Integrated analysis of long noncoding RNA associated-competing endogenous RNA as prognostic biomarkers in clear cell renal carcinoma

Abstract

Clear cell renal cell carcinoma (ccRCC) is one of the most common malignant carcinomas and its molecular mechanisms remain unclear. Long noncoding RNA (lncRNA) could bind sites of miRNA which affect the expression of mRNA according to the competing endogenous (ceRNA) theory. The aim of the present study was to construct a ceRNA network and to identify key lncRNA to predict survival prognosis. We identified differentially expressed mRNA, lncRNA and miRNA between tumor tissues and normal tissues from The Cancer Genome Atlas database. Then, using bioinformatics tools, we explored the connection of 89 lncRNA, 10 miRNA and 22 mRNA, and we constructed the ceRNA network. Furthermore, we analyzed the functions and pathways of 22 differentially expressed mRNA. Then, univariate and multivariate Cox regression analyses of these 89 lncRNA and overall survival were explored. Nine lncRNA were finally screened out in the training group. The patients were divided into high-risk and low-risk groups according to the 9 lncRNA and low-risk scores having better clinical overall survival (P < .01). Furthermore, the receiver operating characteristic curve demonstrates the predicted role of the 9 lncRNA. The 9-lncRNA signature was successfully proved in the testing group and the entire group. Finally, multivariate Cox regression analysis and stratification analysis further proved that the 9-lncRNA signature was an independent factor to predict survival. In summary, the present study provides a deeper understanding of the lncRNA-related ceRNA network in ccRCC and suggests that the 9-lncRNA signature could serve as an independent biomarker to predict survival in ccRCC patients.

Keywords: The Cancer Genome Atlas; biomarker; competing endogenous RNA network; long non-coding RNA; renal cell carcinoma.

Figure 1

Heatmap and volcano map of the differential expression of genes in clear cell renal cell carcinoma (ccRCC) between 519 tumor tissues and 72 normal tissues. Ascending normalized expression level is colored from green to red. A, mRNA; B, lncRNA; C, miRNA

Figure 2

The 22 target DEmRNA that were also involved in the 2331 different mRNA were enrolled in the ceRNA network

Figure 3

The lncRNA‐miRNA‐mRNA ceRNA network. The blue diamonds are downregulated lncRNA and the red diamonds are upregulated lncRNA. The blue rectangles are downregulated miRNA and the red rectangles are upregulated miRNA. The blue balls are downregulated mRNA and the red balls are upregulated mRNA

Figure 4

The functions of DEmRNA in the ceRNA network were analyzed with DAVID. A, GO enrichment significance items of DEmRNA in different functional groups. B and C, Distribution of DEmRNA in clear cell renal cell carcinoma (ccRCC) for different GO‐enriched functions. DEmRNA, differentially expressed mRNA; GO, gene ontology

Figure 5

Signifcant pathway enrichment of DEmRNA. Red represents the upregulated DEmRNA. Green represents the downregulated DEmRNA. Blue represents signaling pathway. DEmRNA, differentially expressed mRNA

Figure 6

The results showed the patients with high expression of COL4A4, ERMP1 and PRELID2 had a better overall survival (OS) (P < .05). In contrast, patients with low expression of NOG, SPI1, TGFβ1, TYRP1 and VIM had better overall survival (P < .05). ( ) High expression.

Figure 7

Identification and performance evaluation of the 9‐lncRNA signature in the training dataset. A, Kaplan‐Meier survival curve analysis for overall survival of clear cell renal cell carcinoma patients using the 9‐lncRNA signature in the training dataset; B, ROC curve analysis of the 9‐lncRNA signature in the training dataset; C, The distributions of the RSlncRNA, survival status and expression profiles of the 9 lncRNA of patients in the training dataset

Figure 8

Evaluation of the 9‐lncRNA signature in the testing dataset. A, Kaplan‐Meier survival curve analysis for overall survival of clear cell renal cell carcinoma patients using the 9‐lncRNA signature in the testing dataset; B, receiver operating characteristic curve analysis of the 9‐lncRNA signature in the testing dataset; C, the distributions of the RSlncRNA, survival status and expression profiles of the 9 lncRNA of patients in the testing dataset

Figure 9

Evaluation of the 9‐lncRNA signature in the entire dataset. A, Kaplan‐Meier survival curve analysis for overall survival of clear cell renal cell carcinoma patients using the 9‐lncRNA signature in the entire dataset; B, receiver operating characteristic curve analysis of the 9‐lncRNA signature in the entire dataset; C, the distributions of the RSlncRNA, survival status and expression profiles of the 9 lncRNA of patients in the entire dataset

Figure 10

The prognostic value of different clinical features for overall survival of clear cell renal cell carcinoma patients. Kaplan‐Meier curves of 3 independent prognostic indictors

Figure 11

Kaplan‐Meier survival curve analysis for overall survival of patients stratified by age, stage and grade using the 9‐lncRNA signature in the entire dataset. A, Kaplan‐Meier survival curves of the younger patients group; B, Kaplan‐Meier survival curves of the older patient group; C, Kaplan‐Meier survival curves of the early stage patients group; D, Kaplan‐Meier survival curves of the late‐stage patients group; E, Kaplan‐Meier survival curves of the low‐grade patients group; F, Kaplan‐Meier survival curves of the high‐grade patients group