage. Gene set enrichment analysis (GSEA) was performed, and a summary of 4 gene sets related to canonical pathway, biological process, molecular function and cellular component was obtained. We demonstrated the biological function of these lncRNAs in ccRCC cell lines and found that LINC00488 and HOTTIP promoted tumour proliferation and inhibited apoptosis. However, LINC-PINT had the opposite effect.

Conclusions: The establishment of the 11-lncRNA signature indicated the underlying biochemical functional roles of the selected lncRNAs in ccRCC. Our results may provide a reliable theoretical basis for clinical evaluation of ccRCC prognosis.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.biopha.2019.109079DOI Listing

Publication Analysis

Top Keywords

long non-coding
8
clear cell
8
cell renal
8
renal cell
8
cell carcinoma
8
non-coding rna
4
rna signature
4
signature improve
4
improve prognostic
4
prognostic prediction
4

Similar Publications

A systems medicine understanding of the regulatory molecular circuits that underpin breast cancer is essential for early cancer detection and precision/personalized medicine in clinical oncology. Transcription factors (TFs), microRNAs (miRNAs), and long non-coding RNAs (lncRNAs) control gene expression and cell biology, and by extension, serve as pillars of the regulatory circuits that determine human health and disease. We report here the development of a regulatory circuit analysis program, , constructing 10 different types of regulatory elements involving messenger RNA, miRNA, lncRNA, and TFs.

View Article and Find Full Text PDF

Diabetic nephropathy (DN) affects about one-third of patients with diabetes and can lead to end-stage renal disease despite numerous trials aimed at improving diabetic management. Non-coding RNAs (ncRNAs) represent a new frontier in DN research, as increasing evidence suggests their involvement in the occurrence and progression of DN. A growing body of evidence suggests that long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) in DN signaling pathways might serve as novel biomarkers or therapeutic targets, although this remains to be fully explored.

View Article and Find Full Text PDF

C.A. Meyer is a perennial herb that is used worldwide for a number of medical purposes.

View Article and Find Full Text PDF

Meis1 is a transcription factor involved in numerous functions including development and proliferation and has been previously shown to harness cell cycle progression. In this study, we used in silico analysis to predict that miR-499-5p targets Meis1 and that Malat1 sponges miR-499-5p. For the first time, we demonstrated that the overexpression of miR-499-5p led to the downregulation of Meis1 mRNA and protein in C166 cells by directly binding to its 3'UTR.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!