Expression signature of six‐snoRNA serves as novel non‐invasive biomarker for diagnosis and prognosis prediction of renal clear cell carcinoma

Abstract Increasing evidence has verified that small nucleolar RNAs (snoRNAs) play significant roles in tumorigenesis and exhibit prognostic value in clinical practice. In the study, we analysed the expression profile and clinical relevance of snoRNAs from TCGA database including 530 ccRCC (clear cell renal cell carcinoma) and 72 control cases. By using univariate and multivariate Cox analysis, we established a six‐snoRNA signature and divided patients into high‐risk or low‐risk groups. We found patients in high‐risk group had significantly shorter overall survival and recurrence‐free survival than those in low‐risk group in test series, validation series and entire series by Kaplan‐Meier analysis. We also confirmed this signature had a great accuracy and specificity in 64 clinical tissue cases and 50 serum samples. Then, depending on receiver operating characteristic curve analysis we found the six‐snoRNA signature was an superior indicator better than conventional clinical factors (AUC = 0.732). Furthermore, combining the signature with TNM stage or Fuhrman grade were the optimal indicators (AUC = 0.792; AUC = 0.800) and processed the clinical applied value for ccRCC. Finally, we found the SNORA70B and its hose gene USP34 might directly regulate Wnt signalling pathway to promote tumorigenesis in ccRCC. In general, our study established a six‐snoRNA signature as an independent and superior diagnosis and prognosis indicator for ccRCC.


| INTRODUC TI ON
Renal cell carcinoma (RCC) is one of the most common malignant tumours in urological malignancies, accounting for about 90% of all adult renal tumours. It is estimated that approximately 73 820 new cases and 14 770 deaths of RCC would occur in the United States during 2019. 1 ccRCC, accounting for 90% of RCC, represents the most common histologic subtype and aggressive form. 2 So far, there are mainly two problems in clinical treatment of ccRCC. On the one hand, it is difficult to diagnose ccRCC early, especially for patients with small renal masses (pT1a, ≤4 cm). 3 On the other hand, there are no specific prognostic indicators for predicting overall survival (OS) and recurrence-free survival (RFS) of ccRCC patients largely depended on stage, grade, tumour size and so on. 4 Although some biomarkers, such as VHL, 5 BAP1 6 mutations and overexpression of AHNAK2 7 and SLC6A3, 8 have been discovered, few markers have been validated their diagnostic or predictive power in clinical practice. 9 Therefore, it is imperative to identify more sensitive and reliable biomarker for ccRCC.
Small nucleolar RNAs (snoRNAs) are a kind of non-coding RNA with 60-300 nucleotides in length, 10 involving in guiding site-specific post-transcriptional modification of rRNAs, tRNAs, snoRNAs and sn-RNAs. 11 Small nucleolar RNAs are primarily classified into H/ACA box and C/D box snoRNAs based on their structure and main function. H/ ACA box snoRNAs guide pseudouridylation of nucleotides, whereas C/D box is responsible for 2′-O-methylation. [11][12][13] The defects in ribosome maturation and function can destroy important protein synthesis processes and lead to diseases, especially cancer. 14 In recent years, new and previously unrecognized functions of snoRNAs have been discovered in various cancers, revealing that the snoRNAs might associate with tumorigenesis. SNORA42, 15 SNORD33, SNORD66, SNORD76, 16 SNORD78 17 and SNORD114. 1 18  Despite the emerging knowledge about the role of snoRNAs in cancer, the clinical relevance of snoRNAs in ccRCC has not been investigated systematically. In this study, we identified a six-snoRNA signature as an independent and specific predictor to predict the prognosis of ccRCC patients from TCGA database and validated its clinical application value in a subset of ccRCC tissue and serum by qRT-PCR. Hence, the six-snoRNA signature might provide a prospective prognostic biomarker set and potential therapeutic targets for ccRCC.

| ccRCC datasets preparation
The TCGA ccRCC tumour and paired adjacent tissue samples RNAseq gene expression data (HTSeq-Counts) and corresponding clinical data were downloaded from The Cancer Genome Atlas of the National Cancer Institute (TCGA, http://cance rgeno me.nih.gov).
After removal of the nine samples without survival study and clinical information, a total of 530 ccRCC patients and 72 control cases were analysed in the present study. The downloaded clinical data included age, gender, TNM stage, Fuhrman grade, haemoglobin levels and so on for ccRCC. Simultaneously, we also downloaded RNA-seq gene expression data (HTSeq-Counts) and corresponding clinical data for patients with papillary renal cell carcinoma (KIRP, 281 patients and 32 control cases included), chromophobe renal cell carcinoma (KICH, 65 patients and 24 control cases included) and bladder cancer (411 patients and 18 control cases included).

| Gene selection and gene signature building
First, the differentially expressed snoRNAs were screened using 'edgeR' package. Then, the 530 ccRCC cases in TCGA data sets were randomly assigned into test series (N = 371) and internal validation series (N = 159) at ratio 7:3 (Table S1). By univariable and multivariable Cox regression analysis, we established a prognostic signature and validated it in the internal validation series and entire validation series. A snoRNA-based risk score model formula was conducted in the test series as follows: where n was the number of predicted snoRNAs, Coe i indicated the coefficient of the ith snoRNA in multivariable Cox regression analysis, and EV i represented the expression value of the ith snoRNA. The snoRNAs with Coe i < 0 were considered as protective factors, whereas those with Coe i > 0 were considered as risky factors.

| Patients and clinical specimens
We recruited 32 pairs of matched fresh-frozen ccRCC and adjacent normal tissue, and also serum from 25

| RNA isolation
The recruited serum sample was allowed to coagulate at room temperature for 30 min and then centrifuged at 1000 g for 10 min to take the supernatant. The supernatant was centrifuged again for 12 000 g for 15 min to remove all cellular components and immediately stored at −80°C. For tissue and serum RNA isolation, 1mL TRIzol (Invitrogen) was added to 50 mg of tissue or 200 μL of serum and total RNA extraction according to the manufacturer's instructions. 20 Purified RNA was quantified using NanoDrop 2000 (Thermo Scientific). In general, the yield was 0.3-1 μg/mg tissue or 0.1-0.5 ng RNA/mL serum.

| qRT-PCR
Reverse transcription was performed from 500 ng of total RNA using the ReverTra Ace qPCR RT Kit. 37°C for 15 min, followed by reverse transcriptase inactivation at 85°C for 5 min was utilized. The cDNA was used for PCR or stored at −80°C immediately. The expression of snoR-NAs was analysed by custom TaqMan assays (Applied Biosystems), using the QuantStudio™ 3 Flex Real-Time PCR System (Applied Biosystems) and using the following condition: 95°C for 1 min, followed by 40 cycles of 95°C for 15 s, 60°C for 30 s and 72°C for 1 min. Primers were as follows: SNORA2 forward (5′-ATTCAAGGCCAGCAGTTTGC-3′) and

| GO term and KEGG enrichment analysis
The clusterProfiler package was implemented to further explore the biological function of snoRNAs including biological process (BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. 22 P < .05 was considered a significant enrichment.

| Construction of nomogram predictive model
The 'rms', 'nomogramEx' and 'regplot' R package were used to construct nomogram. The nomogram was used to predict the survival rate of ccRCC with multiple indicators. 23 The total points were obtained by plus the points of each prognostic parameters, and patients with higher total points had worse survival. Separating capacity of the nomogram was tested by Harrell's concordance index (C-index).

| Statistical analysis
Overall survival differences between patients in high-risk and lowrisk groups were estimated by Kaplan-Meier survival curve and calculated using the log-rank test. The receiver operating characteristic (ROC) curve was made to determine the sensitivity and specificity of the snoRNA signature through calculating the area under curve (AUC). The Cancer Cell Line Encyclopedia (CCLE) data were downloaded to explore the expression of snoRNAs (http://www.broad insti tute.org/ccle). 24 The GEPIA (http://gepia.cancer-pku.cn) website was used to analyse expression correlation between two genes. 25 We compared two groups using t test for numerical variables, one-

| Identifying a six-snoRNA signature as a potential prognostic marker for ccRCC
Although some small nucleolar RNAs (snoRNAs) had prognostic value in various cancer, 15,16 the expression level and clinical significance of snoRNAs in ccRCC have not been established systematically. Therefore, we first determined the expression level of snoRNAs by analysing TCGA database and identified 43 significantly differential expression snoRNAs between 530 ccRCC cases and 72 control cases (|Log FC|>1, P < .05, Figure 1A and   Figure 1F).
To evaluate the prognostic utility of the six-snoRNA signature in ccRCC, patients were divided into high-risk group or low-risk group by the median risk score as the cut-off point. By Kaplan-Meier analysis, we found patients in high-risk group had significantly shorter OS than those in low-risk group (P < .0001) ( Figure 1G). Besides, similar results were also observed in validation series and entire TCGA series ( Figure 1G). Furthermore, we also analysed the RFS and found patients in high-risk group had significantly shorter RFS than those in low-risk group in test series (P < .0001), validation series (P = .0071) and entire series (P < .0001) ( Figure S1A) What is more, patients with recurrence had higher risk score than those without recurrence ( Figure S1B).

| High-risk score is associated with advanced TNM, higher Fuhrman grade and low haemoglobin level
To further comprehensively investigate whether there was a relationship between the risk score and pathological characteristics, patients were arranged according to their risk score. The results showed obviously asymmetric distribution of the Fuhrman grade, TNM stage and haemoglobin level (Figure 2A). We found elevated risk score was positively associated with advanced TNM stage, higher Fuhrman grade and lower haemoglobin level. However, age, gender, VHL status, chemotherapy, immunotherapy and target molecular therapy showed no difference in the distribution (Figure 2A).
We further compared the risk score of patients separated by clinical F I G U R E 2 Relationship between the predictive signature risk score and clinicopathologic characteristics. A, The clinicopathologic information of patients in TCGA database, arranged by the increasing risk score. The distribution of risk score in patients stratified by age (B), gender (C), VHL status (D), therapy type (E), TNM stage (F), Fuhrman grade (G) and haemoglobin level (H). *P values were measured by unpaired t test. *P < .05, **P < .01, ***P < .001, ****P < .0001. #, one-way ANOVA for different pathological stages. ###P < .001, ####P < .0001 characteristics. Some clinical characteristics were not associated with our risk scores, such as gender status, VHL status and therapy type ( Figure 2C-E). However, the risk score was highly related to age, TNM stage, Fuhrman grade and haemoglobin level ( Figure 2B, 2-H). To illustrate, the risk score was higher in TNM stages III and IV, compared with TNM stages I and II ( Figure 2F). Contrasted to the Fuhrman I and II stages, the risk score was higher in Fuhrman III and Fuhrman IV stages ( Figure 2G). Furthermore, the risk score was higher in low haemoglobin level ( Figure 2H). We also found that along with the ccRCC stage developed, the risk score was higher ( Figure 2F,2). These results demonstrated that elevated risk score indicated advanced TNM stage, higher Fuhrman grade and lower haemoglobin level.

| Prognostic value of the six-snoRNA signature is independent of conventional clinical factors
To further appraise the predictive effect of the six-snoRNA signature and other clinicopathologic characteristics on survival status, we performed univariable and multivariable Cox analysis to determine whether the six-snoRNA signature could be an independent risk factor for evaluating prognosis of ccRCC patients. Univariable Cox analysis revealed that age, the six-snoRNA signature, TNM stage, Fuhrman grade and haemoglobin level were significantly related to the patients' survival status (Table S5). Multivariable Cox regression showed that the six-snoRNA signature, age, TNM stage, Fuhrman grade and haemoglobin were independent prognostic factors (Table S6). In addition, the six-snoRNA signature was also an independent risk factor for RFS by univariable and multivariable Cox regression analysis (Tables S7 and S8). Therefore, to further assess the robustness of the six-snoRNA signature, we performed data stratification analysis to estimate whether the six-snoRNA signature exhibited prognostic value within the same clinical factor. The Fuhrman grading system was an important independent and most widely used predictive factor in renal cell carcinoma, based on assessment of the uniformity of nuclear size, nuclear shape and nuclear prominence. [26][27][28] Hence, we first stratified patients into Fuhrman I and II groups, III group and IV group. Then, we divided these patients into high-risk and low-risk groups again. As results shown in Figure 3A, patients in high-risk group had significantly shorter OS than those in low-risk group no matter in Fuhrman I and II groups or in Fuhrman III group and IV group (P < .0001).
Furthermore, we separately analysed Fuhrman I and II groups, III group and IV group, and found after the six-snoRNA signature separated them into high-risk and low-risk groups, patients in high-risk group had shorter OS ( Figure 3B-D). In addition, patients in highrisk group had shorter RFS than those in low-risk group which was the same with OS, although there was no statistically significant in Fuhrman IV subgroup ( Figure S1C).

For ccRCC, according to the Memorial Sloan-Kettering Cancer
Center (MSKCC) criteria, serum haemoglobin less than the lower limit of normal (LLN) was one of the adverse prognostic factors. 29,30 Hence, we first stratified patients into normal and low-level haemoglobin groups. Then, we divided these patients into high-risk and low-risk groups again. As results shown in Figure 3E, patients in high-risk group had significantly shorter OS than those in lowrisk group no matter in normal or in low-level haemoglobin group (P < .0001). Furthermore, we separately analysed normal and lowlevel haemoglobin groups, and found after the six-snoRNA signature separated them into high-risk and low-risk groups, patients in highrisk group had shorter OS ( Figure 3F-G). In addition, patients in highrisk group had shorter RFS than those in low-risk group which was the same with OS ( Figure S1C).
Lastly, we first stratified patients into TNM I and II stage groups and TNM III and IV stage groups according to TNM stage. Then, we divided these patients into high-risk and low-risk groups again. As results shown in Figure 3H,  Figure 3N). This result demonstrated our risk score system was effective and would be a good index parameter in prognostic prediction.

| Validating the prognostic value of the six-snoRNA signature in ccRCC tissue and serum
To further validate the prognostic value of the six-snoRNA signature for ccRCC, we, respectively, measured these six-snoRNA expressions in ccRCC patients and healthy people by qRT-PCR. Then, we calculated and compared the risk score of patients in tissue and serum, respectively, and found that the risk score was significantly higher in patients' tissue and serum, compared with normal controls (P < .0001, P = .0018, Figure 4A). Furthermore, no matter in tissue or in serum samples, patients in high-risk group exhibited higher expression of risky snoRNAs, whereas patients in low-risk group exhibited higher expression of protective snoRNAs ( Figure 4B-M). Taken together, these results suggested that the six-snoRNA signature exhibited its diagnostic value as biomarker for ccRCC patients.

| The snoRNA methylation and WNT pathway potentially function ccRCC tumorigenesis
The biological function of snoRNAs has not been investigated clearly. is a key regulator of gene expression, and some snoRNAs were significantly associated with their CNVs in various cancers. 19,32,33 Therefore, we evaluated relevance of CNV and these snoRNA expressions. The result showed that the expression levels of SNORA2, SNORD12B, SNORA70B, SNORD93 and SNORD116-2 were positively correlated with their CNVs in ccRCC, respectively ( Figure 5B).
In addition, DNA methylation is a common epigenetic mechanism that regulates gene expression and studies have reported that the methylation level of snoRNAs was involved in regulating snoRNA expression. 19,34,35 Thus, we further evaluated the correlation of these snoRNAs with DNA methylation using snoRic database. The results showed SNORD12B and SNORD93 had significant correlation with their DNA methylation ( Figure 5C). To illustrate, we have validated SNORD12B was the risk factor in ccRCC, and the expression of SNORD12B was negatively correlated with the methylation level of probe cg18598146. Hence interestingly, probe cg18598146 methylation of SNORD12B was protective factor for ccRCC ( Figure 5C).
Similar result was also observed in SNORD93. These results demonstrated that the methylation level of snoRNAs might affect prognosis of ccRCC by regulating the expression level of snoRNAs.
In eukaryotes, the complexes between snoRNAs and ribonucleoproteins (RNPs) are called small nucleolar RNPs (snoRNPs). 36 Previous report suggested that RNPs were strongly correlated with snoRNAs and demonstrated their co-activation and synergy involves in cancer progression by affecting the processes of ribosome and protein translation. 19 Therefore, we further investigated the correlations between these six snoRNAs and their corresponding RNPs. We identified 159 RNPs that were associated with these six snoRNAs ( Figure 5D). To further explore the potential function of these snoRNAs, we first performed protein-protein interaction analysis for these snoRNPs by STRING tool (Figure 5E,G). Then, we performed GO and KEGG enrichment analysis by clusterProfiler package to analyse high-risk group-related snoRNP genes genes and low-risk group related-snoRNP genes, respectively. We found most high-risk group-related snoRNP genes genes were similar to low-risk group-related snoRNP genes (Table S10). The GO analysis results showed that high-risk-related snoRNPs ( Figure 5H) and low-risk-related snoRNPs ( Figure 5F) were mainly enriched in 'mRNA processing', 'RNA splicing' and so on. The KEGG analysis results showed that high-risk-related snoRNPs ( Figure 5H) and low-risk-related snoRNPs ( Figure 5F) were mainly enriched in 'Splicesome', 'Transcription misregulation in cancer', 'cell cycle', 'Wnt signalling pathway' and so on.
These results suggested that there was no significant difference between high-risk-related snoRNPs and low-risk-related snoRNPs.
In mammals, the majority of snoRNAs are encoded within introns of protein-coding or non-coding genes, which are called 'host genes'. 37 An alteration of snoRNA expression may result from host genes through co-transcription. 38 To understand the potential F I G U R E 4 The expressions of six snoRNAs and risk score in ccRCC patients' tissues and serums. A, The comparisons of six-snoRNA risk score for ccRCC tissues and serums, respectively. The expressions of six snoRNAs in tissues (B-G) and serums (H-M). P values were measured by unpaired t test. N, ROC analysis of the sensitivity and specificity of the overall survival prediction by the six-snoRNA risk score in tissues and serums. O, 3-D PCA plot analysis of the six-snoRNA expression data in tissues and serums samples. Abbreviations; TN: normal tissue, TT: tumour tissue, SN: normal serum, ST: tumour serum. P, Kaplan-Meier curves for TCGA patients with KICH, KIRP and bladder cancer by the six-snoRNA risk score. Q, ROC analysis of the sensitivity and specificity of the overall survival prediction for patients with KICH, KIRP and bladder cancer by the six-snoRNA risk score regulated mechanism of snoRNA expression, we further analysed the distribution and function of host genes for these snoRNAs. We observed that host genes of SNORA2, SNORA59B and SNORA70B were protein-coding genes and host genes of SNORD12B, SNORD93 and SNORD116-2 were non-coding genes ( Figure 6A). Then, we analysed the correlation between snoRNAs and their corresponding host genes, and observed that SNORA2, SNORD12B, SNORA59B, SNORA70B and SNORD116-2 were highly correlated with their host genes, respectively ( Figure 6B-F). In addition, we also analysed differential expression of host genes between low-risk and high-risk groups ( Figure 6G). The results showed that SLC47A1 and SNHG14, host genes of protective SNORA59B and SNORD116-2, had higher expression in low-risk group and ZFAS1 and USP34, host genes of risky SNORD12B and SNORA70B, had higher expression in high-risk group. These results suggested that the host genes of snoRNA may play the same protective or risky role as well as snoRNA itself. To explore the potential function of these protein-coding host genes, we first performed protein-protein interactions analysis for these host genes by STRING tool (Figure 6H,J). Then, we performed GO and KEGG enrichment analysis by clusterProfiler package to analyse high-risk group-related host genes and low-risk group-related host genes, respectively. The GO analysis results showed that

F I G U R E 5
The function analysis of these six snoRNAs. A, Distribution of different types of snoRNAs. B, The correlation between snoRNAs and copy number variations. C, The correlation between snoRNAs and methylation and multivariable cox analysis of methylation site in ccRCC. D, Network of snoRNP genes highly associated with six snoRNAs. Protein-protein interactions analysis of snoRNP genes highly correlated with low-risk snoRNAs (E) and high-risk snoRNAs (G) by STRING tool. Biological process analysis and KEGG analysis of snoRNP genes highly correlated with low-risk snoRNAs (F) and high-risk snoRNAs (H) by clusterProfiler package low-risk-related host genes were mainly enriched in 'peptidyl-lysine modification', 'histone modification' and so on ( Figure 6I) and high-risk-related host genes were mainly enriched in 'Wnt signalling pathway', 'cell-cell signalling by wnt' and so on ( Figure 6K). The KEGG analysis results showed that low-risk-related host genes were mainly enriched in 'Lysine degradation', 'Cushing's syndrome' and so on

F I G U R E 6
The function analysis of the host genes of these six snoRNAs. A, The host genes of six snoRNAs. (B-F) The correlation between host genes and snoRNAs. G, The differential expression of snoRNAs' host genes between high-risk and low-risk groups. Protein-protein interaction analysis of snoRNP genes highly correlated with low-risk snoRNAs (H) and high-risk snoRNAs (J) by STRING tool. Biological process analysis and KEGG analysis of snoRNP genes highly correlated with low-risk snoRNAs (I) and high-risk snoRNAs (K) by clusterProfiler package.
(L) The correlation between SNORA70B and CTNBB1, and between SNORA70B host gene USP34 and CTNBB1, MYC, TCF4 and TCF7L2 ( Figure 6I), and high-risk-related host genes were mainly enriched in 'Ribosome', 'Wnt signalling pathway' and so on ( Figure 6K). Both GO and KEGG analysis results showed that high-risk-related host genes were mainly enriched in 'Wnt signalling pathway'. Hence, we further analysed correlation between SNORA70B, its host gene USP34 and Wnt signalling pathway-related genes such as CTNNB1, MYC, TCF4 and TCF7L2 ( Figure 6L). The results showed that SNORA70B was positively related to CTNNB1 and USP34 was positively related to CTNNB1, MYC, TCF4 and TCF7L2, suggesting SNORA70B and its host gene USP34 might play significant roles in ccRCC tumorigenesis through 'Wnt signalling pathway'.

| D ISCUSS I ON
Clear cell renal cell carcinoma is the most common subtype among renal cell carcinoma. 2,39 In the past few decades, great progress has been made from a non-specific immune approach to targeted therapy (against VEGF, PDGF) and now to novel immunotherapy with immune-checkpoint inhibitors. 31 At present, indolent and aggressive tumours cannot be distinguished depending on TNM staging system, which mainly relies on anatomical information without biological characteristics. Although APEX1 has been reported diagnosis value of ccRCC, its clinical practical prospective was still a long way. 40 In recent years, the potential of snoRNA as biomarkers has been graduated recognized. 15 There is still a desert of specific diagnostic indicators in ccRCC Gong et al have reported snoRNAs were specifically overexpression in ccRCC, implying the potential value of snoRNAs as biomarkers. In our study, we found the six-snoRNA signature was an independent risk factor for OS and RFS in ccRCC (Tables S6 and S8), and high six-snoRNA signature expression indicated poor OS and RFS. In addition, we also compared the performance between conventional clinical-pathological characteristics and our six-snoRNA signature.
Intriguingly, we found six-snoRNA signature was as good as TNM stage and much better than Fuhrman grade and haemoglobin level.
Besides, we further integrated the six-snoRNA signature with TNM stage or Fuhrman grade and found their potentially clinical applied value. More significantly, we confirmed the six-snoRNA signature was related to clinical characteristics in tissue, especially TNM stage and Fuhrman grade.
Minimal invasion, easy method for detection, low cost and convenient census are the advantages of serum-based biomarkers. The previous study has reported snoRNAs were stably present and reliably detectable in serum and suggested snoRNAs could be as novel non-invasive diagnostic biomarkers for osteoarthritis. 42 However, the expression of snoRNA in ccRCC serum has not been studied.
Hence, we further investigated the six-snoRNA signature's stability in serum to evaluate the clinical potential value in ccRCC. In our study, we found the six-snoRNA signature expressed stably in serum, suggesting serum snoRNAs may serve as novel non-invasive biomarkers for ccRCC. The results were similar to above TCGA data set analysis, verifying the feasibility and validity of the six-snoRNA signature as molecular marker. Interestingly, according to the results of ROC and 3-D PCA, we found the six-snoRNA signature performance in tissue was better than that in serum. The possible reasons for these results may be as follows: (a) Although snoRNA itself was stable, it was not that high level in serum. Therefore, our detection Hypermethylation is characteristic of most ccRCCs. 43 On the one hand, the C/D box snoRNAs exert a promotion role in tumorigenesis by regulating rRNA 2′-O-methylation. 44,45 SNORD12B and SNORD93 in our selected snoRNAs were C/D box snoRNAs and exhibited a tumorigenic effect in ccRCC. On the other hand, snoR-NAs exist methylation site and have poor prognosis in KIRC, so is snoRNA methylation correlated with better survival? 19 In this study, we found high expression of risky factors SNORD12B and SNORD93 was negatively correlated with methylation sites cg18598146, cg04907244 and cg22407942, respectively, and these methylation sites were associated with better survival in ccRCC. In addition, Ferreira et al 46 suggested that the host gene-associated 5'CpG islands of SNORA59B were hypermethylated in colorectal cancer cells.
Hence, the specific function of methylation in snoRNAs regulating ccRCC is paradox and further investigation is needed.
WNT family genes play important roles in human organogenesis and tumorigenesis. Moreover, they were involved in renal development and initiation of several renal diseases including kidney malignancy. 47 In our study, host gene USP34 of SNORA70B was mainly involved in regulating WNT signalling pathway.
Interestingly, we did not found SNORA70B-related snoRNPs, which was the most studied mechanism, participated in WNT pathway.
Lasted studies have reported that snoRNAs could directly bind to functional protein to promote tumorigenesis. 48,49 Therefore, we inferred that USP34 or SNORA70B might bind WNT pathway-related protein to activate it.
In general, we identified a six-snoRNA signature as an independent and specific indicator to diagnose and predict prognosis of ccRCC patients, providing a prospective diagnostic and prognostic biomarker and potential therapeutic targets for ccRCC.

CO N FLI C T S O F I NTE R E S T
The authors declare no potential conflicts of interest.

DATA AVA I L A B I L I T Y S TAT E M E N T
The data sets used and analysed during the current study are available from the corresponding author on reasonable request.