Key miRNAs and target genes played roles in the development of clear cell renal cell carcinoma

Abstract

BACKGROUND:

Clear cell renal cell carcinoma (CCRCC) is the most aggressive form of renal cell carcinoma (RCC).

OBJECTIVE:

This study was aimed to identify the differentially expressed miRNAs and target genes in CCRCC.

METHODS:

The miRNA and mRNA next-generation sequencing data were downloaded from The Cancer Genome Atlas (TCGA) dataset. Differential expression analysis was performed, followed by correlation analysis of miRNA-mRNA. Functional enrichment and survival analysis was also performed.

RESULTS:

Seven hundred and eighty-seven patients with CCRCC from TCGA data portal were included in this study. A total of 52 differentially expressed miRNAs were identified in CCRCC. Then 2361 differentially expressed genes (DEGs) were identified. Prediction analysis and correlation analysis revealed that 89 miRNA-mRNA pairs were not only predicted by algorithms but also had a significant inverse relationship. Several differentially expressed miRNAs such as hsa-mir-501 and their target genes including AK1, SLC25A15 and PCDHGC3 had a significant prognostic value for CCRCC patients.

CONCLUSIONS:

Alterations of differentially expressed miRNAs and target genes may be involved in the development of CCRCC and can be considered as the prognostic markers for CCRCC.

Keywords

Clear cell renal cell carcinoma differentially expressed miRNAs targets survivability

1. Introduction

Renal cell carcinoma (RCC) accounts for about 80% of kidney cancers, and large numbers of new cases are diagnosed worldwide [1]. RCC consists of various types of cancer: papillary renal cell carcinoma, clear cell renal cell carcinoma, chromophobe renal cell carcinoma, urothelial carcinoma, renal medullary carcinoma and collecting duct carcinoma [2]. Clear cell renal cell carcinoma (CCRCC) develops from epithelium of the proximal tubules and is the most aggressive form of RCC. CCRCC has two origins including sporadic and familial cases [3]. The underlying mechanism includes obesity, smoking and acquired kidney disease [4, 5, 6].

The majority of CCRCC cases are diagnosed at the late stage because of the asymptomatic course of the disease. The classic symptoms of advanced CCRCC are hematuria, fatigue, flank pain and rarely abdominal mass [7]. CCRCC is a therapy-resistant carcinoma due to the poor or not at all response to radiotherapy, chemotherapy and hormonal therapy. Generally, most patients with localized disease are treated with surgical resection, however, nearly a third develop recurrence and die of the disease. It is suggested that CCRCC is rarely cured once spread beyond the kidney [8]. In addition, there is no commensurate improvement in survival of CCRCC. It is reported that CCRCC has a worse prognosis with 5-year survival of 77% [9, 10]. Large numbers of prognostic markers have been studied in CCRCC such as p53, XIAP, VEGF, HIF1- $\alpha$ , surviving and carbonic anhydrase IX, however, their clinical use remains debatable [11].

Recently, the pathological mechanism of CCRCC is still unclear. It is needed to discover the molecular basis of the disease, which is helpful in understanding the molecular mechanism and providing potential detection and prognostic markers. It is noted that miRNA expression analyses have provided meaningful insight into the clinical pathology of many solid tumors. MiRNAs are small non-coding RNAs that function in the regulation of protein-coding or non-coding gene expression. Several miRNAs including hsa-mir-215, hsa-mir-708, hsa-mir-204, hsa-mir-205, hsa-mir-141 and hsa-mir-199a have been found to regulate of CCRCC cell growth, apoptosis, invasion and migration, which suggested that miRNA dysfunction may be related to CCRCC carcinogenesis [12, 13, 14, 15, 16, 17, 18].

In this study, we integrated the analysis of miRNA and mRNA profiling data, which were downloaded from The Cancer Genome Atlas (TCGA) dataset of CCRCC. Differentially expressed miRNAs and differentially expressed genes (DEGs) were identified in CCRCC compared with the normal tissues. Function enrichment analysis of a variety of target genes of differentially expressed miRNAs was then performed. In the end, several candidate miRNAs and target genes were identified for the survival analysis of CCRCC. Our result may provide the new field in uncovering the molecular mechanism of CCRCC.

Table 1
Clinical information about these 787 patients with CCRCC

Parameter	Patients (N $=$ 787)
Gender
Male	596
Female	191
Age
$<$ 60	247
$\geqslant$ 60	540
Race
Asian	8
Black or African American	56
White	716
Vital status
Unknown	7
Alive	360
Dead	427
Tumor histologic grade
G1	14
G2	230
G3	207
G4	78
GX	5
Unknown	253
Radiation therapy
Yes	31
Yes	2
No	142
Not Available	393
Tumor histologic stage
Stage I	269
Stage II	57
Stage III	125
Stage IV	84
Targeted molecular therapy
Yes	9
No	135
Not applicable	393

Figure 1.

The interactions of miRNA-targets pairs with negatively correlation. Diamonds and ellipses represent the miRNAs and target genes, respectively. The red and green colors represent up-regulation and down-regulation, respectively.

2. Materials and methods

2.1 Datasets

In this study, the miRNA expression and mRNA expression data from TCGA data portal (http://tcga.cancer.gov) was downloaded. Based on the corresponding clinical information, the patients were excluded who had a history of other malignancy. Only patients (with primary solid tumor) with fully characterized miRNA profiles and mRNA profiles data were included. The para-carcinoma tissue was used for the normal control. Furthermore, study types were defined as: miRNASeq BCGSC IlluminaHiSeq-miRNASeq platform for miRNA expression data (including 254 cases and 71 normal controls), RNASeqV2 UNC IlluminaHiseq_RNASeqV2 platform for mRNA expression data (including 533 cases and 72 normal controls). Clinical information about these 787 patients with CCRCC was showed in Table 1.

2.2 Differential expression analysis of miRNAs and genes

Differential expression of miRNAs and genes between CCRCC tumors and normal tissues was assessed using the R-bioconductor package DESeq [19], and the p-value were then calculated. Multiple hypothesis testing was performed by the Benjamini-Hochberg procedure [20], and the false discovery rate was used to control for differential expression. Differentially expressed miRNAs and genes were identified with the thresholds of p_value $<$ 0.001, | log2 (Fold_change) normalized | $>$ 1 and | value1-value2 | $>$ 100. Herein, the value 1 refers to the mean value of read count in the RNA sequencing of the solid tumor. The value 2 refers to the mean value of read count in the RNA sequencing of the para-carcinoma tissue. | value1-value2 | refers to cancer-normal. The inclusion criteria of | value1-value2 | $>$ 100 was used to identify differentially expressed miRNAs and genes more strictly.

2.3 MiRNA-mRNA expression correlation analysis

Pairwise pearson correlation coefficients between differentially expressed miRNAs and genes were first calculated. Statistical significance was defined as p_value $<$ 0.05 and r_value $<$ 0. Six miRNA-target prediction tools (RNA22, miRanda, miRDB, miRWalk, PICTAR2 and Targetscan) were then used to predict miRNAs that regulate the identified DEGs. The miRNA-targets which predicted by more than four algorithms and verified by experiment in miRWalk database were screened out. All the miRNA-target pairs were finally obtained, which were not only predicted by algorithms (or verified by experiment) but also had the negative correlations. In addition, miRNA-target pairs with negative correlations were further screened with the thresholds of p_value $<$ 0.05 and r_value $<-$ 0.3. The miRNA-target regulatory network was then constructed and visualized using Cytoscape software [21].

Table 2
Top 15 Go terms for target genes in CCRCC

GO ID	GO term	Count	False	Genes
			discovery rate
Biological process
GO:0007165	Signal transduction (BP)	10	0.026807	CDK18, HMOX1, GNB5, INPP5D, LILRB1, UNC5B, PLK2, PAG1, NR3C2, ARHGAP24
GO:0007155	Cell adhesion (BP)	6	0.035423	MCAM, ITGA5, ICAM1, NID1, SCARB1, PCDHGC3
GO:0030198	Extracellular matrix organization (BP)	2	0.046681	COL14A1, OLFML2B
GO:0006810	Transport (BP)	6	0.030157	SDHD, ARL4C, UQCRFS1, SLC25A15, GOT2, SCARB1
GO:0055085	Transmembrane transport (BP)	6	0.035956	SLC1A4, SLC41A2, HMOX1, SLC9A9, SLC15A4, SFXN3
GO:0043065	Positive regulation of apoptotic process (BP)	3	0.043386	INPP5D, LILRB1, IGFBP3
GO:0032516	Positive regulation of phosphoprotein phosphatase activity (BP)	1	0.044893	MAGI2
GO:0005975	Carbohydrate metabolic process (BP)	5	0.02603	GOT2, KIAA1161, GPD1L, PPP1R3B, GAPDH
GO:0007596	Blood coagulation (BP)	6	0.028559	PROS1, EHD2, ITGA5, PIK3CG, INPP5D, PROCR
GO:0007588	Excretion (BP)	3	0.008773	HMOX1, SCNN1A, NR3C2
GO:0007264	Small GTPase mediated signal transduction (BP)	6	0.009351	MYO9B, ARL4C, HMOX1, RAB37, RALGPS1, ARHGAP24
GO:0006096	Glycolysis (BP)	3	0.009211	LDHB, PDHB, GAPDH
GO:0051056	Regulation of small GTPase mediated signal transduction (BP)	3	0.03989	MYO9B, RASGRP4, ARHGAP24
GO:0007166	Cell surface receptor signaling pathway (BP)	3	0.039582	OSMR, TNFRSF1B, LILRB1
GO:0071222	Cellular response to lipopolysaccharide (BP)	2	0.030356	ICAM1, LILRB1
Molecular function
GO:0005524	ATP binding (MF)	10	0.035611	AK1, CDK18, MYO9B, PAK6, EHD2, PIP4K2C, PIK3CG, SACS, SUCLG2, PLK2
GO:0005515	Protein binding (MF)	27	0.002397	MYO9B, ARL4C, EHD2, FSTL3, HSPB8, ITGA5, FHOD1, INPP5D, ICAM1, USP46, TNFRSF1B, MAGI2, LILRB1, SCNN1A, UNC5B, DISC1, CAV2, PLK2, INHBB, PNMA2, PAG1, PROCR, EGLN3, NR3C2, GAPDH, IGFBP3, ARHGAP24
GO:0000166	Nucleotide binding (MF)	12	0.039187	AK1, CDK18, MYO9B, PAK6, ARL4C, EHD2, PIP4K2C, RAB37, PIK3CG, SUCLG2, PLK2, GAPDH
GO:0005509	Calcium ion binding (MF)	6	0.035635	PROS1, EHD2, PLS3, RASGRP4, NID1, PCDHGC3
GO:0004872	Receptor activity (MF)	14	0.002662	PAQR7, PAQR5, ILDR1, ITGA5, ICAM1, OSMR, FCGR2A, TNFRSF1B, PTGER3, LILRB1, SCARB1, UNC5B, PROCR, NR3C2
GO:0004674	Protein serine/threonine kinase activity (MF)	5	0.027515	CDK18, PAK6, HSPB8, PIK3CG, PLK2
GO:0005525	GTP binding (MF)	4	0.049328	ARL4C, EHD2, RAB37, SUCLG2
GO:0003779	Actin binding (MF)	4	0.038836	MYO9B, PLS3, FHOD1, SCNN1A
GO:0042803	Protein homodimerization activity (MF)	9	0.001099	MYO9B, HMOX1, OLFML2B, GPD1L, LILRB1, SCARB1, CAV2, INHBB, NR3C2
GO:0016814	Hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds, in cyclic amidines (MF)	1	0.049651	APOBEC3F
GO:0051287	NAD binding (MF)	3	0.004201	LDHB, GPD1L, GAPDH
GO:0016491	Oxidoreductase activity (MF)	5	0.034842	LDHB, PDHB, HMOX1, EGLN3, GAPDH
GO:0005518	Collagen binding (MF)	2	0.033325	COL14A1, NID1
GO:0051219	Phosphoprotein binding (MF)	2	0.025839	GPRIN1, CAV2
GO:0008121	Ubiquinol-cytochrome-c reductase activity (MF)	1	0.049651	UQCRFS1

Table 2, continued
GO ID	GO term	Count	False	Genes
			discovery rate
Cellular component
GO:0016021	Integral to membrane (CC)	31	1.60E-05	MCAM, SEZ6L2, SDHD, CLEC14A, SLC1A4, UQCRFS1, PAQR7, C14orf37, SLC41A2, PAQR5, FAM57A, CHST15, EVI2A, ILDR1, SLC25A15, ITGA5, OSMR, KIAA1161, SLC9A9, FCGR2A, TNFRSF1B, PTGER3, LILRB1, SCARB1, LHFPL2, SCNN1A, UNC5B, SLC15A4, SFXN3, PAG1, PCDHGC3
GO:0016020	Membrane (CC)	25	0.001372	SDHD, AP1M2, CLEC14A, SLC1A4, UQCRFS1, PIP4K2C, C14orf37, PAQR5, FAM57A, CHST15, EVI2A, SLC25A15, OSMR, KIAA1161, SLC9A9, SCARB1, LHFPL2, SCNN1A, LRG1, SLC15A4, SFXN3, PROCR, NR3C2, GAPDH, PCDHGC3
GO:0005737	Cytoplasm (CC)	31	0.000575	AK1, DUSP10, MYO9B, PAK6, LDHB, ARL4C, ACOX1, PLS3, PIP4K2C, ILDR1, HSPB8, BHLHB9, FHOD1, PIK3CG, INPP5D, APOBEC3F, GPD1L, RASGRP4, NID1, MAGI2, LILRB1, SCARB1, SACS, DISC1, CAV2, PLK2, RALGPS1, EGLN3, NR3C2, GAPDH, ARHGAP24
GO:0005739	Mitochondrion (CC)	13	0.00189	SDHD, LDHB, PDHB, ACOX1, UQCRFS1, SLC25A15, GOT2, IVD, ACAD8, SACS, SUCLG2, ACADSB, SFXN3
GO:0005886	Plasma membrane (CC)	30	1.08E-06	MCAM, SEZ6L2, ARL4C, EHD2, SLC1A4, PAQR7, SLC41A2, FAM57A, ILDR1, GPRIN1, GNB5, ITGA5, GOT2, PIK3CG, INPP5D, ICAM1, RASGRP4, FCGR2A, TNFRSF1B, MAGI2, PTGER3, LILRB1, SCARB1, SCNN1A, UNC5B, CAV2, RALGPS1, PAG1, PCDHGC3, ARHGAP24
GO:0005624	Membrane fraction (CC)	6	0.034533	EHD2, HMOX1, RASGRP4, LILRB1, SCNN1A, CAV2
GO:0005759	Mitochondrial matrix (CC)	6	0.000533	PDHB, GOT2, IVD, ACAD8, SUCLG2, ACADSB
GO:0016942	Insulin-like growth factor binding protein complex (CC)	1	0.047948	IGFBP3
GO:0009897	External side of plasma membrane (CC)	4	0.012118	ITGA5, ICAM1, LILRB1, SCNN1A
GO:0005901	Caveola (CC)	3	0.006982	HMOX1, SCARB1, CAV2
GO:0005900	Oncostatin-M receptor complex (CC)	1	0.047948	OSMR
GO:0071062	Alphav-beta3 integrin-vitronectin complex (CC)	1	0.047948	ITGA5
GO:0034706	Sodium channel complex (CC)	1	0.047948	SCNN1A
GO:0009331	Glycerol-3-phosphate dehydrogenase complex (CC)	1	0.047948	GPD1L
GO:0030895	Apolipoprotein B mRNA editing enzyme complex (CC)	1	0.047948	APOBEC3F

Table 3

KEGG pathways for target genes in CCRCC

KEGG ID	KEGG term	Count	False discovery rate	Genes
Kegg:00010	Glycolysis/Gluconeogenesis	3	0.013755	LDHB, GAPDH, PDHB
Kegg:00270	Cysteine and methionine metabolism	2	0.035034	LDHB, GOT2
Kegg:00620	Pyruvate metabolism	2	0.038062	LDHB, PDHB
Kegg:00640	Propanoate metabolism	2	0.030092	LDHB, SUCLG2
Kegg:04910	Insulin signaling pathway	3	0.037919	PPP1R3B, INPP5D, PIK3CG
Kegg:00071	Fatty acid metabolism	2	0.039173	ACOX1, ACADSB
Kegg:04666	Fc gamma R-mediated phagocytosis	3	0.023925	FCGR2A, INPP5D, PIK3CG
Kegg:04810	Regulation of actin cytoskeleton	4	0.024688	PAK6, ITGA5, PIP4K2C, PIK3CG
Kegg:04975	Fat digestion and absorption	2	0.036229	GOT2, SCARB1
Kegg:00020	Citrate cycle (TCA cycle)	3	0.007862	SDHD, PDHB, SUCLG2
Kegg:04145	Phagosome	3	0.036578	ITGA5, FCGR2A, SCARB1
Kegg:04070	Phosphatidylinositol signaling system	3	0.015547	PIP4K2C, INPP5D, PIK3CG
Kegg:04510	Focal adhesion	4	0.021933	CAV2, PAK6, ITGA5, PIK3CG
Kegg:04960	Aldosterone-regulated sodium reabsorption	3	0.010856	SCNN1A, NR3C2, PIK3CG
Kegg:05211	Renal cell carcinoma	3	0.013999	EGLN3, PAK6, PIK3CG
Kegg:04380	Osteoclast differentiation	4	0.010439	LILRB1, FCGR2A, LILRB2, PIK3CG
Kegg:05150	Staphylococcus aureus infection	2	0.038693	FCGR2A, ICAM1
Kegg:05100	Bacterial invasion of epithelial cells	3	0.013999	CAV2, ITGA5, PIK3CG
Kegg:00280	Valine, leucine and isoleucine degradation	3	0.008317	IVD, ACADSB, ACAD8

Figure 2.

The heat map of miRNAs in the miRNA-targets regulatory network. Diagram presents the result of a two-way hierarchical clustering of miRNAs and samples. The clustering is constructed using the complete-linkage method together with the Euclidean distance. Each row represents a miRNA and each column, a sample. The miRNA clustering tree is shown on the right. The colour scale illustrates the relative level of miRNA expression: red, below the reference channel; green, higher than the reference.

Figure 3.

The heat map of target genes in the miRNA-targets regulatory network. Diagram presents the result of a two-way hierarchical clustering of DEGs and samples. The clustering is constructed using the complete-linkage method together with the Euclidean distance. Each row represents a DEG and each column, a sample. The DEG clustering tree is shown on the right. The colour scale illustrates the relative level of DEG expression: red, below the reference channel; green, higher than the reference.

Figure 4.

The survival curves of selected miRNAs and DEGs with significant prognostic value for patients with CCRCC. Censoring samples are marked with ‘+’. The x-axis represents time to event and the y-axis represents the percentage. The number of samples, censored number and concordance index (CI) is shown in the top-right insets. High- and low-risk groups are labeled with red and green curves, respectively.

2.4 Functional annotation of target genes of differentially expressed miRNAs

In order to investigate the functional information of target genes in the miRNA-target regulatory network, Database for Annotation, Visualization, and Integrated Discovery (DAVID) was applied to address it by web-based analysis modules, including Gene Ontology (GO) functional categories [22] and Kyoto Encyclopedia of Genes and Genomes (KEGG) biochemical pathway [23].

2.5 Survivability analysis of differentially expressed miRNAs and target genes

The online software SurvExpress (http://bioinformatica.mty.itesm.mx:8080/Biomatec/SurvivaX.jsp) is an online biomarker validation tool for cancer gene expression data using survival analysis. SurvMicro (http://bioinformatica.mty.itesm.mx:8080/Biomatec/Survmicro.jsp) is an assessment of miRNA-based prognostic signatures for cancer clinical outcomes by multivariate survival analysis. In order to analyze the survivability of miRNAs and target genes in the miRNA-target regulatory network, the TCGA dataset was applied to analyze the survivability of selected miRNAs and genes in CCRCC patients by using SurvMivro and SurvExpress. In this study, we performed the SurvMivro/SurvExpress workflow as follows: (1) Input miRNA/gene list and select dataset; (2) Specify analysis, stratification and outputs; (3) Cox fitting, risk estimation and output writing; (4) Visualize plots and tables. The detailed processes are as follows: In the first Input web page, we paste the list of miRNA/gene and choose the dataset from available datasets; SurvExpress validates and searches the miRNA/gene and dataset to show the Analysis web page where we select the censored outcome and visualize the results. Only those miRNAs and genes with the survival curves not overlapping at one or more points were associated with the survival time of CCRCC patients.

3. Results

3.1 MiRNA and mRNA expression signature

According to the thresholds of p_value $<$ 0.001, | log2 (Fold_change) normalized | $>$ 1 and | value1-value2 | $>$ 100, a total of 52 differentially expressed miRNAs between CCRCC and normal tissue, including 31 up-regulated genes and 21 down-regulated miRNAs were identified. In addition, a total of 2361 DEGs including 1350 up- and 1011 down-regulated genes were identified in CCRCC compared to normal tissue. A clear list of the p_value and log2 (Fold_change) normalized for miRNAs highlighted in their discussion was shown in the Supplementary Table 1.

3.2 MiRNA-mRNA interactions

Epigenetic modification of gene regulation plays an important role in many cancers. According to miRNA-targets expression correlation analysis, there were 40939 miRNA-targets pairs which were negatively correlated (p_value $<$ 0.05 and r_value $<$ 0). Based on the prediction by more than four algorithms or verification by experiment in miRWalk database, there were 5566 miRNA-targets pairs predicted by algorithms and 1273 miRNA-targets pairs verified by experiment. Taken together, we obtained 6839 miRNA-targets pairs in total. After further screening, a total of 89 miRNA-targets pairs with negatively correlation were identified under the thresholds of p_value $<$ 0.05 and r_value $<-$ 0.3. Figure 1 illustrated the interactions of miRNA-targets pairs with negatively correlation. The heat map of miRNAs and genes in the miRNA-targets regulatory network was presented in Figs 2 and 3, respectively.

3.3 Functional annotation

To find the potential biological functions of target genes in the miRNA-targets regulatory network, functional enrichment was conducted. The GO results showed that target genes were significantly related to signal transduction, cell adhesion and extracellular matrix organization of biological process; ATP binding, protein binding and nucleotide binding of molecular function; integral to the membrane, membrane and cytoplasm of the cellular component. Renal cell carcinoma signal was one of significant enriched signal pathways involving EGLN3, PAK6 and PIK3CG. Top 15 GO terms and all KEGG pathways of target genes were shown in Tables 2 and 3, respectively.

3.4 Survivability analysis

In order to analyze the survivability of miRNAs and target genes in the miRNA-target regulatory network, hsa-mir-215, hsa-mir-199a-1, hsa-mir-204, hsa-mir-501, hsa-mir-2355, AK1, CDK18, LRG1, GAPDH, SCARB1, HMOX1, SUCLG2, SLC25A15, TOM1L2, PCDHGC3, PROS1, SLC9A9 and EGLN3 were selected for survivability analysis. Our result showed that hsa-mir-501, AK1, SLC25A15 and PCDHGC3 had a significant prognostic value for CCRCC patients (Fig. 4).

4. Discussion

CCRCC is the most common type of RCC, which affects the health of patients. Complementation of multidimensional omics data can remarkably increase the value of the study results and highlight the prominent associations between gene expression and cancer [24]. In this study, we integrated miRNA profiles and mRNA profiles data of CCRCC to find the value miRNAs and genes which were associated with the development of CCRCC. Integration of miRNA profiles led to 52 differentially expressed miRNAs and 2361 differentially expressed genes in CCRCC compared with normal tissue. According to miRNA-mRNA expression correlation analysis and miRNA target prediction, we obtained 89 miRNA-mRNA pairs under the thresholds of p_value $<$ 0.05 and r_value $<-$ 0.3. The results suggested that these differentially expressed miRNAs and target genes may be associated with carcinogenesis of patients with CCRCC.

Hsa-mir-215 is involved in cancer cellular proliferation and migration, and can induce cell-cycle arrest by increasing the expression of p21 [25]. It is found that hsa-mir-215 is decreased in metastatic RCC samples compared to primary RCC tissues [12]. It is found that AK1 is significantly expressed in renal cells [26]. In this study, we found that hsa-mir-215 was up-regulated in the tumor tissues of CCRCC. One of target genes of hsa-mir-215, adenylate kinase 1 (AK1), was down-regulated in CCRCC. It is suggested that epigenetic modification regulation between hsa-mir-215 and AK1 was correlated with CCRCC progression. In addition, AK1 were closely associated with the survival time of CCRCC.

It is proposed that hsa-mir-199a-1 may target cancer-specific lncRNAs by miRNA response elements [27]. CDK18 belongs to the cell cycle-related CDK subfamilies, which play various functional roles by regulating different cell cycle progress [28]. Increased phosphorylation of CDK18 is found in the database of vasopressin regulation of kidney [29]. It is a putative up-regulated target gene of hsa-mir-101 in RCC specimens [30]. Herein, we first found that the expression of hsa-mir-199a-1 was down-regulated in the tumor tissues of CCRCC. Additionally, cyclin dependent kinase 18 (CDK18) was up-regulated and targeted by hsa-mir-199a-1. This indicated that the cell cycle-related gene CDK18 under the regulation of hsa-mir-199a-1 may serve as a biomarker for the early stages of CCRCC tumor development.

Lu et al. found that the expression of hsa-mir-204 was higher in human kidney compared with other organs tested [31]. It has been demonstrated that hsa-mir-204 is down-regulated and is a potential survival molecular biomarker of CCRCC [32, 33]. Moreover, the higher tumor grade of CCRCC is associated with a concomitant decrease in hsa-mir-204 [16]. LRG1 has been reported to play roles in cell proliferation, cell migration, cell apoptosis, immune response and neovascularization [34, 35, 36, 37]. It is worth mentioning that the missense mutations (L279F) in LRG1 gene is detected in the chromophobe RCC tissues [38]. In this study, we found that hsa-mir-204 was down-regulated. Furthermore, leucine rich alpha-2-glycoprotein 1 (LRG1), one of target genes of hsa-mir-204, was up-regulated in the tumor tissues of CCRCC. Our results provided additional evidence for their important roles of hsa-mir-204 and LRG1 in CCRCC.

It is reported that hsa-mir-501-5p is down-regulated and involved in the focal adhesion and extracellular matrix pathways in the papillary RCC [39]. Vila et al. found that the amount of GAPDH was increased in tumor tissues of CCRCC patients [40]. Scavenger receptor class B member 1 (SCARB1) has been suggested implicated in the development of CCRCC [41]. RNA-seq analysis showed that the expression of SCARB1 was up-regulated in CCRCC [42]. In addition, the mutation (rs4765623) in the SCARB1 gene has been found associated with CCRCC [43]. Banerjee et al. found that Heme oxygenase 1 (HMOX1, also called HO-1) was often over expressed in RCC, which protected cancer cells from apoptosis induced by chemo- therapeutic drug [44]. It is proposed that HMOX1 expression may be a useful biomarker for drugs response prediction for patients with metastatic CCRCC [45]. Our study found that hsa-mir-501 was down-regulated in CCRCC, which had a potential interaction with the up-regulation of GAPDH, SCARB1 and HMOX1. Our results suggested that hsa-mir-501 and target genes including GAPDH, SCARB1 and HMOX1 were correlated with CCRCC progression.

The expression of hsa-mir-2355 is up-regulated in lung adenocarcinoma compared with adjacent normal tissues [46]. In addition, hsa-mir-2355-5p has been detected in breast cancer when compared to normal tissue [47]. The expression of succinate-CoA ligase GDP-forming beta subunit (SUCLG2) is detected in the chromophobe RCC [48]. There is a significant decreased expression of SUCLG2 in CCRCC tissues compared with normal kidney tissue [49]. Herein, we found that hsa-mir-2355 was up-regulated in CCRCC and had a potential interaction with the down-regulation of SUCLG2, which indicated the important roles in the process of CCRCC.

Down-regulated expression of hsa-mir-200a is found in metastatic CCRCC compared to benign kidney tissues [50]. Additionally, the expression of hsa-mir-200a-3p in plasma is related to overall survival of metastatic kidney cancer [51]. It has been found that protocadherin gamma subfamily C, 3 (PCDHGC3) is expressed in RCC [52]. Protein S (PROS1) is up-regulated and involved in organismal and immune system of CCRCC [53]. In addition, genetic mutation of PROS1 (c.1294C $>$ T; p.R432W) is found in tuberous sclerosis complex-associated papillary RCC [54]. In the present study, we found that hsa-mir-200a was down-regulated, which was significantly inversely correlated with PCDHGC3 and PROS1. It is noted that PCDHGC3 was significantly associated with survival time of CCRCC patients. Our results provided additional evidence for their important roles of hsa-mir-200a and target genes including PCDHGC3 and PROS1 in CCRCC.

Hsa-mir-30c-2 is decreased in metastatic CCRCC compared to benign kidney tissue [50]. It is found that solute carrier family 9 member A9 (SLC9A9) is involved in chromosomal rearrangements in RCC [55]. Herein, we found that the expression of hsa-mir-30c-2 was decreased and inversely correlated with SLC9A9. In a word, the expression changes of SLC9A9 under the regulation of hsa-mir-30c-2 illuminated the mechanism of carcinogenesis which may play a crucial role in the progress of CCRCC.

Down-regulated hsa-mir-429 can significantly inhibit cancer cell proliferation, suggesting that hsa-mir-429 was a candidate tumor suppressive miRNA in RCC [56]. It is reported that hsa-mir-429 was down-regulated in CCRCC [32]. It is reported that egl-9 family hypoxia inducible factor 3 (EGLN3, also called PHD3) is important for the hypoxic cell cycle to proceed over G1/S checkpoint by reducing the stability of p27 in CCRCC cells [57]. Microarray analysis showed that EGLN3 was significantly up-regulated in CCRCC compared with normal tissue [58]. Furthermore, it has been demonstrated that high EGLN3 expression serves as the marker of poor prognosis in CCRCC patients [59]. In this study, we found that hsa-mir-429 was down-regulated, and the target gene, EGLN3, was up-regulated in CCRCC. Additionally, EGLN3 was significantly enriched in the renal cell carcinoma signal pathway. Our results suggested that epigenetic modification of EGLN3 in terms of hsa-mir-429 regulation was linked to CCRCC progression.

5. Conclusions

In summary, integrated analysis of the TCGA data led to a pool of differentially expressed miRNAs and DEGs. The epigenetic modifications between miRNAs (such as hsa-mir-215, hsa-mir-199a-1, hsa-mir-204, hsa-mir-501, hsa-mir-2355, hsa-mir-200a, hsa-mir-30c-2 and hsa-mir-429) and target genes including AK1, CDK18, LRG1, GAPDH, SCARB1, HMOX1, SUCLG2, SLC25A15, TOM1L2, PCDHGC3, PROS1, SLC9A9 and EGLN3 may be implicated in the growth of CCRCC. The epigenetic alterations of genes hold promise for becoming biomarkers for the prognosis of CCRCC. However, there is a limitation to our study. The sample size in the normal controls was small in the TCGA dataset. Larger numbers of normal controls that match the solid tumor are needed for further research.

Footnotes

Acknowledgments

This work was funded by “The molecular mechanism study of microRNAs in the development of Clear cell renal cell carcinoma” (KJ2017A244).

Conflict of interest

None declared.

Abbreviations

Supplementary data

Table 1

A clear list of the p_value and log2 (Fold_change) normalized for miRNAs

miRNAs	log2(Fold_change)	p_value
hsa-mir-2355	1.630849451	$<$ 1E-16
hsa-mir-30c-2	$-$ 1.00172441	$<$ 1E-16
hsa-mir-204	$-$ 1.071030601	$<$ 1E-16
hsa-mir-200a	$-$ 1.119397958	$<$ 1E-16
hsa-mir-501	$-$ 1.201711431	$<$ 1E-16
hsa-mir-199a-1	$-$ 1.283070226	$<$ 1E-16
hsa-mir-429	$-$ 1.816832419	$<$ 1E-16
hsa-mir-215	1.081785581	1.38E-09

References

Ferlay

Soerjomataram

Dikshit

Eser

Mathers

Rebelo

Parkin

D.M.

Forman

and Bray

, Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012, Int J Cancer 136 (2015), E359–386.

Jonasch

Gao

and Rathmell

W.K.

, Renal cell carcinoma, BMJ 349 (2014), g4797.

Pavlovich

C.P.

and Schmidt

L.S.

, Searching for the hereditary causes of renal-cell carcinoma, Nat Rev Cancer 4 (2004), 381–393.

Naito

Tomita

Rha

S.Y.

Uemura

Oya

Song

H.Z.

Zhong

L.H.

and Wahid

M.I.

, Kidney Cancer Working Group report, Jpn J Clin Oncol 40(Suppl 1) (2010), i51–56.

Lindblad

, Epidemiology of renal cell carcinoma, Scand J Surg 93 (2004), 88–96.

Murai

and Oya

, Renal cell carcinoma: etiology, incidence and epidemiology, Curr Opin Urol 14 (2004), 229–233.

Sternberg

C.N.

, Renal Cell Cancer: Diagnosis and Therapy, Springer (2009), 1916–1917.

Lovisolo

J.A.

Casati

Clerici

Marafante

Bono

A.V.

Celato

and Salvadore

, Gene expression profiling of renal cell carcinoma: a DNA macroarray analysis, BJU Int 98 (2006), 205–216.

Cheville

J.C.

Lohse

C.M.

Zincke

Weaver

A.L.

and Blute

M.L.

, Comparisons of outcome and prognostic features among histologic subtypes of renal cell carcinoma, American Journal of Surgical Pathology 27 (2003), 612.

10.

Steffens

Janssen

Roos

F.C.

Becker

Schumacher

Seidel

Wegener

Thuroff

J.W.

Hofmann

Stockle

Siemer

Schrader

Hartmann

Kuczyk

M.A.

Junker

and Schrader

A.J.

, Incidence and long-term prognosis of papillary compared to clear cell renal cell carcinoma – a multicentre study, Eur J Cancer 48 (2012), 2347–2352.

11.

Sun

Shariat

S.F.

Cheng

Ficarra

Murai

Oudard

Pantuck

A.J.

Zigeuner

and Karakiewicz

P.I.

, Prognostic factors and predictive models in renal cell carcinoma: a contemporary review, Eur Urol 60 (2011), 644–661.

12.

White

N.M.

Khella

H.W.

Grigull

Adzovic

Youssef

Y.M.

Honey

R.J.

Stewart

Pace

K.T.

Bjarnason

G.A.

Jewett

M.A.

Evans

A.J.

Gabril

and Yousef

G.M.

, miRNA profiling in metastatic renal cell carcinoma reveals a tumour-suppressor effect for miR-215, Br J Cancer 105 (2011), 1741–1749.

13.

Yoshino

Enokida

Itesako

Tatarano

Kinoshita

Fuse

Kojima

Nakagawa

and Seki

, Epithelial-mesenchymal transition-related microRNA-200s regulate molecular targets and pathways in renal cell carcinoma, J Hum Genet 58 (2013), 508–516.

14.

Saini

Yamamura

Majid

Shahryari

Hirata

Tanaka

and Dahiya

, MicroRNA-708 induces apoptosis and suppresses tumorigenicity in renal cancer cells, Cancer Res 71 (2011), 6208–6219.

15.

Majid

Saini

Dar

A.A.

Hirata

Shahryari

Tanaka

Yamamura

Ueno

Zaman

M.S.

Singh

Chang

Deng

and Dahiya

, MicroRNA-205 inhibits Src-mediated oncogenic pathways in renal cancer, Cancer Res 71 (2011), 2611–2621.

16.

Mikhaylova

Stratton

Hall

Kellner

Ehmer

Drew

Gallo

Plas

Biesiada

and Meller

, VHL-Regulated MiR-204 Suppresses Tumor Growth through Inhibition of LC3B-Mediated Autophagy in Renal Clear Cell Carcinoma, Cancer Cell 21 (2012), 532–546.

17.

Tsukigi

Bilim

Yuuki

Ugolkov

Naito

Nagaoka

Kato

Motoyama

and Tomita

, Re-expression of miR-199a suppresses renal cancer cell proliferation and survival by targeting GSK-3beta, Cancer Lett 315 (2012), 189–197.

18.

Chen

Wang

Ruan

Han

Zhao

Xiao

Shi

Wang

Chen

Yang

and Zhang

, miR-141 is a key regulator of renal cell carcinoma proliferation and metastasis by controlling EphA2 expression, Clin Cancer Res 20 (2014), 2617–2630.

19.

Anders

and Huber

, Differential expression analysis for sequence count data, Genome Biology 11 (2010), R106.

20.

Benjamini

and Hochberg

, Controlling The False Discovery Rate – A Practical And Powerful Approach To Multiple Testing, Journal of the Royal Statistical Society 57 (1995), 289–300.

21.

Smoot

M.E.

Ono

Ruscheinski

Wang

P.L.

and Ideker

, Cytoscape 2.8: new features for data integration and network visualization, Bioinformatics 27 (2011), 431–432.

22.

Young

M.D.

Wakefield

M.J.

Smyth

G.K.

and Oshlack

, Gene ontology analysis for RNA-seq: accounting for selection bias, Genome Biol 11 (2010), R14.

23.

Kanehisa

and Goto

, KEGG: kyoto encyclopedia of genes and genomes, Nucleic Acids Res 28 (2000), 27–30.

24.

Krasnov

G.S.

Dmitriev

A.A.

Melnikova

N.V.

Zaretsky

A.R.

Nasedkina

T.V.

Zasedatelev

A.S.

Senchenko

V.N.

and Kudryavtseva

A.V.

, CrossHub: a tool for multi-way analysis of The Cancer Genome Atlas (TCGA) in the context of gene expression regulation mechanisms, Nucleic Acids Res 44 (2016), e62.

25.

Braun

C.J.

Zhang

Savelyeva

Wolff

Moll

U.M.

Schepeler

Orntoft

T.F.

Andersen

C.L.

and Dobbelstein

, p53-Responsive micrornas 192 and 215 are capable of inducing cell cycle arrest, Cancer Res 68 (2008), 10094–10104.

26.

S.N.

Pisitkun

Belleannée

Miller

L.R.

Nelson

Knepper

M.A.

Brown

and Breton

, Proteomic analysis of V-ATPase-rich cells harvested from the kidney and epididymis by fluorescence-activated cell sorting, American Journal of Physiology Cell Physiology 298 (2010), C1326.

27.

H.T.

Kuang

Han

X.Y.

Wang

M.Q.

and Yang

, Biomarker and competing endogenous RNA potential of tumor-specific long noncoding RNA in chromophobe renal cell carcinoma, Onco Targets Ther 9 (2016), 6399–6406.

28.

Matsuda

Kominato

Koideyoshida

Miyamoto

Isshiki

Tsuji

and Yuasa

, PCTAIRE Kinase 3/Cyclin-dependent Kinase 18 Is Activated through Association with Cyclin A and/or Phosphorylation by Protein Kinase A, Journal of Biological Chemistry 289 (2014), 18387–18400.

29.

Bansal

A.D.

Hoffert

J.D.

Pisitkun

Hwang

Chou

C.L.

Boja

E.S.

Wang

and Knepper

M.A.

, Phosphoproteomic profiling reveals vasopressin-regulated phosphorylation sites in collecting duct, J Am Soc Nephrol 21 (2010), 303–315.

30.

Goto

Kurozumi

Nohata

Kojima

Matsushita

Yoshino

Yamazaki

Ishida

Ichikawa

Naya

and Seki

, The microRNA signature of patients with sunitinib failure: regulation of UHRF1 pathways by microRNA-101 in renal cell carcinoma, Oncotarget 7 (2016), 59070–59086.

31.

Getz

Miska

E.A.

Alvarez-Saavedra

Lamb

Peck

Sweet-Cordero

Ebert

B.L.

Mak

R.H.

Ferrando

A.A.

Downing

J.R.

Jacks

Horvitz

H.R.

and Golub

T.R.

, MicroRNA expression profiles classify human cancers, Nature 435 (2005), 834–838.

32.

Chen

Teng

Liu

Cao

Ding

Wang

Chen

and An

, Identification of biological targets of therapeutic intervention for clear cell renal cell carcinoma based on bioinformatics approach, Cancer Cell Int 16 (2016), 16.

33.

Dimitrieva

Schlapbach

and Rehrauer

, Prognostic value of cross-omics screening for kidney clear cell renal cancer survival, Biol Direct 11 (2016), 68.

34.

Serada

Fujimoto

Terabe

Iijima

Shinzaki

Matsuzaki

Ohkawara

Nezu

Nakajima

Kobayashi

Plevy

S.E.

Takehara

and Naka

, Serum leucine-rich alpha-2 glycoprotein is a disease activity biomarker in ulcerative colitis, Inflamm Bowel Dis 18 (2012), 2169–2179.

35.

Lynch

Fay

Meehan

Bryan

Watters

K.M.

Murphy

D.M.

and Stallings

R.L.

, MiRNA-335 suppresses neuroblastoma cell invasiveness by direct targeting of multiple genes from the non-canonical TGF-beta signalling pathway, Carcinogenesis 33 (2012), 976–985.

36.

Zhong

Zhao

Lang

Jiang

and Li

, Stable knockdown of LRG1 by RNA interference inhibits growth and promotes apoptosis of glioblastoma cells in vitro and in vivo , Tumour Biol 36 (2015), 4271–4278.

37.

Wang

Abraham

McKenzie

J.A.G.

Jeffs

Swire

Tripathi

V.B.

Luhmann

U.F.O.

Lange

C.A.K.

Zhai

Arthur

H.M.

Bainbridge

Moss

S.E.

and Greenwood

, LRG1 promotes angiogenesis by modulating endothelial TGF-beta signalling, Nature 499 (2013), 306–311.

38.

Ren

Liu

Kang

Pang

Jia

Zhang

Liang

Zhao

Zou

Yuan

and Li

, Chromophobe renal cell carcinoma with and without sarcomatoid change: a clinicopathological, comparative genomic hybridization, and whole-exome sequencing study, Am J Transl Res 7 (2015), 2482–2499.

39.

Wala

S.J.

Karamchandani

J.R.

Saleeb

Evans

Ding

Ibrahim

Jewett

Pasic

Finelli

Pace

Lianidou

and Yousef

G.M.

, An integrated genomic analysis of papillary renal cell carcinoma type 1 uncovers the role of focal adhesion and extracellular matrix pathways, Mol Oncol 9 (2015), 1667–1677.

40.

Vila

M.R.

Nicolas

Morote

and Meseguer

, Increased glyceraldehyde-3-phosphate dehydrogenase expression in renal cell carcinoma identified by RNA-based, arbitrarily primed polymerase chain reaction, Cancer 89 (2000), 152–164.

41.

Ligeza

Marona

Gach

Lipert

Miekus

Wilk

Jaszczynski

Stelmach

Loboda

Dulak

Branicki

Rys

and Jura

, MCPIP1 contributes to clear cell renal cell carcinomas development, 20 (2017), 325–340.

42.

Grampp

Schmid

Salama

Lauer

Kranz

Platt

J.L.

Smythies

Choudhry

Goppelt-Struebe

Ratcliffe

P.J.

Mole

D.R.

and Schodel

, Multiple renal cancer susceptibility polymorphisms modulate the HIF pathway, https://www.ncbi.nlm.nih.gov/pubmed/?term=Multiple+renal+cancer+susceptibility+polymorphisms+modulate+the+HIF+pathway PLoS Genet 13 (2007), e1006872.

43.

Purdue

M.P.

Johansson

Zelenika

Toro

J.R.

Scelo

Moore

L.E.

Prokhortchouk

Kiemeney

L.A.

Gaborieau

Jacobs

K.B.

Chow

W.H.

Zaridze

Matveev

Lubinski

Trubicka

Szeszenia-Dabrowska

Lissowska

Rudnai

Fabianova

Bucur

Bencko

Foretova

Janout

Boffetta

Colt

J.S.

Davis

F.G.

Schwartz

K.L.

Banks

R.E.

Selby

P.J.

Harnden

Berg

C.D.

Hsing

A.W.

Grubb

R.L.

, 3rd Boeing

Vineis

Clavel-Chapelon

Palli

Tumino

Krogh

Panico

Duell

E.J.

Quiros

J.R.

Sanchez

M.J.

Navarro

Ardanaz

Dorronsoro

Khaw

K.T.

Allen

N.E.

Bueno-de-Mesquita

H.B.

Peeters

P.H.

Trichopoulos

Linseisen

Ljungberg

Overvad

Tjonneland

Romieu

Riboli

Mukeria

Shangina

Stevens

V.L.

Thun

M.J.

Diver

W.R.

Gapstur

S.M.

Pharoah

P.D.

Easton

D.F.

Albanes

Weinstein

S.J.

Virtamo

Vatten

Hveem

Njolstad

Tell

G.S.

Stoltenberg

Kumar

Koppova

Cussenot

Benhamou

Oosterwijk

Vermeulen

S.H.

Aben

K.K.

van der Marel

S.L.

Wood

C.G.

Mazur

A.M.

Boulygina

E.S.

Chekanov

N.N.

Foglio

Lechner

Gut

Heath

Blanche

Hutchinson

Thomas

Wang

Yeager

Fraumeni

J.F.

, Jr. Skryabin

K.G.

McKay

J.D.

Rothman

Chanock

S.J.

Lathrop

and Brennan

, Genome-wide association study of renal cell carcinoma identifies two susceptibility loci on 2p21 and 11q13.3, Nat Genet 43 (2011), 60–65.

44.

Banerjee

Basu

Datta

Gasser

Waaga-Gasser

A.M.

and Pal

, The heme oxygenase-1 protein is overexpressed in human renal cancer cells following activation of the Ras-Raf-ERK pathway and mediates anti-apoptotic signal, J Biol Chem 286 (2011), 33580–33590.

45.

Zheng

W.X.

Yan

Xue

G.J.

Qin

W.J.

Wang

F.L.

Qin

Tian

C.J.

and Yuan

J.L.

, Heme oxygenase-1 is a predictive biomarker for therapeutic targeting of advanced clear cell renal cell carcinoma treated with sorafenib or sunitinib, Onco Targets Ther 8 (2015), 2081–2088.

46.

Lin

B.S.

Pan

Y.Q.

Sun

H.L.

Peng

H.X.

X.X.

and Wang

S.K.

, MicroRNA expression profiles predict progression and clinical outcome in lung adenocarcinoma, Onco Targets Ther 9 (2016), 5679–5692.

47.

Maltseva

D.V.

Galatenko

V.V.

Samatov

T.R.

Zhikrivetskaya

S.O.

Khaustova

N.A.

Nechaev

I.N.

Shkurnikov

M.U.

Lebedev

A.E.

Mityakina

I.A.

Kaprin

A.D.

Schumacher

and Tonevitsky

A.G.

, miRNome of inflammatory breast cancer, BMC Res Notes 7 (2014), 871.

48.

Williams

A.A.

Higgins

J.P.

Zhao

Ljunberg

and Brooks

J.D.

, CD 9 and vimentin distinguish clear cell from chromophobe renal cell carcinoma, BMC Clin Pathol 9 (2009), 9.

49.

White

N.M.

Masui

Desouza

L.V.

Krakovska

Metias

Romaschin

A.D.

Honey

R.J.

Stewart

Pace

Lee

Jewett

M.A.

Bjarnason

G.A.

Siu

K.W.

and Yousef

G.M.

, Quantitative proteomic analysis reveals potential diagnostic markers and pathways involved in pathogenesis of renal cell carcinoma, Oncotarget 5 (2014), 506–518.

50.

Weng

Guo

Pal

S.K.

Jin

J.M.

Nelson

R.A.

Onami

S.H.

J.J.

Ruel

N.H.

Wilczynski

S.P.

Gao

Covarrubias

Figlin

R.A.

Weiss

L.M.

and Wu

, Identification of a 4-microRNA signature for clear cell renal cell carcinoma metastasis and prognosis, PLoS One 7 (2012), e35661.

51.

Giridhar

K.V.

Tian

Tschannen

M.R.

Zhu

Huang

C.C.

Kilari

Kohli

and Wang

, Plasma exosomal miRNAs-based prognosis in metastatic kidney cancer, Oncotarget 8 (2017), 63703–63714.

52.

Stassar

M.J.

Devitt

Brosius

Rinnab

Prang

Schradin

Simon

Petersen

Kopp-Schneider

and Zoller

, Identification of human renal cell carcinoma associated genes by suppression subtractive hybridization, Br J Cancer 85 (2001), 1372–1382.

53.

Huang

Tang

Huang

Zhong

and Yang

, Key pathways and genes controlling the development and progression of clear cell renal cell carcinoma (ccRCC) based on gene set enrichment analysis, Int Urol Nephrol 46 (2014), 539–553.

54.

Tyburczy

M.E.

Jozwiak

Malinowska

I.A.

Chekaluk

Pugh

T.J.

C.L.

Nussbaum

R.L.

Seepo

Dzik

Kotulska

and Kwiatkowski

D.J.

, A shower of second hit events as the cause of multifocal renal cell carcinoma in tuberous sclerosis complex, Hum Mol Genet 24 (2015), 1836–1842.

55.

Malouf

G.G.

Yao

Gao

Xiong

Comperat

Couturier

Molinie

Escudier

Camparo

Doss

D.J.

Thompson

E.J.

Khayat

Wood

C.G.

Teh

B.T.

Weinstein

and Tannir

N.M.

, Next-generation sequencing of translocation renal cell carcinoma reveals novel RNA splicing partners and frequent mutations of chromatin-remodeling genes, Clin Cancer Res 20 (2014), 4129–4140.

56.

Hidaka

Seki

Yoshino

Yamasaki

Yamada

Nohata

Fuse

Nakagawa

and Enokida

, Tumor suppressive microRNA-1285 regulates novel molecular targets: aberrant expression and functional significance in renal cell carcinoma, Oncotarget 3 (2012), 44–57.

57.

Hogel

Miikkulainen

Bino

and Jaakkola

P.M.

, Hypoxia inducible prolyl hydroxylase PHD3 maintains carcinoma cell growth by decreasing the stability of p27, Mol Cancer 14 (2015), 143.

58.

Schrodter

Braun

Syring

Klumper

Deng

Schmidt

Perner

Muller

S.C.

and Ellinger

, Identification of the dopamine transporter SLC6A3 as a biomarker for patients with renal cell carcinoma, Mol Cancer 15 (2016), 10.

59.

Garcia-Donas

Leandro-Garcia

L.J.

Gonzalez Del Alba

Morente

Alemany

Esteban

Arranz

J.A.

Climent

M.A.

Gallardo

Castellano

D.E.

Bellmunt

Mellado

Puente

Moreno

Font

Hernando

Robledo

and Rodriguez-Antona

, Prospective study assessing hypoxia-related proteins as markers for the outcome of treatment with sunitinib in advanced clear-cell renal cell carcinoma, Ann Oncol 24 (2013), 2409–2414.

Key miRNAs and target genes played roles in the development of clear cell renal cell carcinoma

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1. Introduction

Table 1 Clinical information about these 787 patients with CCRCC

2.1 Datasets

2.2 Differential expression analysis of miRNAs and genes

2.3 MiRNA-mRNA expression correlation analysis

Table 2 Top 15 Go terms for target genes in CCRCC

2.5 Survivability analysis of differentially expressed miRNAs and target genes

3. Results

3.1 MiRNA and mRNA expression signature

3.2 MiRNA-mRNA interactions

3.3 Functional annotation

3.4 Survivability analysis

4. Discussion

5. Conclusions

Footnotes

Acknowledgments

Conflict of interest

Abbreviations

Supplementary data

References

Table 1
Clinical information about these 787 patients with CCRCC

Table 2
Top 15 Go terms for target genes in CCRCC