The ceRNA Network Has Potential Prognostic Value in Clear Cell Renal Cell Carcinoma: A Study Based on TCGA Database

Clear cell renal cell carcinoma (ccRCC) is a very common cancer in urology. Many evidences suggest that complex changed pathways take a nonnegligible part in the occurrence and development of ccRCC. Nevertheless, the underlying mechanism is not clear. In this study, the expression data between ccRCC and normal tissue samples in TCGA database were compared to distinguish differentially expressed genes (DEGs: mRNAs, miRNAs, and lncRNAs). Afterwards, we used GO enrichment and KEGG pathway enrichment analyses to explore the functions of these DEGs. We also found the correlation between three RNAs and created a competing endogenous RNA (ceRNA) network. Moreover, we used univariate Cox regression analysis to select DEGs that are connected with overall survival (OS) of ccRCC patients. We found 1652 mRNAs, 1534 lncRNAs, and 173 miRNAs that were distinguished in ccRCC compared with normal tissues. According to GO analysis, the maladjusted mRNAs are mainly concentrated in immune cell activation and kidney development, while according to KEGG, they are mainly concentrated in pathways related to cancer. A total of 5 mRNAs, 1 miRNA, and 4 lncRNAs were connected with patients' OS. In this article, a network of lncRNA-miRNA-mRNA was established; it is expected to be able to indicate possible molecular mechanisms for initial of ccRCC and provide a new viewpoint for diagnosis of ccRCC.


Introduction
RCC accounts for about 3% of human cancers; in the last 20 years, morbidity has increased about 2% per year both worldwide and in Europe, causing about 99200 RCC patients and 39100 RCC-related deaths in EU in 2018 [1]. RCC is the commonest kidney essence lesion, accounting for approximately 90% of all renal malignances. Many patients with renal mass have no symptoms until the terminal stage. At present, >60% of RCC patients are discovered by ultrasound (US) or CT performed for other disease. Flank pain, visible haematuria, and palpable abdominal mass which are classical signs of RCC are less now [2]. Therefore, an in-depth study of the biomarkers of tumorigenesis and progression in RCC is badly needed to gain the solutions and realize diagnosis goals.
In the human genome, protein-coding genes account for less than 3 percent; however, more than 80% of our genomes have no protein-coding capacity. Such transcription is called noncoding RNAs (ncRNAs), which are divided into long noncoding RNAs (lncRNAs) and small noncoding RNAs (sncRNAs). More and more evidences showed that lncRNAs are connected with tumorigenesis and development through a variety of biological processes, including transcriptional regulation, self-renewal, and carcinogenesis [3]. The rapid development of RNA-Seq technology allows people to discover new lncRNAs related to urologic malignancies [4]. The article analyzed the differentially expressed lncRNAs and their function in ccRCC.
microRNAs are sncRNAs with regulatory function [5]. After binding to the 3′-UTR of mRNA, it can inhibit the process of transcription and even induce mRNA degradation. However, miRNAs are also influenced by other ncRNAs, for example, lncRNAs and circular RNAs (circRNAs), which serve as sponges for miRNAs [6,7]. Currently, dysregulated miRNAs are believed to be connected with the occurrence of tumors including gastrointestinal cancer [8], colon cancer [9], prostate cancer [10], and colorectal cancer [11]. Thus, miRNAs may be a good biomarker for cancer diagnosis.
Competitive endogenous RNA theory indicated that RNAs which have miRNA binding sites struggle for posttranscriptional control. This theory has got wide interest as a whole function of lncRNAs and circRNAs and a substitution function of mRNAs. Moreover, the hypothesis assumes that specific RNAs can regulate the expression of miRNA target gene by inhibiting miRNA activity [12]. However, there are few studies on ceRNA of ccRCC.
We explored the interaction among three differentially expressed RNAs from The Cancer Genome Atlas (TCGA) database. In addition, these RNAs were annotated for possible biological functions. Then, we established a ceRNA network in ccRCC. Besides, we used K-M and receiver operating characteristic analysis to distinguish prognostic DEGs for predicting OS of ccRCC. Our results provide new insights into diagnosis strategies for ccRCC.

Materials and Methods
2.1. Data Download and Processing. We downloaded ccRCC patients' transcriptome profiling and clinical data from TCGA database (https://tcga-data.nci.nih.gov/tcga/), which was inputted on the Illumina HiSeq RNA-Seq platform. The exclusion criteria were (i) samples without clinical data and (ii) samples without complete information of the stage and overall survival period. Finally, a total of 534 cases contain 472 clear cell renal cell carcinoma tissues and 62 adjacent nontumor renal tissues were included in our study. We annotated the names of RNAs through Homo sapiens.GRCH38.84.chr.gtf.gz downloaded from Ensembl Genome Browser 97 (http://www.ensembl.org). Both RNA profile data and clinical characteristics of ccRCC are publicly available and in open-access platforms. Therefore, approval by local ethics committee was not needed.

Analysis of Differentially Expressed Genes.
We used the edgeR package to make comparisons between tumor tissues and normal specimens to select the DEGs. DEmRNAs and DElncRNAs were distinguished through the threshold of a log fold change ðlogFCÞ > 2 and an adj. P value < 0.05, and the DEmiRNAs was identified using the threshold of a logFC > 1 and an adj. P value < 0.05.

Cluster
Analysis of the DEGs. R software was used for cluster analysis based on the expression value of each sample. With R package "pheatmap," hierarchical clustering analysis was conducted. We showed the results through a clustergram. The column means the samples, and the row shows the gene expression level.

2.6.
Construction of the ceRNA Network. We identified DEGs and the relationships between miRNA-mRNA and miRNA-lncRNA. Based upon these results, Cytoscape 3.6.1 (version 3.6.1, San Diego CA) was used to establish the ceRNA network.

Construction of the Protein-Protein Interaction (PPI)
Network. To explore the interaction between mRNAs involved in this ceRNA network, a PPI network was constructed by the Search Tool for the Retrieval of Interacting Genes (http://string.embl.de/) with a composite score greater than 0.4 as the cut-off criterion [13].

Survival and Receiver Operating Characteristic Analyses.
We used a univariate Cox regression model to analyze the connection between ccRCC patients' OS and ceRNA network DEGs. P value < 0.05 was regarded as statistically significant. Then, those DEGs were presented with the survival curve. The cut-off point divided all samples into high and low expression groups. In addition, sensitivity and specificity were calculated through ROC curves and AUC values.
2.9. Statistical Analysis. We analyzed the data through R and Cytoscape software. Data were expressed as mean ± standard deviation. The differences between two groups were analyzed through fold change and Student's t-test. The survival curves were constructed by K-M method, and log-rank tests were used to test survival differences. When P < 0:05, we thought the difference are significant.

GO Enrichment Analysis of DEmRNAs.
We used DAVID database to analyze these dysregulated genes and perform enrichment analysis to determine their respective functions. The upregulated genes were mainly enriched in T cell activation and regulation of leukocyte activation (Figures 2(a) and 2(c)). The downregulated genes were mostly enriched in renal system development and urogenital system development (Figures 2(b) and 2(d)).

KEGG Pathway Enrichment Analysis of DEmRNAs.
The KEGG pathway enrichment method was also used to analyze the DEmRNAs. These DEmRNAs had the strongest correlation with neuroactive ligand-receptor interaction, cytokinecytokine receptor interaction, metabolism of xenobiotics by cytochrome P450, cell adhesion molecules (CAMs), and complement and coagulation cascades ( Figure 3). Based on these results, we believed that the DEmRNAs play an indispensable part in the progression of ccRCC.
3.4. ceRNA Network. There were 44 DElncRNAs that may bind to 25 DEmiRNAs. We searched for the DEmiRNAs' target mRNAs through using miRDB, miRTarBase, and Tar-getScan. Based on the Venn intersection analysis, 33 DEmRNAs were identified. At last, we established a ceRNA network ( Figure 4).

Protein-Protein Interaction Network.
To clarify the relationships between the 33 DEmRNAs involved in this ceRNA network, a PPI network was constructed using the STRING database containing 32 nodes and 15 edges ( Figure 5). KEGG pathway enrichment analysis of these mRNAs was performed with KOBAS 3.0 with P < 0:05 as the cut-off criterion. The results showed that these mRNAs were mainly enriched in pathways related to cancer (Table 4).

Discussion
The abnormal expression of protein-coding and proteinnoncoding transcription is one feature of the cancer transcriptome [14]. The prognosis of ccRCC has been a big         BioMed Research International challenge due to the difficultly of diagnosis. So, it is significant to find effective biomarkers of ccRCC. In these years, dysregulated mRNAs [15], miRNAs [16], and lncRNAs [17] have been reported in ccRCC. There have been many reports of these RNAs on kidney tumor alone, but the interaction among mRNA, miRNA, and lncRNA is still unclear. Many of the genes with known ceRNA interactors have been found to be associated with liver cancer, leukaemias, lymphomas, and so on [18]. It is necessary to study the ceRNA network in ccRCC systematically. In this study, 4 prognostic DElncRNAs (AC011383.1, ALDH1L1-AS2, DNAJC3-AS1, and PSORS1C3) were included in the ceRNA network. They may be independent prognostic factors of ccRCC patients' OS. Liang et al.  Figure 4: The ceRNA network. The triangles denote miRNAs, circles denote mRNAs, and rhombuses denote lncRNAs. Red and green represent upregulated and downregulated genes, respectively. The size of the point represents the expression of a given RNAs, whereas the larger points indicate a higher expression. 7 BioMed Research International reported that DNAJC3-AS1 plays a positive role in osteosarcoma evolvement by regulating DNAJC3. Moreover, it is a possible marker and treatment point for osteosarcoma [19].
Besides, we identified one independent prognostic DEmiRNA (miR-144). The upregulated miR-144 could enhance ccRCC malignancy and resistance to sunitinib by     [20]. Nevertheless, Liu et al. believed that miR-144 inhibited cancer cell proliferation and metastasis in renal cell carcinoma [21]. The results in this study show that miR-144 has a positive impaction on ccRCC patients' OS.
We found 5 mRNAs that are connected with ccRCC patients' prognosis: NETO2, NOD2, PAPPA, PCDH9, and SPI1. Oparina et al. report that NETO2's expression level was increased in 90% ccRCC and 50% of papillary renal cancers. It is a possible biomarker in kidney cancer [22]. Mey et al. found that NOD2's expression level is higher in tumor tissues compared to normal tissues [23]. Dalgin   genes are related to proteolysis and cell adhesion, including PAPPA [24]. However, there is no research about PCDH9 and SPI1 in ccRCC. In the present work, we found the two novel mRNAs as key predictors of ccRCC prognosis. PCDH9 is a member of the protocadherin family. It is associated with a kind of tumors, for example, melanoma [25], ovarian cancer [26], and medulloblastoma [27]. Ren et al. proved that PCDH9 is a tumor-inhibiting gene and has prognostic value in prostate cancer [28]. SPI1 is a member of the ETS family. It is relevant to multiple malignancies, for example, papillary thyroid carcinoma 30 , pediatric T cell acute lymphoblastic leukemia [29], and breast cancer [30]. SPI1 is closely related to clinical features such as grade, metastasis, and stage, which means that SPI1 may be a potential prognostic biomarker of ccRCC.
A PPI network was built to illustrate the relationship between the mRNAs involved in the ceRNA network. Some of these mRNAs are closely connected with each other, suggesting that their interactions may play an important role in the development of ccRCC, which further confirms the significant role of this ceRNA network in ccRCC.
However, this study has some limitations. First, although we established the ceRNA network, we have not demonstrated other regulatory models. Besides, other studies may draw a different conclusion due to the differences in inclusion and exclusion criteria. Finally, there are few experimental data explaining the mechanisms of ceRNA, and we need further experiments to illustrate the role of ceRNA in ccRCC.

Conclusion
This article illustrated that lncRNAs, miRNAs, and mRNAs involved in the ceRNA network may be possible biomarkers. They are expected to predict the survival rate in ccRCC patients. Nevertheless, we need more experiments to validate these RNAs' biological function.

Data Availability
Both RNA profiles data and clinical characteristics of ccRCC are publicly available and in open access platforms.

Conflicts of Interest
The authors declare that there is no conflict of interests.