SNORA33 promotes clear cell renal cell carcinoma development and resistance to sunitinib through triggering the JAK/STAT pathway

SNORA33 promotes clear cell renal cell carcinoma development and resistance to sunitinib through triggering the JAK/STAT pathway | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article SNORA33 promotes clear cell renal cell carcinoma development and resistance to sunitinib through triggering the JAK/STAT pathway Jiajia Sun, Shuo Zhao, Qinglong Du, Qinzheng Chang, Wei Guo, Lin Yang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6241591/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Accumulating evidence has confirmed that snoRNAs exert a role in a variety of cancer, however, less known in ccRCC. This study was aimed at elucidating the role and mechanism of snoRNAs in the tumorigenesis and progression of ccRCC. The snoRNAs expression matrices were obtained from the public TCGA and SNORic databases. The Kaplan-Meier analysis and Cox univariate and multivariate analyses confirmed the prognostic value of SNORA33 in ccRCC. A series of in vitro experiments were conducted to explore the functional role of SNORA33 in ccRCC. GSEA and western blot were used to explore and validate the involved mechanisms. SNORA33 was highly expressed in patients with ccRCC and was correlated with poor prognosis. The findings of in vitro experiments indicated that SNORA33 was capable of promoting the proliferation, invasion, migration, and resistance to sunitinib in ccRCC. SNORA33 is capable of attaining these effects through regulating the JAK/STAT signaling pathway. Biological sciences/Cancer Health sciences/Biomarkers Health sciences/Urology SNORA33 clear cell renal cell carcinoma sunitinib jak/stat pathway Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction In 2022, an estimated 79,000 people (50,290 men and 28,710 women) in the US will be diagnosed with kidney cancer, and 13,920 people will be expected to die from the disease (8,960 men and 4,960 women)[ 1 ]. Renal cell carcinoma (RCC) is a common malignancy of the urinary system, accounting for about 90% of kidney cancers[ 2 ]. RCC encompasses a spectrum of subtypes, with ccRCC arising from proximal curved renal tubules representing the most prevalent form, accounting for approximately 70% of cases[ 3 ]. The insidious onset and lack of specific clinical symptoms in the early stage of ccRCC may result in misdiagnosis [ 4 ]. Currently, the treatment of ccRCC is primarily based on a combination of surgical intervention and the administration of targeted pharmaceutical agents. However, approximately 30% of patients are diagnosed with metastases during the follow-up period, and approximately 10% die of disease progression within five years of surgery[ 5 ]. The targeted agents such as sunitinib and nivolumab are also limited in clinical application due to resistance[ 2 ]. It is therefore imperative that new reliable tumor biomarkers which are prognostically valuable are developed in order to facilitate the diagnosis and treatment of ccRCC. The central dogma of gene expression dictates the unidirectional flow of genetic information from DNA-mRNA-proteins[ 6 ]. Consequently, previous researchers have concentrated their efforts on protein-coding genes and their transcripts, as well as messenger RNAs (mRNAs). However, in recent times, non-coding RNAs (ncRNAs) have become a popular study for gene regulation, because they play an important role in regulating various biological processes in various diseases, especially cancers development[ 6 ]. In particular, studies have demonstrated that the aberrant expression levels of ncRNAs, including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), are closely associated with tumourigenesis, development, recurrence and prognosis[ 7 – 9 ]. At present, there is a growing interest in the potential of ncRNAs as regulators of tumourigenesis. Small nucleolar RNAs (snoRNAs) constitute a class of non-coding RNAs, of 60 to 300 nucleotides in length, and are classified into two categories: box C/D snoRNAs and box H/ACA snoRNAs[ 10 ]. Box C/D small nucleolar ribonucleic acids bind conserved core box C/D small nucleolar ribosonucleoproteins (snoRNPs), thereby directing the 2′-O-ribose methylation of ribosomal RNAs or small nuclear RNAs box. The box H/ACA small nucleolar RNAs bind conserved core box H/ACA snoRNPs and catalyse pseudouridylation[ 11 ]. Consequently, in previous research, snoRNAs were frequently designated as "housekeeping genes" due to their pivotal function in rRNA maturation[ 12 ]. The latest research has uncovered a previously unidentified function of snoRNAs in regulating tumor cell fate and oncogenesis in various cancers. For instance, SNORA23 has been shown to have anti-tumour activity in hepatocellular carcinoma (HCC) through inhibition of the PI3K/AKT/mTOR pathway[ 13 ]. Recently, Yi and colleagues demonstrated that SNORA42 enhanced the viability, migration, and epithelial-mesenchymal transition (EMT) of prostate cancer cells, and was correlated with a poor prognosis in prostate cancer[ 14 ]. Furthermore, another study demonstrated that SNORA21 was significantly upregulated in colorectal cancer and predicted poor prognosis in CRC patients[ 12 ]. And given their small size and stability, snoRNAs are increasingly being recognized as potential biomarkers for cancers and therapeutic targets. However, to date, only a limited number of snoRNA genes has been fully identified as being associated with prognosis in ccRCC, and the related molecular mechanisms remain largely unknown. The occurrence and development of malignant tumors might involve multiple signaling pathways. The janus kinase (JAK) signal transducer and activator of transcription (JAK/STAT) pathway represents an evolutionarily conserved mechanism of transmembrane signal transduction, enabling cells to communicate with the external[ 15 ]. The abnormal activation of JAK/STAT signaling has been recognized in various immune-mediated conditions and cancers, such as melanomas, glioblastomas, as well as head, neck, lung, pancreatic, breast, rectal, and prostate cancers[ 15 ]. Studies have demonstrated that activation of the JAK/STAT3 signaling pathway augments epithelial-mesenchymal transition, thereby giving rise to an increase in malignant and metastatic potential, facilitating the transformation of cancer stem cells, and inducing cancer chemotherapy resistance[ 16 ]. Moreover, the upregulation of the JAK/STAT pathway not only mediates resistance to radiation therapy or cytotoxic agents but also governs the response of tumor cells to molecularly targeted and immunomodulatory drugs[ 17 – 19 ]. However, the correlation between the JAK/STAT signaling pathway and snoRNAs has rarely been reported in ccRCC. In the present study, we found that SNORA33 was a potential oncogenic snoRNA and correlated with poor prognosis in ccRCC patients. The carcinogenic potential was validated through a series of experimental investigations. Furthermore, we have elucidated its potential involvement in the emergence of resistance to sunitinib in ccRCC patients. All of these bear a close relationship with the JAK/STAT signaling pathway. Our results provided a potential novel target for the treatment and biomarker for prognostication of ccRCC. 2. Materials and Methods 2.1 Cell culture and transfection The 786-O and A498 ccRCC cancer cells were obtained from CellScource China. The cells were utilised within 15 passages for each designed experiment. The cell culture media comprised 10% fetal bovine serum (ExCell Bio, China), 1% penicillin and streptomycin (KeyGen Biotech, China). All cell lines were incubated at 37°C in 5% CO2. All cell cultures were routinely assessed for Mycoplasma contamination. The SNORA33 overexpression plasmid and small interfering RNA (siRNA) were obtained from GenePharma (Shanghai, China) for the purpose of facilitating SNORA33 overexpression (OE-SNORA33) and knockdown (si-SNORA33). Quantitative real-time PCR (qRT-PCR) was performed to evaluate the expression levels of SNORA33, following the manufacturer's instructions. And the sequences are listed in supplementary file. 2.2 Cell viability measurement Cell proliferation was detected by using the Cell Counting Kit-8 assay. The CCK-8 assay (Beyotime, Shanghai, China) was employed to assess cell proliferation and cell viability. 786-O and A498 cells were seeded into a 96-well plate (1500 cells/well) 24 hours after transfection. When the cells were cultivated for 24, 48, and 72 hours, 10 µL of CCK-8 reagent was added to each well, followed by a 2-hour incubation. The absorbance was then measured at a wavelength of 450 nm using a microplate reader (Thermo Fisher Scientific, Waltham, MA, USA). When exploring the role of SNORA33 in resistance to sunitinib, triplicate wells were treated with varying concentrations of the sunitinib (ranging from 0 to 32 µmol/L) in media, and the cells were incubated for an additional 48 hours. A graph was plotted on a coordinate axis, with the concentration of sunitinib on the x-axis and cell viability on the y-axis, to determine the IC50 value. 2.3 Cell colony formation assay Following a 24-hour transfection period, ccRCC cells were plated in 6-well plates at a density of 1,000 cells per well. The culture medium was refreshed every three days. After a cultivation period of 10 to 14 days, the cell colonies were fixed with 4% paraformaldehyde for 30 minutes, stained with 0.1% crystal violet, and subsequently counted for colony formation in each well. 2.4 Transwell assay In summary, 24-well Transwell plates were employed for the assessment of cell invasion and migration. In the cell migration assay, 5×10⁴ ccRCC cells were seeded into the upper chambers of the Transwell in 200 µl serum-free DMEM, while the lower chamber was filled with DMEM supplemented with 10% FBS. Following a 24-hour incubation at 37°C, non-migrating cells were removed from the upper side of the chamber with the aid of a cotton swab. Migrating cells were fixed with 95% ethanol for 10 minutes and subsequently stained with 1% crystal violet for an additional 5 minutes. Then, images were captured and the number of invading cells was quantified under a microscope at 400x magnification. The invasive cells were imaged and quantified using the same methodology previously outlined. 2.5 Cell apoptosis analysis Cells from each experimental group were stained utilizing the Annexin V-FITC Apoptosis Detection kit to identify apoptotic cells. Flow cytometry analysis, conducted on an ACEA Bio instrument, was employed to quantify the percentage of apoptotic cells. All experiments were conducted in triplicate to ensure reproducibility and statistical reliability. 2.6 Real-time quantitative polymerase chain reaction Total RNA was extracted using TRIzol reagent (CWBIO, Beijing, China). The concentration of RNA was quantified with a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, USA). cDNA synthesis was performed using the PrimeScript RT kit (TaKaRa, Japan). RT-PCR was employed to evaluate the expression of SNORA33 following the manufacturer's instructions (SYBR Green Master Mix, Vazyme). The expression levels of the target gene were normalized relative to the U6 mRNA levels [ 20 ]. The relative RNA expression levels were calculated using the 2-ΔΔCt method. Table S1 provides the sequences of all PCR primers utilized in this study. 2.7 Western blot Western blotting was conducted in accordance with established protocols from our prior studies [ 21 ]. The experiment was conducted using commercially available antibodies. β-actin (mouse, 1:1000, Santa Cruz Biotechnology) was utilized as the internal control. The primary antibodies utilized in this study included Bax (1:1000; Proteintech), JAK1 (1:2000; Abcam), STAT3 (1:2000; Abcam) and EPOR (1:1000; Abcam). 2.8 Bioinformatics analysis We obtained the snoRNA expression matrix for TCGA-ccRCC patients from the SNORic database ( http://bioinfo.life.hust.edu.cn/SNORic/ ), and retrieved the clinical data for TCGA-ccRCC from UCSC Xena ( https://xenabrowser.net/datapages/ ). Perform a logarithmic transformation of the snoRNA expression data to facilitate subsequent analyses. Furthermore, we acquired a matched normal tissue dataset from the TCGA ( https://www.cancer.gov/tcga ) database for ccRCC patients to conduct differential analysis between ccRCC and corresponding normal tissues. The study cohort included 532 ccRCC tissue samples and 72 normal kidney tissue samples. A total of 793 valid SNORNA genes were included in the study. The gene expression data of ccRCC used for validation cohort were obtained from GSE167573 ( http://www.ncbi.nlm.nih.gov/geo ) and the International Cancer Genome Consortium (ICGC) ( https://icgc.org/ ). The accession number for the ICGC is RECA-EU, which encompasses 91 individuals with available follow-up information. IMmotion151, a phase III, randomized controlled trial was used to investigate the association between the SNORA33 expression and resistance to the chemotherapy drug, sunitinib, in patients with ccRCC [ 22 ]. 2.9 Gene set enrichment analysis GSEA was conducted on the SNORA33 expression matrix for both the high-risk and low-risk groups using the GSEA software (version 4.1.0). The Hallmark gene set was utilized as a reference to identify the distinct Hallmarks between the high-risk and low-risk groups. Similarly, use the Kyoto Encyclopedia of Genes and Genomes (KEGG) database to investigate the pathways associated with SNORA33 in clear cell renal cell carcinoma (ccRCC). The identification of pathways significantly associated with SNORA33 was based on the normalized enrichment score (NES) > 1, with screening criteria set at a normalized P-value < 0.05 and a false discovery rate (FDR) q-value < 0.25. 3.0 Immune-related analysis To assess the infiltration of 24 immune cell types, single-sample Gene Set Enrichment Analysis (ssGSEA) was employed, utilizing established immune signatures previously documented in the literature [ 23 ]. Utilizing ssGSEA, we quantified the respective scores for each patient based on the expression profiles of hallmark genes. The correlation between SNORA33 and the expression of immune cell markers was validated through the analysis of genomic variants using TIMER ( https://cistrome.shinyapps.io/timer/ ). CIBERSORT was employed to analyze and compare the differences in immune infiltrating cells between the two risk groups. 3.1 Statistics analysis Statistical analyses were conducted utilizing SPSS 24.0 (Chicago, IL, USA) and GraphPad Prism version 9.0 (San Diego, CA, USA). Univariate Cox regression analysis was conducted to identify independent prognostic indicators for patients, followed by multivariate Cox regression analysis based on the characteristics selected from the univariate analysis. Survival probabilities were estimated using the Kaplan–Meier method and evaluated with a log-rank test. The nomogram was developed by integrating SNORA33 with relevant clinical characteristics. To assess the accuracy of the nomogram, calibration curves were utilized to evaluate the concordance between the predicted probabilities and the actual outcomes. Statistical comparisons between the two groups were conducted using either the student's t-test or Mann-Whitney U test. The Spearman correlation test was employed to assess the relationship between the two datasets. The assays conducted in this study were performed independently on at least three occasions. Two-sided P values less than 0.05 were deemed statistically significant for all statistical analyses. 3. Results 3.1 Identification of the aberrant snoRNAs in ccRCC from the database To identify differentially expressed snoRNAs, we retrieved the expression profile data of snoRNAs from 72 control tissues and 532 tumor tissues sourced from the TCGA and SNORic databases. The differential expression of snoRNAs between ccRCC patients and normal donors was assessed and illustrated in a volcano plot ( Fig. 1 A ) and a heat map ( Fig. 1 C ) , adhering to the criteria of an adjusted p-value 1. Systematic ordering and visualisation of differentially expressed genes using differential ordering maps ( Fig. 1 B ). Additionally, the prognostic factors influencing overall survival (OS) in patients with ccRCC were evaluated using both univariate and multivariate cox proportional hazards models ( Fig. 1 D and Fig. 1 E ) . As illustrated in Fig. 1 F, the expression level of SNORA33 emerged as an independent prognostic factor for OS (HR = 1.505, 95% CI = 1.188–1.907, P = 0.001); and it was the most notable of them all. The remaining meaningful ones are AC073149.1 (HR = 0.714, 95% CI = 0.572–0.891, P = 0.003); AL713899.1 (HR = 1.207, 95% CI = 1.026–1.419, P = 0.023) and SNORD104 (HR = 0.845, 95% CI = 0.721–0.99, P = 0.037). The heatmap indicates that elevated expression levels of SNORA33 and SNORD104 are significantly correlated with advanced clinical stages, whereas the expression of AC073149.1 and AL713899.1 shows no strong association with the clinical variables ( Fig. 1 G ) . 3.2 Correlation between SNORA33 and prognosis in ccRCC patient Kaplan-Meier survival analysis, in conjunction with the Log-rank test, demonstrates that elevated expression of SNORA33 is significantly correlated with reduced overall survival (OS) (p < 0.001, HR = 2.62), disease-specific survival (DSS) (p < 0.001, HR = 3.01), and progression-free interval (PFI) (p < 0.001, HR = 1.75) among patients with ccRCC ( Fig. 2A ). Similarly, we conducted an analysis of the associations between the SNORD104, AL713899.1, AC073149.1 genes and the prognosis of ccRCC patients ( Fig. S1 ). To further assess the prognostic value of the SNORA33 gene in patients with ccRCC, we performed a time-dependent receiver operating characteristic (ROC) analysis. The results showed that the area under the curve (AUC) of the SNORA33 was 0.643, 0.735 and 0.788 for 3-year, 7-year and 9-year OS; respectively. The AUC for disease specific survival (DSS) at 3-year, 7-year, and 9-year intervals was measured at 0.638, 0.777, and 0.819, respectively. The AUCs of the SNORA33 were 0.608, 0.653 and 0.767 for 3-year, 7-year and 9-year progress free interval (PFI), respectively ( Fig. 2B ). Both univariate and multivariate cox regression analyses were conducted to determine whether SNORA33 serves as an independent risk factor for OS and DSS in ccRCC patients (p < 0.0001, p = 0.007, respectively; Table.1–2 ). Multivariate Cox regression analyses identified statistically significant independent predictors, which facilitated the development of two nomograms for the quantitative assessment of significant risks associated with OS and DSS ( Fig. 2C and 2D ). The calibration plot of the nomogram demonstrated improved concordance between the predictions made by the nomogram and the actual observations ( Fig. 2E and 2F ). Meanwhile, our analysis of patient survival across various risk stratifications such as age, tumor stage, metastasis and stage indicated that high SNORA33 expression groups exhibited lower survivability (all p < 0.05, Fig. 3I-P ). To assess the predictive accuracy of SNORA33, our analysis revealed that the expression levels of the SNORA33 gene were significantly elevated in deceased patients within the late-stage ccRCC cohort from the ICGC database (p < 0.05, Fig. S1 ). These results suggested that elevated expression of SNORA33 was associated with a reduction in survival time in patients with ccRCC, indicating its potential as an effective prognostic marker or therapeutic target for this malignancy. Table 1 Uni- and multivariate Cox analyses of prognostic factors for OS in the TCGA cohort Characteristics Univariate analysis Multivariate analysis Hazard ratio (95% CI) P value Hazard ratio (95% CI) P value Age 60 1.791 (1.319–2.432) < 0.001 1.692 (1.105–2.591) 0.016 Pathologic T stage T1&T2 Ref. Ref. T3&T4 3.210 (2.373–4.342) < 0.001 1.395 (0.618–3.146) 0.423 Pathologic N stage N0 Ref. Ref. N1 3.422 (1.817–6.446) < 0.001 1.389 (0.694–2.779) 0.354 Pathologic M stage M0 Ref. Ref. M1 4.401 (3.226–6.002) < 0.001 2.345 (1.391–3.954) 0.001 Pathologic stage Stage I&Stage II Ref. Ref. Stage III&Stage IV 3.910 (2.852–5.360) < 0.001 1.494 (0.594–3.760) 0.394 Histologic grade G1&G2 Ref. Ref. G3&G4 2.665 (1.898–3.743) < 0.001 1.452 (0.876–2.404) 0.148 SNORA33 Low Ref. Ref. High 2.886 (2.083–3.999) < 0.001 2.924 (1.766–4.841) < 0.001 Gender Female Ref. Male 0.924 (0.679–1.257) 0.613 95% CI, 95% Confidence Interval Table 2 Uni- and multivariate Cox analyses of prognostic factors for DSS in the TCGA cohort . Characteristics Univariate analysis Multivariate analysis Hazard ratio (95%CI) P value Hazard ratio (95%CI) P value Age 60 1.351 (0.926–1.971) 0.118 Gender Female Ref. Male 1.183 (0.786–1.781) 0.420 Pathologic T stage T1&T2 Ref. Ref. T3&T4 5.606 (3.697–8.502) < 0.001 1.122 (0.485–2.593) 0.788 Pathologic N stage N0 Ref. Ref. N1 3.864 (1.831–8.157) < 0.001 1.373 (0.634–2.975) 0.422 Pathologic M stage M0 Ref. Ref. M1 9.219 (6.294–13.504) < 0.001 3.150 (1.739–5.708) < 0.001 Pathologic stage Stage I&Stage II Ref. Ref. Stage III&Stage IV 9.937 (5.989–16.486) < 0.001 3.494 (1.154–10.581) 0.027 Histologic grade G1&G2 Ref. Ref. G3&G4 4.850 (2.925–8.043) < 0.001 1.705 (0.856–3.397) 0.129 SNORA33 Low Ref. Ref. High 3.061 (2.012–4.656) < 0.001 2.405 (1.271–4.552) 0.007 95% CI, 95% Confidence Interval 3.3 Expression of SNORA33 in ccRCC and its correlation with clinicopathological characteristics As illustrated in Fig. 3C and 3D , based on TCGA, GSE167573 and ICGC ccRCC cohort, the level of SNORA33 was markedly elevated in cancerous tissues in comparison to those observed in normal tissues and paired samples (p < 0.001, p < 0.05, p < 0.01 and p < 0.01 respectively). Subsequently, a receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the biomarker. The AUC for SNORA33 was 0.768, demonstrating a sensitivity of 58.2% and specificity of 91.7% ( Fig. 3B ). Furthermore, differential expression of SNORA33 was also identified among cancer tissues classified by varying living statue, T stages, metastasis and pathologic stage but not to gender, age and lymph node staging ( Fig. 3A and 3E-3H ). The supplementary figures present a comprehensive analysis of the expression levels of the SNORD104, AL713899.1, AC073149.1 genes in ccRCC tumors and normal tissues, along with their associations with clinical characteristics ( Fig. S2 ). 3.4 GSEA and pathway correlation analysis The genes that were found to be differentially expressed between the high and low expression subgroups of SNORA33 will be employed in a subsequent functional enrichment analysis ( Fig. 4A ). Gene Set Enrichment Analysis (GSEA) was performed to elucidate the pathways associated with the SNOR33 gene. And HALLMARK_IL6_JAK_STAT3_SIGNALING , KEGG_JAK_STAT3_SIGNALING_PATHWAY exhibited a significant positive correlation with the SNORA33 gene (p.adj < 0.05, NES = 1.34, FDR < 0.01; p.adj < 0.05, NES = 1.14, FDR < 0.01, respectively; Fig. 4B and 4C ). Prior researches had also demonstrated that this pathway exerted an oncogenic role in ccRCC[ 24 , 25 ]. The lasso-cox regression was carried out on all genes within the HALLMARK_IL6_JAK_STAT3_SIGNALING pathway to identify those that significantly influence the prognosis of ccRCC patients ( Fig. 4D ). We also examined the expression of these genes in TCGA for ccRCC and normal tissues (Fig. S3) , and evaluated the correlation between SNORA33 expression and those of the other genes. Additionally, we analyzed the scatter distribution, survival status, and risk gene expression associated with SNORA33 ( Fig. 4E and 4F ). As shown in Fig. 4G-4J , we observed a robust positive correlation between the expression levels of the SNORA33 and the erythropoietin receptor (EPOR) gene based on the ccRCC cohorts sourced from TCGA and ICGC databases (p < 0.001, R = 0.529; p < 0.001, R = 0.503, respectively). And among the ccRCC subpopulation characterized by high SNORA33 expression, we observed a concordantly significant upregulation of the EPOR gene. Western blot analysis also showed that overexpression of SNORA33 in ccRCC cells increased the levels of JAK1, STAT3 and EPOR ( Fig. 4K and 4L ). However, knockdown of SNORA33 in 786-O and A498 cells reduced the levels of JAK1, STAT3 and EPOR ( Fig. 4K and 4L ). Moreover, the EPOR gene was identified as a key regulator that influenced the responsiveness of renal cancer to sunitinib therapy. 3.5 Immune landscape of the SNORA33 To gain a deeper understanding of how SNORA33 affected the immune microenvironment of ccRCC patients, we conducted an extensive analysis focusing on immune-related parameters. Within the TCGA-KIRC cohort, we employed the ssGSEA and CIBERSORT methodologies to determine the immune cell infiltration scores for 24 and 22 unique immune cell types, respectively, for each participant ( Fig. 5D and 5E ). Strikingly, a subset of these cell types demonstrated substantial variations in infiltration levels between patients classified as having high versus low SNORA33 expression, such as T regulatory cells (Tregs), Tgd (T gamma delta) cells, Th17 cells and T helper cells ( Fig. 5D ). Furthermore, we explored the relationship between the levels of immune cells infiltration and the expression of SNORA33, and graphically represented the result using bar charts ( Fig. 5A ). A significant positive correlation was observed between the levels of pro-tumorigenic Treg cells and SNORA33. Conversely, elevated expression levels of SNORA33 are associated with a decrease in the infiltration of Tgd anti-cancer cells and Th17 cells, and this was validated through the analysis of its association with cellular markers (Treg cells markers: FOXP3 and TNFRSF18, Fig. 5B and 5C ). The imbalance between Tregs and Th17 cells had emerged as a critical factor in the progression of cancer [ 26 ]. The ssGSEA scores for the ESTIMATE score, Immune score, and Stromal score of each sample from TCGA ccRCC patients were presented in the form of ssGSEA scores, and box plots were used to show the differences between the high and low SNORA33 subgroups. The results indicated that the subpopulation with elevated SNORA33 expression exhibited higher ESTIMATE and Immune scores, while no significant difference was observed in stromal scores ( Fig. 5F ). Furthermore, high immune scores were strongly associated with poor patient survival ( Fig. 5G ). 3.6 Investigation of SNORA33-related tumour mutations and comprehensive pan-cancer analysis Subsequently, we conducted an analysis of SNOR33 mutations in ccRCC, and we found that the likelihood of a Catenin Beta 1 (CTNNB1) gene mutation is markedly elevated in patients exhibiting high expression of SNORA33 ( Fig. 6A ). Nevertheless, copy number variation of CTNNB1 did not exert a significant influence on the survival outcomes of patients diagnosed with ccRCC (p > 0.05), the expression level of CTNNB1 was closely linked to prognosis, and its high expression often indicated a better prognosis of ccRCC patients (p < 0.001, Fig. 6B and 6C ). Subsequently, we conducted an investigation into the differential expression of SNORA33 in 33 cancerous tissues and normal tissues, and performed a more in-depth analysis of paired samples from 23 of these cancer types ( Fig. 6D and 6E ). The results indicated that SNORA33 demonstrates significant expression differences in BLCA (Bladder Urothelial Carcinoma), CHOL (Cholangiocarcinoma), COAD (Colon Adenocarcinoma), LIHC (Liver Hepatocellular Carcinoma) and STAD (Stomach Adenocarcinoma), as well as in several other malignancies. We selected OS to assess the prognostic significance of SNORA33 across pan-cancer. High levels of SNORA33 expression were linked to poor OS in both adrenocortical carcinoma (ACC) (p = 0.005, HR = 3.27) and kidney renal clear cell carcinoma (KIRC) (p < 0.001, HR = 2.67). In contrast, elevated SNORA33 expression was associated with favorable OS in thymoma (THYM) (p = 0.047, HR = 0.20, Fig. 6F ). In various cancer types, SNORA33 exhibits a close association with Tgd cells, mast cells, macrophages, neytrophils and other immune cell populations ( Fig. 6G ). 3.7 The immune-related gene expression profile of SNORA33 and its implications for drug prediction The expression levels of immune-related genes can serve as indicators of the immune status in different patients and their potential responsiveness to immunotherapy. We conducted a comparative analysis of gene expression across various subgroups within a comprehensive immune-related gene set. The high SNORA33 expression group exhibited elevated expression levels of interferon response-related genes, whereas no significant differences were observed in the expression levels of antigen presentation-related genes (Fig. 7 A-B). Additionally, we observed that certain immunostimulatory molecules, including CD27, CD80, TNFRSF14 and TNFRSF25, exhibited elevated expression levels within the high SNORA33 expression group (Fig. 7 C). Among the immunosuppressive molecules, the expression levels of several immune checkpoint molecules, including CTLA4, PDCD1, TIGIT, IDO2 and SPDL1 were significantly elevated in the high-risk group (Fig. 7 D). In the analysis of SNORA33 co-expression, it was observed that the majority of immune-related genes exhibited positive expression (Fig. 7 E-H). To assess the impact of SNORA33 expression on drug response and identify suitable treatment options, we noted that within the IMmotion151 cohort undergoing sunitinib therapy, the expression level of SNORA33 was significantly lower in patients who complete response (CR) and partial response (PR) compared to those with progressive disease (PD) and stable disease (SD) within the IMmotion151 cohort ( Fig. S3 ). Subsequently, we employed the cell ability assay to further elucidate the impact of SNORA33 on the sensitivity of ccRCC cells to sunitinib and performed a quantitative analysis. The results showed that in comparison to vector 786-O cells, the overexpressed SNORA33 cells demonstrated significantly enhanced cell viability and increased IC50 values under various concentrations of sunitinib treatment (Fig. 7 I). And the cell viability and IC50 values were significantly lower in the si-SNORA33 group compared to the control group (Fig. 7 J). This further indicated a close link between the expression of SNORA33 and resistance to sunitinib. 3.8 SNORA33 promotes the proliferation, invasion and migration of ccRCC cells Building on our previous studies, we identified that the SNORA33 gene plays a significant role in the development and progression of ccRCC. A series of in vitro experiments further substantiated our hypothesis. To investigate the biological function of SNORA33 in ccRCC, we employed small interfering RNA (siRNA) to downregulate its expression and plasmid transfection to enhance its expression (OE-SNORA33). After transfecting 786-O and A498 cells with si-SNORA33 and constructing plasmid-based transfection, the significant reduction and overexpression of SNORA33 validated the successful establishment of these cells ( Fig. 8 A and B) . Then, we investigated the impact of SNORA33 overexpression on ccRCC cells. The Transwell assay demonstrated that SNORA33 overexpression significantly enhanced the invasive and migratory capacities of 786-O and A498 cells (Fig. 8 E and 8 F). The statistical results are illustrated in Fig. 8 I, 8 J and supplemental figure. Furthermore, both CCK-8 and clone formation assay indicated that SNORA33 overexpression markedly promoted the proliferation of ccRCC cells ( Fig. 8 C-D and 8 G-H). Additionally, we focused on the effects of SNORA33 gene knockdown on ccRCC. The CCK-8 assay results demonstrated that cellular proliferation ability was significantly impaired in the si-SNORA33 group relative to the negative control group ( Fig. 9A and 9B ). And Colony formation assays revealed that reduced SNORA33 expression significantly diminished both the number and size of colonies in 786-O cells compared to the Control group ( Fig. 9E and 9F ). Migration and invasion are critical biological characteristics of renal cancer cells. Transwell assay results showed that silencing SNORA33 substantially decreased both invasive and migratory capacities of these cells ( Fig. 9C and 9D ). The statistical results are illustrated in Fig. 9G, 9H and supplemental figure. Flow cytometry analysis revealed that SNORA33 knockdown significantly promoted apoptosis in 786-O and A498 cells ( Fig. 9I ). The results of the western blot analysis indicated that the knockdown of the SNORA33 gene result in elevated expression levels of the pro-apoptotic protein bax ( Fig. 9J and 9K ). In addition, Collectively, these findings suggest that SNORA33 facilitates the proliferation, migration, and invasion of ccRCC cells. 4. Discussion In the past few years, a lot of work has been done to study the expression profiles and prognostic roles of non-coding RNAs, including lncRNAs, microRNA, circRNAs and etc, in different types of cancer. However, other classes of small non-coding RNAs, such as snoRNAs, have been infrequently investigated in this context, particularly within the realm of ccRCC. In the present study, we screened a variety of snoRNAs from the TCGA database, we for the first time discovered that the expression of SNORA33 was significantly higher in ccRCC tissues than in normal kidney tissues, and we further examined the expression of SNORA33 in our ccRCC cells, the result of which was still the case. We postulated that the elevated SNORA33 was correlated with the poor prognosis and lower survival rates, and was associated with an advanced clinical stage and distant metastasis in ccRCC patients. As anticipated, through loss of function and functional acquisition analysis, we have verified that SNORA33 can facilitate the proliferation, invasion, and metastasis of ccRCC cells. This indicates that the expression of SNORA33 could act as a reliable prognostic indicator for ccRCC. Meanwhile, via the cell viability assay, we discovered that the upregulation of SNORA33 might facilitate the resistance of ccRCC cells to sunitinib. Mechanistically, we first demonstrated that SNORA33 is significantly positively correlated with the JAK/STAT3 pathway in ccRCC cells. More importantly, the upregulation of SNORA33 lead to the development of ccRCC and resistance to sunitinib, which can be accomplished by activating the JAK/STAT3 pathway. SnoRNAs constitute a class of non-coding RNAs that exert critical regulatory functions in various physiological and pathological events[ 27 ]. Previous studies have indicated that snoRNAs possess more stable characteristics and are technically more amenable to enrichment and detection compared with miRNAs, ctDNA (circulating tumor DNA), and exosome[ 28 ]. Consequently, in recent years, an increasing amount of attention has been devoted to the role of snoRNAs in the development of cancer[ 29 ]. On the one hand, snoRNAs exert their influences via classical modes of action such as ribosome binding and catalyzing pseudouridylation, among others. SNORD89 modifies Bim through 2'-O-methylation, influencing the Bim-mediated Bcl-2/Bax signaling pathway and facilitating the progression of endometrial cancer[ 30 ]. SNORA56-mediated pseudouridylation of 28 S rRNA suppresses ferroptosis and promotes colorectal cancer proliferation by enhancing GCLC translation[ 31 ]. SNORD88C-guided 2′-O-methylation of 28S rRNA regulates SCD1 translation to suppress autophagy and facilitate growth and metastasis in non-small cell lung cancer[ 32 ]. On the other hand, snoRNAs not only play a role in the modification of rRNA, but it could also bind to specific tumor-related proteins and directly participate in the molecular regulatory network of cancer. SNORD17 interacts with NPM1 and MYBBP1A to suppress p53 activation and thereby drive the progression of HCC[ 33 ]. SnoRNAs are capable of binding to PARP-1, stimulating PARP-1 self-modification and facilitating the interaction between PARP-1 and DDX21. This subsequently promotes rDNA transcription and cell growth in breast cancer [ 34 ]. Due to its significant role in tumor cell survival, proliferation and invasion, JAK/STAT signaling has emerged as a preferred target for drug development and cancer therapy[ 35 ]. The JAK/STAT signaling pathway was initially identified in the research of interferon-related transcription activation. Subsequently, over approximately 20 years, a general outline of the components and pathogenesis of the JAK/STAT signaling pathway was gradually accomplished [ 15 ]. More than 50 types of cytokines, such as hormones, growth factors, and chemokines, have been demonstrated to be correlated with JAK/STAT3 activation, resulting in cell differentiation, metabolism, survival, as well as the development, recurrence, anticancer drug resistance, and cancer stem cell generation of malignant tumors [ 36 ]. Among them, recent studied have indicated that the EPOR, as a constituent of the cytokine receptor superfamily, plays a crucial role in the activation of the JAK/STAT pathway, facilitating the advancement of malignant melanoma and head and neck squamous cell carcinoma (HNSCC), among others [ 37 , 38 ]. From the results of our bioinformatics analysis indicated that the expression of SNORA33 is significantly and positively correlated with EPOR, which was further verified by western blot analysis in ccRCC cells. Meanwhile, GSEA analysis and western blot demonstrated that in ccRCC, SNORA33 resulted in a significant upregulation of the JAK/STAT pathway. Wu et al. have demonstrated that EPOR was related to the growth, invasion, and survival of RCC cells and was capable of counteracting the anti-tumor effects of sunitinib on ccRCC cells [ 39 ]. These results suggest that SNORA33 may exert a promoting effect on ccRCC by participating in EPOR/JAK/STAT axis and may potentially serve as a therapeutic target for ccRCC. Limitations of the study Here, we have determined that SNORA33 promotes the progression and resistance of ccRCC by influencing the expression of key proteins within the JAK/STAT pathway. Nevertheless, the mechanism through which SNORA33 exerts its effects, whether by means of modification or direct binding to pathway proteins, has not been fully clarified, further research is required to elucidate this. There is still much room for improvement in our research. 5. Conclusion In summary, we investigate the H/ACA box snoRNA SNORA33, which is upregulated in ccRCC cells and associated with poor prognosis. SNORA33 could function as a reliable prognostic marker for ccRCC. Our findings demonstrate that SNORA33 promotes the development and progression of ccRCC by modulating the JAK/STAT3 signaling pathway, and enhancing resistance to sunitinib. SNORA33 has the potential to serve as a novel biomarker for the prognosis and treatment of ccRCC in the future, offering new strategies for diagnosis and therapy. Abbreviations AUC ACC BLCA area under the curve adrenocortical carcinoma Bladder Urothelial Carcinoma CTNNB1 Catenin Beta 1 ccRCC Clear Cell Renal Cell Carcinoma CHOL COAD Cholangiocarcinoma Colon Adenocarcinoma CR DSS complete response disease specific survival EPOR erythropoietin receptor GSEA HNSCC Gene set enrichment analysis head and neck squamous cell carcinoma JAK janus kinase KIRC kidney renal clear cell carcinoma OS Overall Survival PFI snoRNPs snoRNAs Progress Free Interval small nucleolar ribosonucleoproteins small nucleolar RNAs STAT signal transducer and activator of transcription TCGA The Cancer Genome Atlas Declarations Funding Statement This study was supported by the National Natural Science Foundation of China (Grant Number 82102999). Author contributions Formal analysis, Qinglong Du, Shuo Zhao and Jiajia Sun; Funding acquisition, Yidong Fan and Jikai Liu; Methodology, Jiajia Sun and Qinglong Du; Project administration, Jikai Liu; Software, Qinzheng Chang; Supervision, Yidong Fan and Jikai Liu; Visualization, Qinglong Du and Qinzheng Chang; Writing – original draft, Qinglong Du, Shuo Zhao, Jiajia Sun, Wei Guo, Lin Yang and Laiyuan Qiu; Writing – review & editing, Nianzhao Zhang, Yidong Fan and Jikai Liu. All authors have read and agreed to the published version of the manuscript. Resource availability Lead contact Further information and requests for reagents and resources should be directed to and will be fulfilled by the lead contact, Jikai Liu ( [email protected] ). Materials availability This study did not generate new unique reagents. Acknowledgements The authors appreciate study investigators and staff who participated in this study. Data Availability Statement All datasets used in this study were downloaded from the TCGA, GEO, ICGC and SNORic databases and the manuscript's Methods section offers comprehensive details on the sources for data downloads. The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare no potential conflicts of interest. Ethics approval and consent to participate This article encompassed no research concerning human or animal samples. Consent for publication We have obtained consent to publish this paper from all the participants of this study. References Siegel, R.L., et al., Cancer statistics, 2022. CA Cancer J Clin, 2022. 72 (1): 7-33. Hsieh, J.J., et al., Renal cell carcinoma. Nat Rev Dis Primers, 2017. 3 : 17009. Ljungberg, B., et al., EAU guidelines on renal cell carcinoma: 2014 update. Eur Urol, 2015. 67 (5): 913-24. 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Sudhakar Reddy, P., et al., Comprehensive evaluation of candidate reference genes for real-time quantitative PCR (RT-qPCR) data normalization in nutri-cereal finger millet [Eleusine Coracana (L.)]. PLoS One, 2018. 13 (10): e0205668. Zhao, S., et al., CTCF-activated FUCA1 functions as a tumor suppressor by promoting autophagy flux and serum α-L-fucosidase serves as a potential biomarker for prognosis in ccRCC. Cancer Cell Int, 2024. 24 (1): 327. Rini, B.I., et al., Atezolizumab plus bevacizumab versus sunitinib in patients with previously untreated metastatic renal cell carcinoma (IMmotion151): a multicentre, open-label, phase 3, randomised controlled trial. Lancet, 2019. 393 (10189): 2404-2415. Hänzelmann, S., R. Castelo, and J. Guinney, GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics, 2013. 14 : 7. Lin, H., et al., Fatty acids metabolism affects the therapeutic effect of anti-PD-1/PD-L1 in tumor immune microenvironment in clear cell renal cell carcinoma. J Transl Med, 2023. 21 (1): 343. Xie, L., et al., TTC13 expression and STAT3 activation may form a positive feedback loop to promote ccRCC progression. PeerJ, 2023. 11 : e16316. Knochelmann, H.M., et al., When worlds collide: Th17 and Treg cells in cancer and autoimmunity. Cell Mol Immunol, 2018. 15 (5): 458-469. Toffano-Nioche, C., D. Gautheret, and F. Leclerc, Revisiting the structure/function relationships of H/ACA(-like) RNAs: a unified model for Euryarchaea and Crenarchaea. Nucleic Acids Res, 2015. 43 (16): 7744-61. Song, J., et al., Clinical significance and prognostic value of small nucleolar RNA SNORA38 in breast cancer. Front Oncol, 2022. 12 : 930024. Liang, J., et al., Small Nucleolar RNAs: Insight Into Their Function in Cancer. Front Oncol, 2019. 9 : 587. Bao, H.J., et al., Box C/D snoRNA SNORD89 influences the occurrence and development of endometrial cancer through 2'-O-methylation modification of Bim. Cell Death Discov, 2022. 8 (1): 309. Xu, C., et al., SNORA56-mediated pseudouridylation of 28 S rRNA inhibits ferroptosis and promotes colorectal cancer proliferation by enhancing GCLC translation. J Exp Clin Cancer Res, 2023. 42 (1): 331. Wang, K., et al., SNORD88C guided 2'-O-methylation of 28S rRNA regulates SCD1 translation to inhibit autophagy and promote growth and metastasis in non-small cell lung cancer. Cell Death Differ, 2023. 30 (2): 341-355. Liang, J., et al., Non-coding small nucleolar RNA SNORD17 promotes the progression of hepatocellular carcinoma through a positive feedback loop upon p53 inactivation. Cell Death Differ, 2022. 29 (5): 988-1003. Kim, D.S., et al., Activation of PARP-1 by snoRNAs Controls Ribosome Biogenesis and Cell Growth via the RNA Helicase DDX21. Mol Cell, 2019. 75 (6): 1270-1285.e14. Groner, B. and V. von Manstein, Jak Stat signaling and cancer: Opportunities, benefits and side effects of targeted inhibition. Mol Cell Endocrinol, 2017. 451 : 1-14. Jin, W., Role of JAK/STAT3 Signaling in the Regulation of Metastasis, the Transition of Cancer Stem Cells, and Chemoresistance of Cancer by Epithelial-Mesenchymal Transition. Cells, 2020. 9 (1). Mirmohammadsadegh, A., et al., Role of erythropoietin receptor expression in malignant melanoma. J Invest Dermatol, 2010. 130 (1): 201-10. Lai, S.Y., et al., Erythropoietin-mediated activation of JAK-STAT signaling contributes to cellular invasion in head and neck squamous cell carcinoma. Oncogene, 2005. 24 (27): 4442-9. Wu, P., et al., The erythropoietin/erythropoietin receptor signaling pathway promotes growth and invasion abilities in human renal carcinoma cells. PLoS One, 2012. 7 (9): e45122. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6241591","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":449017554,"identity":"deb6e559-1ac7-4562-a040-cf9fdafb9f6d","order_by":0,"name":"Jiajia Sun","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Jiajia","middleName":"","lastName":"Sun","suffix":""},{"id":449017555,"identity":"7bfb4b07-1c0f-4c75-b5c4-cfa0b7de8ff4","order_by":1,"name":"Shuo Zhao","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Shuo","middleName":"","lastName":"Zhao","suffix":""},{"id":449017556,"identity":"1174dfd8-97cf-4f86-9088-7846ea8a6bd0","order_by":2,"name":"Qinglong Du","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Qinglong","middleName":"","lastName":"Du","suffix":""},{"id":449017557,"identity":"d9ddfaca-9b9c-4c82-9d64-cff997e40a83","order_by":3,"name":"Qinzheng Chang","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Qinzheng","middleName":"","lastName":"Chang","suffix":""},{"id":449017558,"identity":"0201bf11-1f03-4c51-85cc-260f8783536c","order_by":4,"name":"Wei Guo","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Guo","suffix":""},{"id":449017559,"identity":"0640d922-9fac-46ef-a818-3998b7970abd","order_by":5,"name":"Lin Yang","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Yang","suffix":""},{"id":449017561,"identity":"cab5fd03-35b1-43b5-981e-4c6f7a8370ce","order_by":6,"name":"Laiyuan Qiu","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Laiyuan","middleName":"","lastName":"Qiu","suffix":""},{"id":449017563,"identity":"61048a51-7f44-42f4-88b5-5b30ea3a8cd8","order_by":7,"name":"Nianzhao zhang","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Nianzhao","middleName":"","lastName":"zhang","suffix":""},{"id":449017565,"identity":"bac253de-a058-467f-8cff-22d46bf47b7d","order_by":8,"name":"Yidong Fan","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Yidong","middleName":"","lastName":"Fan","suffix":""},{"id":449017569,"identity":"440400e7-b749-40de-9523-7bb26022d92f","order_by":9,"name":"Jikai Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIie3Rv0oDMRzA8V8JxCXYNVLovUKkoJTD9lXuCKSLaEGQGyOFTH0EHyKr2y/ccEtxPujSo+DkYJeCi5q7VVNudMh3DPmQPz+AWOwfRsFpzIpvRgnkuwJIt8pPkfNB+YS7DRkPz/RebPqQMalWrjFkcrF2b7wXobS9GKW5raUqnEmTayBuy2B2FySsJYx5olTtzOLyRVOZMpAPQcJbwrknt2p7MOXAIrsaMcBch0jSeCKEJ/fHpTPl3OLweJp0n5xl/vmowJPcn0J7EMT2kyXH14W0JZ1Mn4UMkkRX+8PnF3aj/MDH9MZWq6Z+L2ZB8kfdaET//bFYLBb73Q+ljGbDZ8JgYAAAAABJRU5ErkJggg==","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":true,"prefix":"","firstName":"Jikai","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-03-17 06:38:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6241591/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6241591/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82122600,"identity":"b67e1a44-de69-48cb-97d3-467dce204f51","added_by":"auto","created_at":"2025-05-07 03:29:52","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3277113,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScreening and identification of effector genes.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Volcano plot of DEGs between ccRCC patients and normal (|log2 fold change|\u0026gt; 1 and P \u0026lt; 0.05). Significantly upregulated and downregulated genes are depicted as red and green dots, respectively. \u003cstrong\u003e(B)\u003c/strong\u003eThe difference ranking plot shows the results of the difference analysis. \u003cstrong\u003e(C)\u003c/strong\u003eHeat map of expression of DEGs in tumor and normal tissues. Forest maps of univariate Cox regression analysis \u003cstrong\u003e(D)\u003c/strong\u003e and multivariate Cox regression analysis \u003cstrong\u003e(E)\u003c/strong\u003e identified SNORNA genes associated with prognosis in TCGA ccRCC cohort. \u003cstrong\u003e(F)\u003c/strong\u003e A Venn diagram showing the intersection between UniCox regression results and mulCox regression results. \u003cstrong\u003e(G)\u003c/strong\u003e Heatmap of relationship between clinical features and SNORA33, SNORD104, AL713899.1 and AC073149.1. TCGA, The Cancer Genome Atlas; DEGs, differential expression genes.\u003c/p\u003e","description":"","filename":"Fig.1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6241591/v1/46e304bb0dc02a2ce44e74c0.jpg"},{"id":82125248,"identity":"c28369da-2176-4d99-9656-3041997b631a","added_by":"auto","created_at":"2025-05-07 03:45:53","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2561977,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe verification of the influence of SNORA33 gene expression on prognosis.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Analysis of the prognostic significance of SNORA33 expression in ccRCC patients based on TCGA. (OS, left panel; DSS, middle panel; PFI, right panel). \u003cstrong\u003e(B)\u003c/strong\u003eTime-dependent ROC curves to predict OS, DSS and PFI at 3-,7-, and 9-year for patients with ccRCC (OS, left panel; DSS, middle panel; PFI, right panel). The nomogram to predict OS \u003cstrong\u003e(C)\u003c/strong\u003e and DSS \u003cstrong\u003e(D)\u003c/strong\u003e at 1-, 2-, and 3-year for ccRCC patients (OS, up panel; DSS, down panel). Calibration curve of the nomogram to predict OS \u003cstrong\u003e(E)\u003c/strong\u003e and DSS\u003cstrong\u003e (F)\u003c/strong\u003e (at 1-, 2-, and 3-year for ccRCC patients. The C-index for OS and DSS were 0.734 (95% CI: 0.715-0.754) and 0.806(0.789-0.824) respectively. The horizontal axis of the nomogram represents the expected value, while the vertical axis represents the observed value. AUC: area under the curve; C-index,concordance index; CI, confidence level; OS, overall survival; DSS, disease specific survival; PFI, progress free interval.\u003c/p\u003e","description":"","filename":"Fig.2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6241591/v1/0d1a1fc3c056c9236b3235a0.jpg"},{"id":82122599,"identity":"954a951b-c735-46b5-9250-945a71c604d5","added_by":"auto","created_at":"2025-05-07 03:29:52","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2672090,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClinical value of SNORA33 expression in ccRCC patients from TCGA.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Sankey chart shows the relationship between SNORA33, clinical features and prognostic status. \u003cstrong\u003e(B)\u003c/strong\u003e ROC curve of SNORA33 in ccRCC. \u003cstrong\u003e(C)\u003c/strong\u003e Results of differential analysis of SNORA33 expression in paired ccRCC samples. \u003cstrong\u003e(D)\u003c/strong\u003e Results of differential analysis of SNORA33 expression in unpaired ccRCC samples based on TCGA, GSE167573 and ICGC ccRCC cohorts. Box plot revealing the relationship between SNORA33 expression and different clinical indicators. \u003cstrong\u003e(E)\u003c/strong\u003e Gender, \u003cstrong\u003e(F)\u003c/strong\u003ePathologic T stage, \u003cstrong\u003e(G)\u003c/strong\u003e Metastasis, \u003cstrong\u003e(H)\u003c/strong\u003e Pathologic stage. The survival differences between high- and low- risk groups stratified by clinical variables: \u003cstrong\u003e(I)\u003c/strong\u003e, \u003cstrong\u003e(J)\u003c/strong\u003e tumor (T1-2 and T3-4); \u003cstrong\u003e(K)\u003c/strong\u003e, \u003cstrong\u003e(L)\u003c/strong\u003eage (≤ 60 and \u0026gt; 60); \u003cstrong\u003e(M)\u003c/strong\u003e,\u003cstrong\u003e (N)\u003c/strong\u003e stage (stage I-II and stage III-IV); \u003cstrong\u003e(O)\u003c/strong\u003e, \u003cstrong\u003e(P)\u003c/strong\u003e metastasis (M0 and M1). Time: days. TCGA, The Cancer Genome Atlas; ROC, Receiver Operator Characteristic.\u003c/p\u003e","description":"","filename":"Fig.3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6241591/v1/67f8c50a8efe92ae733e4f14.jpg"},{"id":82122606,"identity":"08ff840c-db8e-4463-9fd4-b54c9c8656a9","added_by":"auto","created_at":"2025-05-07 03:29:52","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2914323,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional analyses of SNORA33 in ccRCC.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e The volcano plot shows the significantly differentially expressed genes between the high and low expression groups of SNORA33. \u003cstrong\u003e(B)\u003c/strong\u003eGESA analysis on High SNORA33 vs. Low SNORA33 ccRCC tumor tissues in TCGA database. GSEA enrichment plot showing for HALLMARK_IL6_JAK_STAT3_SIGNALING and \u003cstrong\u003e(C)\u003c/strong\u003e KEGG_ JAK_STAT3_SIGNALING_PATHWAY enriched in High SNORA33 group. \u003cstrong\u003e(D)\u003c/strong\u003eLASSO coefficient profiles and cross-validation via minimum criteria to select significant prognostic related genes from IL6_JAK_STAT3_SIGNALING pathway. \u003cstrong\u003e(E)\u003c/strong\u003eChord plot of the correlation between SNORA33 with prognostic genes of the previously described pathways in ccRCC based on TCGA. \u003cstrong\u003e(F)\u003c/strong\u003e Distribution of the SNORA33 expression, survival status, and expression profiles of twelve genes in the ccRCC based on TCGA. \u003cstrong\u003e(G, I)\u003c/strong\u003e The scatterplot showing Spearman correlation between SNORA33 expression and EPOR gene expression. \u003cstrong\u003e(H, J)\u003c/strong\u003e Boxplots of EPOR expression in the high and low SNORA33 expression ccRCC patients groups. (\u003cstrong\u003eK, L\u003c/strong\u003e) overexpression of SNORA33 in 786-O and A498 cells increased the protein levels of JAK1, STAT3 and EPOR; while knockdown of SNORA33 reduced the levels of JAK1, STAT3 and EPOR compared with the control group. (B) overexpression of SNORD60 could induce p110α, p-AKT, and mTOR protein levels of xenograft tumor in vivo; no significant change in the expression of AKT. GESA, Gene Set Enrichment Analysis; ccRCC, clear cell renal cell carcinoma; NES, normalized enrichment score; FDR, false discovery rate; Survival status: 1: dead, 0: alive.\u003c/p\u003e","description":"","filename":"Fig.4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6241591/v1/76bbe07c8c150ef64b32ecef.jpg"},{"id":82124040,"identity":"85e9c432-1c40-48fb-b10a-7e33deaf2db3","added_by":"auto","created_at":"2025-05-07 03:37:53","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3158270,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune-related analysis of SNORA33 in the TCGA.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Results of ssGSEA of the correlation between the expression of SNORA33 and 24 immune cells. \u003cstrong\u003e(B)\u003c/strong\u003e The scatterplot showing Spearman correlation between SNORA33 expression and ssGSEA score of Treg cells, right graph illustrated the correlation between SNORA33 expression and the markers associated with Treg cells. \u003cstrong\u003e(C)\u003c/strong\u003e The scatterplot showing Spearman correlation between SNORA33 expression and the ssGSEA score of Th17 cells (Left panel) and T helper cells (Right panel) in ccRCC from TCGA database. \u003cstrong\u003e(D)\u003c/strong\u003eComparison of the ssGSEA scores of immune cells between the SNORA33-high and SNORA33-low groups in the TCGA dataset. \u003cstrong\u003e(E)\u003c/strong\u003e Immune cell infiltration landscapes of the two cluster subtypes according to CIBERSORT. \u003cstrong\u003e(F)\u003c/strong\u003eBoxplots of ESTIMATE score, immune score, and stromal score in the high-SNORA33 and low-SNORA33 expression ccRCC patients. \u003cstrong\u003e(G)\u003c/strong\u003e Boxplots of immune score in the live and dead ccRCC groups. ssGSEA, single sample Gene Set Enrichment Analysis; *P\u0026lt;0.05, **P\u0026lt;0.01, ***P\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Fig.5.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6241591/v1/179f4de0954b402cba2a9147.jpg"},{"id":82122602,"identity":"3a8e82be-150d-48df-939d-ee43948dcd95","added_by":"auto","created_at":"2025-05-07 03:29:52","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3634111,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between SNORA33 expression and gene mutation and expression of SNORA33 in pan-cancer.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e The overview of the genomic alternation and relation to the SNORA33 expression in the TCGA ccRCCs. \u003cstrong\u003e(B)\u003c/strong\u003e Analysis of the prognostic significance of CTNNB1 CNV in ccRCC patients. (C) Analysis of the prognostic significance of CTNNB1 expression in ccRCC patients based on TCGA. (OS, left panel; DSS, right panel). \u003cstrong\u003e(D)\u003c/strong\u003e SNORA33 expression in cancer tissues and para-cancer tissues of different types of tumors. \u003cstrong\u003e(E)\u003c/strong\u003e SNORA33 expression in different tumor types compare with normal tissues. \u003cstrong\u003e(F)\u003c/strong\u003e Forest map showing UniCox result for SNORA33 prognosis (OS) in pan-cancer based on TCGA. \u003cstrong\u003e(G)\u003c/strong\u003eHeat map of the relationship between SNORA33 expression and immune cells. CNV: Copy number variation, *p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"FIG.6.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6241591/v1/0709ccf6fa8a98332d52dde9.jpg"},{"id":82122616,"identity":"df882c9c-6eb1-42c3-a8d9-ee3a555428ba","added_by":"auto","created_at":"2025-05-07 03:29:52","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2842212,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of immune-related gene expression profiles and chemotherapeutic response.\u003c/strong\u003e The expression levels of interferon response-related genes \u003cstrong\u003e(A)\u003c/strong\u003e, antigen presentation genes \u003cstrong\u003e(B)\u003c/strong\u003e, stimulatory immune-related genes\u003cstrong\u003e (C)\u003c/strong\u003e, and immunosuppressors \u003cstrong\u003e(D)\u003c/strong\u003e across different subgroups. \u003cstrong\u003e(E-H)\u003c/strong\u003e The heatmap illustrating the co-expression relationship between the above relevant genes and SNORA33 was generated. \u003cstrong\u003e(I)\u003c/strong\u003e The CCK-8 assay was conducted to assess cell viability and determine the IC50 values of OE-SNORA33 and Vector 786-O cells following treatment with various concentrations of sunitinib (0, 2, 4, 8, 16, and 32 nmol/L). \u003cstrong\u003e(J)\u003c/strong\u003e The CCK-8 assay was conducted to assess cell viability and determine the IC50 values of si-SNORA33 and Control 786-O cells following treatment with various concentrations of sunitinib (0, 2, 4, 8, 16, and 32 nmol/L). ns, not significant; *p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Fig.7.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6241591/v1/cd63f3e1560a19dc2d553442.jpg"},{"id":82122607,"identity":"6f4ec7cd-66fb-49e9-b1d2-f29f0d6d999f","added_by":"auto","created_at":"2025-05-07 03:29:52","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":3024118,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of SNORA33 overexpression on ccRCC cells in vitro. \u003c/strong\u003eSNORA33 overexpression enhances cell viability. \u003cstrong\u003e(A, B) \u003c/strong\u003eThe relative expression of SNORA33 was measured by qRT-PCR in 786-O, A498 cell lines and the si-SNORA33 and OE-SNORA33 cell lines constructed.\u003cstrong\u003e (C, D)\u003c/strong\u003eCCK-8 assay, \u003cstrong\u003e(E)\u003c/strong\u003e cells invasion, with scale bars set at 50 μm\u003cstrong\u003e, (F)\u003c/strong\u003ecells migration, with scale bars set at 50 μm, \u003cstrong\u003e(G)\u003c/strong\u003e colony formation assays. *p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Fig.8.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6241591/v1/793250b105a512435d66c10b.jpg"},{"id":82125736,"identity":"8130f0bc-13fc-4cb0-b440-2f65e56a8ee4","added_by":"auto","created_at":"2025-05-07 03:53:53","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":3230734,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of SNORA33 knockdown on ccRCC cells in vitro.\u003c/strong\u003e SNORA33 knockdown reduces cell viability. \u003cstrong\u003e(A, B)\u003c/strong\u003e CCK-8 assay, \u003cstrong\u003e(C)\u003c/strong\u003e cells invasion, \u003cstrong\u003e(D)\u003c/strong\u003e cells migration, with scale bars set at 50 μm, \u003cstrong\u003e(E)\u003c/strong\u003e colony formation assays, \u003cstrong\u003e(F) \u003c/strong\u003eStatistical representation of cloning experiment outcomes, \u003cstrong\u003e(G, H) \u003c/strong\u003eStatistical analysis of TRANSWELL outcomes, \u003cstrong\u003e(I)\u003c/strong\u003e while increased cell apoptosis, \u003cstrong\u003e(J)\u003c/strong\u003eThe Western blot results show that the expression of apoptosis protein Bax was increased in the si-SNORA33, \u003cstrong\u003e(K)\u003c/strong\u003e Statistical analysis chart of WB outcomes. *p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Fig.9.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6241591/v1/c2fb94ce88b446321e3846f6.jpg"},{"id":95797907,"identity":"a80cacef-1aec-41c2-b259-12d6635bc883","added_by":"auto","created_at":"2025-11-13 08:12:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":29269797,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6241591/v1/c4f0f2d6-b759-4026-9eb9-e539285a406f.pdf"},{"id":82122609,"identity":"f9b662a4-c266-4c4f-ae0a-15b7a8e6fe5c","added_by":"auto","created_at":"2025-05-07 03:29:52","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1462163,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-6241591/v1/09427b4bf481a9164d6ce0c7.docx"},{"id":82122621,"identity":"620c4853-6c07-4398-87d2-0d607e793878","added_by":"auto","created_at":"2025-05-07 03:29:53","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16626,"visible":true,"origin":"","legend":"","description":"","filename":"Table.S1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6241591/v1/1d686abdc40a2f441392d64f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"SNORA33 promotes clear cell renal cell carcinoma development and resistance to sunitinib through triggering the JAK/STAT pathway","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn 2022, an estimated 79,000 people (50,290 men and 28,710 women) in the US will be diagnosed with kidney cancer, and 13,920 people will be expected to die from the disease (8,960 men and 4,960 women)[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Renal cell carcinoma (RCC) is a common malignancy of the urinary system, accounting for about 90% of kidney cancers[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. RCC encompasses a spectrum of subtypes, with ccRCC arising from proximal curved renal tubules representing the most prevalent form, accounting for approximately 70% of cases[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The insidious onset and lack of specific clinical symptoms in the early stage of ccRCC may result in misdiagnosis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Currently, the treatment of ccRCC is primarily based on a combination of surgical intervention and the administration of targeted pharmaceutical agents. However, approximately 30% of patients are diagnosed with metastases during the follow-up period, and approximately 10% die of disease progression within five years of surgery[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The targeted agents such as sunitinib and nivolumab are also limited in clinical application due to resistance[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It is therefore imperative that new reliable tumor biomarkers which are prognostically valuable are developed in order to facilitate the diagnosis and treatment of ccRCC.\u003c/p\u003e \u003cp\u003eThe central dogma of gene expression dictates the unidirectional flow of genetic information from DNA-mRNA-proteins[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Consequently, previous researchers have concentrated their efforts on protein-coding genes and their transcripts, as well as messenger RNAs (mRNAs). However, in recent times, non-coding RNAs (ncRNAs) have become a popular study for gene regulation, because they play an important role in regulating various biological processes in various diseases, especially cancers development[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In particular, studies have demonstrated that the aberrant expression levels of ncRNAs, including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), are closely associated with tumourigenesis, development, recurrence and prognosis[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. At present, there is a growing interest in the potential of ncRNAs as regulators of tumourigenesis.\u003c/p\u003e \u003cp\u003eSmall nucleolar RNAs (snoRNAs) constitute a class of non-coding RNAs, of 60 to 300 nucleotides in length, and are classified into two categories: box C/D snoRNAs and box H/ACA snoRNAs[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Box C/D small nucleolar ribonucleic acids bind conserved core box C/D small nucleolar ribosonucleoproteins (snoRNPs), thereby directing the 2\u0026prime;-O-ribose methylation of ribosomal RNAs or small nuclear RNAs box. The box H/ACA small nucleolar RNAs bind conserved core box H/ACA snoRNPs and catalyse pseudouridylation[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Consequently, in previous research, snoRNAs were frequently designated as \"housekeeping genes\" due to their pivotal function in rRNA maturation[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The latest research has uncovered a previously unidentified function of snoRNAs in regulating tumor cell fate and oncogenesis in various cancers. For instance, SNORA23 has been shown to have anti-tumour activity in hepatocellular carcinoma (HCC) through inhibition of the PI3K/AKT/mTOR pathway[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Recently, Yi and colleagues demonstrated that SNORA42 enhanced the viability, migration, and epithelial-mesenchymal transition (EMT) of prostate cancer cells, and was correlated with a poor prognosis in prostate cancer[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Furthermore, another study demonstrated that SNORA21 was significantly upregulated in colorectal cancer and predicted poor prognosis in CRC patients[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. And given their small size and stability, snoRNAs are increasingly being recognized as potential biomarkers for cancers and therapeutic targets. However, to date, only a limited number of snoRNA genes has been fully identified as being associated with prognosis in ccRCC, and the related molecular mechanisms remain largely unknown.\u003c/p\u003e \u003cp\u003eThe occurrence and development of malignant tumors might involve multiple signaling pathways. The janus kinase (JAK) signal transducer and activator of transcription (JAK/STAT) pathway represents an evolutionarily conserved mechanism of transmembrane signal transduction, enabling cells to communicate with the external[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The abnormal activation of JAK/STAT signaling has been recognized in various immune-mediated conditions and cancers, such as melanomas, glioblastomas, as well as head, neck, lung, pancreatic, breast, rectal, and prostate cancers[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Studies have demonstrated that activation of the JAK/STAT3 signaling pathway augments epithelial-mesenchymal transition, thereby giving rise to an increase in malignant and metastatic potential, facilitating the transformation of cancer stem cells, and inducing cancer chemotherapy resistance[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Moreover, the upregulation of the JAK/STAT pathway not only mediates resistance to radiation therapy or cytotoxic agents but also governs the response of tumor cells to molecularly targeted and immunomodulatory drugs[\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, the correlation between the JAK/STAT signaling pathway and snoRNAs has rarely been reported in ccRCC.\u003c/p\u003e \u003cp\u003eIn the present study, we found that SNORA33 was a potential oncogenic snoRNA and correlated with poor prognosis in ccRCC patients. The carcinogenic potential was validated through a series of experimental investigations. Furthermore, we have elucidated its potential involvement in the emergence of resistance to sunitinib in ccRCC patients. All of these bear a close relationship with the JAK/STAT signaling pathway. Our results provided a potential novel target for the treatment and biomarker for prognostication of ccRCC.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Cell culture and transfection\u003c/h2\u003e \u003cp\u003eThe 786-O and A498 ccRCC cancer cells were obtained from CellScource China. The cells were utilised within 15 passages for each designed experiment. The cell culture media comprised 10% fetal bovine serum (ExCell Bio, China), 1% penicillin and streptomycin (KeyGen Biotech, China). All cell lines were incubated at 37\u0026deg;C in 5% CO2. All cell cultures were routinely assessed for Mycoplasma contamination. The SNORA33 overexpression plasmid and small interfering RNA (siRNA) were obtained from GenePharma (Shanghai, China) for the purpose of facilitating SNORA33 overexpression (OE-SNORA33) and knockdown (si-SNORA33). Quantitative real-time PCR (qRT-PCR) was performed to evaluate the expression levels of SNORA33, following the manufacturer's instructions. And the sequences are listed in supplementary file.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Cell viability measurement\u003c/h2\u003e \u003cp\u003eCell proliferation was detected by using the Cell Counting Kit-8 assay. The CCK-8 assay (Beyotime, Shanghai, China) was employed to assess cell proliferation and cell viability. 786-O and A498 cells were seeded into a 96-well plate (1500 cells/well) 24 hours after transfection. When the cells were cultivated for 24, 48, and 72 hours, 10 \u0026micro;L of CCK-8 reagent was added to each well, followed by a 2-hour incubation. The absorbance was then measured at a wavelength of 450 nm using a microplate reader (Thermo Fisher Scientific, Waltham, MA, USA). When exploring the role of SNORA33 in resistance to sunitinib, triplicate wells were treated with varying concentrations of the sunitinib (ranging from 0 to 32 \u0026micro;mol/L) in media, and the cells were incubated for an additional 48 hours. A graph was plotted on a coordinate axis, with the concentration of sunitinib on the x-axis and cell viability on the y-axis, to determine the IC50 value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Cell colony formation assay\u003c/h2\u003e \u003cp\u003eFollowing a 24-hour transfection period, ccRCC cells were plated in 6-well plates at a density of 1,000 cells per well. The culture medium was refreshed every three days. After a cultivation period of 10 to 14 days, the cell colonies were fixed with 4% paraformaldehyde for 30 minutes, stained with 0.1% crystal violet, and subsequently counted for colony formation in each well.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Transwell assay\u003c/h2\u003e \u003cp\u003eIn summary, 24-well Transwell plates were employed for the assessment of cell invasion and migration. In the cell migration assay, 5\u0026times;10⁴ ccRCC cells were seeded into the upper chambers of the Transwell in 200 \u0026micro;l serum-free DMEM, while the lower chamber was filled with DMEM supplemented with 10% FBS. Following a 24-hour incubation at 37\u0026deg;C, non-migrating cells were removed from the upper side of the chamber with the aid of a cotton swab. Migrating cells were fixed with 95% ethanol for 10 minutes and subsequently stained with 1% crystal violet for an additional 5 minutes. Then, images were captured and the number of invading cells was quantified under a microscope at 400x magnification. The invasive cells were imaged and quantified using the same methodology previously outlined.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.5 Cell apoptosis analysis\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eCells from each experimental group were stained utilizing the Annexin V-FITC Apoptosis Detection kit to identify apoptotic cells. Flow cytometry analysis, conducted on an ACEA Bio instrument, was employed to quantify the percentage of apoptotic cells. All experiments were conducted in triplicate to ensure reproducibility and statistical reliability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Real-time quantitative polymerase chain reaction\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted using TRIzol reagent (CWBIO, Beijing, China). The concentration of RNA was quantified with a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, USA). cDNA synthesis was performed using the PrimeScript RT kit (TaKaRa, Japan). RT-PCR was employed to evaluate the expression of SNORA33 following the manufacturer's instructions (SYBR Green Master Mix, Vazyme). The expression levels of the target gene were normalized relative to the U6 mRNA levels [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The relative RNA expression levels were calculated using the 2-ΔΔCt method. \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e provides the sequences of all PCR primers utilized in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Western blot\u003c/h2\u003e \u003cp\u003eWestern blotting was conducted in accordance with established protocols from our prior studies [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The experiment was conducted using commercially available antibodies. β-actin (mouse, 1:1000, Santa Cruz Biotechnology) was utilized as the internal control. The primary antibodies utilized in this study included Bax (1:1000; Proteintech), JAK1 (1:2000; Abcam), STAT3 (1:2000; Abcam) and EPOR (1:1000; Abcam).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.8 Bioinformatics analysis\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eWe obtained the snoRNA expression matrix for TCGA-ccRCC patients from the SNORic database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinfo.life.hust.edu.cn/SNORic/\u003c/span\u003e\u003cspan address=\"http://bioinfo.life.hust.edu.cn/SNORic/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and retrieved the clinical data for TCGA-ccRCC from UCSC Xena (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/datapages/\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/datapages/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Perform a logarithmic transformation of the snoRNA expression data to facilitate subsequent analyses. Furthermore, we acquired a matched normal tissue dataset from the TCGA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancer.gov/tcga\u003c/span\u003e\u003cspan address=\"https://www.cancer.gov/tcga\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database for ccRCC patients to conduct differential analysis between ccRCC and corresponding normal tissues. The study cohort included 532 ccRCC tissue samples and 72 normal kidney tissue samples. A total of 793 valid SNORNA genes were included in the study. The gene expression data of ccRCC used for validation cohort were obtained from GSE167573 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the International Cancer Genome Consortium (ICGC) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://icgc.org/\u003c/span\u003e\u003cspan address=\"https://icgc.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The accession number for the ICGC is RECA-EU, which encompasses 91 individuals with available follow-up information. IMmotion151, a phase III, randomized controlled trial was used to investigate the association between the SNORA33 expression and resistance to the chemotherapy drug, sunitinib, in patients with ccRCC [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Gene set enrichment analysis\u003c/h2\u003e \u003cp\u003eGSEA was conducted on the SNORA33 expression matrix for both the high-risk and low-risk groups using the GSEA software (version 4.1.0). The Hallmark gene set was utilized as a reference to identify the distinct Hallmarks between the high-risk and low-risk groups. Similarly, use the Kyoto Encyclopedia of Genes and Genomes (KEGG) database to investigate the pathways associated with SNORA33 in clear cell renal cell carcinoma (ccRCC). The identification of pathways significantly associated with SNORA33 was based on the normalized enrichment score (NES)\u0026thinsp;\u0026gt;\u0026thinsp;1, with screening criteria set at a normalized P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and a false discovery rate (FDR) q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.25.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e3.0 Immune-related analysis\u003c/h3\u003e\n\u003cp\u003eTo assess the infiltration of 24 immune cell types, single-sample Gene Set Enrichment Analysis (ssGSEA) was employed, utilizing established immune signatures previously documented in the literature [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Utilizing ssGSEA, we quantified the respective scores for each patient based on the expression profiles of hallmark genes. The correlation between SNORA33 and the expression of immune cell markers was validated through the analysis of genomic variants using TIMER (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cistrome.shinyapps.io/timer/\u003c/span\u003e\u003cspan address=\"https://cistrome.shinyapps.io/timer/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). CIBERSORT was employed to analyze and compare the differences in immune infiltrating cells between the two risk groups.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Statistics analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted utilizing SPSS 24.0 (Chicago, IL, USA) and GraphPad Prism version 9.0 (San Diego, CA, USA). Univariate Cox regression analysis was conducted to identify independent prognostic indicators for patients, followed by multivariate Cox regression analysis based on the characteristics selected from the univariate analysis. Survival probabilities were estimated using the Kaplan\u0026ndash;Meier method and evaluated with a log-rank test. The nomogram was developed by integrating SNORA33 with relevant clinical characteristics. To assess the accuracy of the nomogram, calibration curves were utilized to evaluate the concordance between the predicted probabilities and the actual outcomes. Statistical comparisons between the two groups were conducted using either the student's t-test or Mann-Whitney U test. The Spearman correlation test was employed to assess the relationship between the two datasets. The assays conducted in this study were performed independently on at least three occasions. Two-sided P values less than 0.05 were deemed statistically significant for all statistical analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.1 Identification of the aberrant snoRNAs in ccRCC from the database\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eTo identify differentially expressed snoRNAs, we retrieved the expression profile data of snoRNAs from 72 control tissues and 532 tumor tissues sourced from the TCGA and SNORic databases. The differential expression of snoRNAs between ccRCC patients and normal donors was assessed and illustrated in a volcano plot \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e and a heat map \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e, adhering to the criteria of an adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and a |log2 fold change|\u0026gt; 1. Systematic ordering and visualisation of differentially expressed genes using differential ordering maps \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB\u003cb\u003e).\u003c/b\u003e Additionally, the prognostic factors influencing overall survival (OS) in patients with ccRCC were evaluated using both univariate and multivariate cox proportional hazards models \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF, the expression level of SNORA33 emerged as an independent prognostic factor for OS (HR\u0026thinsp;=\u0026thinsp;1.505, 95% CI\u0026thinsp;=\u0026thinsp;1.188\u0026ndash;1.907, P\u0026thinsp;=\u0026thinsp;0.001); and it was the most notable of them all. The remaining meaningful ones are AC073149.1 (HR\u0026thinsp;=\u0026thinsp;0.714, 95% CI\u0026thinsp;=\u0026thinsp;0.572\u0026ndash;0.891, P\u0026thinsp;=\u0026thinsp;0.003); AL713899.1 (HR\u0026thinsp;=\u0026thinsp;1.207, 95% CI\u0026thinsp;=\u0026thinsp;1.026\u0026ndash;1.419, P\u0026thinsp;=\u0026thinsp;0.023) and SNORD104 (HR\u0026thinsp;=\u0026thinsp;0.845, 95% CI\u0026thinsp;=\u0026thinsp;0.721\u0026ndash;0.99, P\u0026thinsp;=\u0026thinsp;0.037). The heatmap indicates that elevated expression levels of SNORA33 and SNORD104 are significantly correlated with advanced clinical stages, whereas the expression of AC073149.1 and AL713899.1 shows no strong association with the clinical variables \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.2 Correlation between SNORA33 and prognosis in ccRCC patient\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eKaplan-Meier survival analysis, in conjunction with the Log-rank test, demonstrates that elevated expression of SNORA33 is significantly correlated with reduced overall survival (OS) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, HR\u0026thinsp;=\u0026thinsp;2.62), disease-specific survival (DSS) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, HR\u0026thinsp;=\u0026thinsp;3.01), and progression-free interval (PFI) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, HR\u0026thinsp;=\u0026thinsp;1.75) among patients with ccRCC (\u003cb\u003eFig.\u0026nbsp;2A\u003c/b\u003e). Similarly, we conducted an analysis of the associations between the SNORD104, AL713899.1, AC073149.1 genes and the prognosis of ccRCC patients (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). To further assess the prognostic value of the SNORA33 gene in patients with ccRCC, we performed a time-dependent receiver operating characteristic (ROC) analysis. The results showed that the area under the curve (AUC) of the SNORA33 was 0.643, 0.735 and 0.788 for 3-year, 7-year and 9-year OS; respectively. The AUC for disease specific survival (DSS) at 3-year, 7-year, and 9-year intervals was measured at 0.638, 0.777, and 0.819, respectively. The AUCs of the SNORA33 were 0.608, 0.653 and 0.767 for 3-year, 7-year and 9-year progress free interval (PFI), respectively (\u003cb\u003eFig.\u0026nbsp;2B\u003c/b\u003e). Both univariate and multivariate cox regression analyses were conducted to determine whether SNORA33 serves as an independent risk factor for OS and DSS in ccRCC patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, p\u0026thinsp;=\u0026thinsp;0.007, respectively; \u003cb\u003eTable.1\u0026ndash;2\u003c/b\u003e). Multivariate Cox regression analyses identified statistically significant independent predictors, which facilitated the development of two nomograms for the quantitative assessment of significant risks associated with OS and DSS (\u003cb\u003eFig.\u0026nbsp;2C and 2D\u003c/b\u003e). The calibration plot of the nomogram demonstrated improved concordance between the predictions made by the nomogram and the actual observations (\u003cb\u003eFig.\u0026nbsp;2E and 2F\u003c/b\u003e). Meanwhile, our analysis of patient survival across various risk stratifications such as age, tumor stage, metastasis and stage indicated that high SNORA33 expression groups exhibited lower survivability (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003cb\u003eFig.\u0026nbsp;3I-P\u003c/b\u003e). To assess the predictive accuracy of SNORA33, our analysis revealed that the expression levels of the SNORA33 gene were significantly elevated in deceased patients within the late-stage ccRCC cohort from the ICGC database (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003cb\u003eFig.\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). These results suggested that elevated expression of SNORA33 was associated with a reduction in survival time in patients with ccRCC, indicating its potential as an effective prognostic marker or therapeutic target for this malignancy.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable border=\"1\"\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUni- and multivariate Cox analyses of prognostic factors for OS in the TCGA cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;= 60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.791 (1.319\u0026ndash;2.432)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.692 (1.105\u0026ndash;2.591)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic T stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u0026amp;T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u0026amp;T4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.210 (2.373\u0026ndash;4.342)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.395 (0.618\u0026ndash;3.146)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic N stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.422 (1.817\u0026ndash;6.446)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.389 (0.694\u0026ndash;2.779)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic M stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.401 (3.226\u0026ndash;6.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.345 (1.391\u0026ndash;3.954)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage I\u0026amp;Stage II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage III\u0026amp;Stage IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.910 (2.852\u0026ndash;5.360)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.494 (0.594\u0026ndash;3.760)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistologic grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1\u0026amp;G2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG3\u0026amp;G4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.665 (1.898\u0026ndash;3.743)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.452 (0.876\u0026ndash;2.404)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNORA33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.886 (2.083\u0026ndash;3.999)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.924 (1.766\u0026ndash;4.841)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.924 (0.679\u0026ndash;1.257)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e95% CI, 95% Confidence Interval\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eUni- and multivariate Cox analyses of prognostic factors for DSS in the TCGA cohort\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHazard ratio (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHazard ratio (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;= 60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.351 (0.926\u0026ndash;1.971)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.183 (0.786\u0026ndash;1.781)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic T stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u0026amp;T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u0026amp;T4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.606 (3.697\u0026ndash;8.502)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.122 (0.485\u0026ndash;2.593)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic N stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.864 (1.831\u0026ndash;8.157)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.373 (0.634\u0026ndash;2.975)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic M stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.219 (6.294\u0026ndash;13.504)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.150 (1.739\u0026ndash;5.708)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage I\u0026amp;Stage II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage III\u0026amp;Stage IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.937 (5.989\u0026ndash;16.486)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.494 (1.154\u0026ndash;10.581)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistologic grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1\u0026amp;G2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG3\u0026amp;G4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.850 (2.925\u0026ndash;8.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.705 (0.856\u0026ndash;3.397)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNORA33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.061 (2.012\u0026ndash;4.656)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.405 (1.271\u0026ndash;4.552)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e95% CI, 95% Confidence Interval\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv class=\"DuplicateTablecaptionEnd\"\u003e\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Expression of SNORA33 in ccRCC and its correlation with clinicopathological characteristics\u003c/h2\u003e \u003cp\u003eAs illustrated in \u003cb\u003eFig.\u0026nbsp;3C and 3D\u003c/b\u003e, based on TCGA, GSE167573 and ICGC ccRCC cohort, the level of SNORA33 was markedly elevated in cancerous tissues in comparison to those observed in normal tissues and paired samples (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 respectively). Subsequently, a receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the biomarker. The AUC for SNORA33 was 0.768, demonstrating a sensitivity of 58.2% and specificity of 91.7% (\u003cb\u003eFig.\u0026nbsp;3B\u003c/b\u003e). Furthermore, differential expression of SNORA33 was also identified among cancer tissues classified by varying living statue, T stages, metastasis and pathologic stage but not to gender, age and lymph node staging (\u003cb\u003eFig.\u0026nbsp;3A and 3E-3H\u003c/b\u003e). The supplementary figures present a comprehensive analysis of the expression levels of the SNORD104, AL713899.1, AC073149.1 genes in ccRCC tumors and normal tissues, along with their associations with clinical characteristics (\u003cb\u003eFig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.4 GSEA and pathway correlation analysis\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe genes that were found to be differentially expressed between the high and low expression subgroups of SNORA33 will be employed in a subsequent functional enrichment analysis (\u003cb\u003eFig.\u0026nbsp;4A\u003c/b\u003e). Gene Set Enrichment Analysis (GSEA) was performed to elucidate the pathways associated with the SNOR33 gene. And \u003cem\u003eHALLMARK_IL6_JAK_STAT3_SIGNALING\u003c/em\u003e,\u003cem\u003eKEGG_JAK_STAT3_SIGNALING_PATHWAY\u003c/em\u003e exhibited a significant positive correlation with the SNORA33 gene (p.adj\u0026thinsp;\u0026lt;\u0026thinsp;0.05, NES\u0026thinsp;=\u0026thinsp;1.34, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01; p.adj\u0026thinsp;\u0026lt;\u0026thinsp;0.05, NES\u0026thinsp;=\u0026thinsp;1.14, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01, respectively; \u003cb\u003eFig.\u0026nbsp;4B and 4C\u003c/b\u003e). Prior researches had also demonstrated that this pathway exerted an oncogenic role in ccRCC[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The lasso-cox regression was carried out on all genes within the \u003cem\u003eHALLMARK_IL6_JAK_STAT3_SIGNALING\u003c/em\u003e pathway to identify those that significantly influence the prognosis of ccRCC patients (\u003cb\u003eFig.\u0026nbsp;4D\u003c/b\u003e). We also examined the expression of these genes in TCGA for ccRCC and normal tissues \u003cb\u003e(Fig. S3)\u003c/b\u003e, and evaluated the correlation between SNORA33 expression and those of the other genes. Additionally, we analyzed the scatter distribution, survival status, and risk gene expression associated with SNORA33 (\u003cb\u003eFig.\u0026nbsp;4E and 4F\u003c/b\u003e). As shown in \u003cb\u003eFig.\u0026nbsp;4G-4J\u003c/b\u003e, we observed a robust positive correlation between the expression levels of the SNORA33 and the erythropoietin receptor (EPOR) gene based on the ccRCC cohorts sourced from TCGA and ICGC databases (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, R\u0026thinsp;=\u0026thinsp;0.529; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, R\u0026thinsp;=\u0026thinsp;0.503, respectively). And among the ccRCC subpopulation characterized by high SNORA33 expression, we observed a concordantly significant upregulation of the EPOR gene. Western blot analysis also showed that overexpression of SNORA33 in ccRCC cells increased the levels of JAK1, STAT3 and EPOR (\u003cb\u003eFig.\u0026nbsp;4K and 4L\u003c/b\u003e). However, knockdown of SNORA33 in 786-O and A498 cells reduced the levels of JAK1, STAT3 and EPOR (\u003cb\u003eFig.\u0026nbsp;4K and 4L\u003c/b\u003e). Moreover, the EPOR gene was identified as a key regulator that influenced the responsiveness of renal cancer to sunitinib therapy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.5 Immune landscape of the SNORA33\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eTo gain a deeper understanding of how SNORA33 affected the immune microenvironment of ccRCC patients, we conducted an extensive analysis focusing on immune-related parameters. Within the TCGA-KIRC cohort, we employed the ssGSEA and CIBERSORT methodologies to determine the immune cell infiltration scores for 24 and 22 unique immune cell types, respectively, for each participant (\u003cb\u003eFig.\u0026nbsp;5D and 5E\u003c/b\u003e). Strikingly, a subset of these cell types demonstrated substantial variations in infiltration levels between patients classified as having high versus low SNORA33 expression, such as T regulatory cells (Tregs), Tgd (T gamma delta) cells, Th17 cells and T helper cells (\u003cb\u003eFig.\u0026nbsp;5D\u003c/b\u003e). Furthermore, we explored the relationship between the levels of immune cells infiltration and the expression of SNORA33, and graphically represented the result using bar charts (\u003cb\u003eFig.\u0026nbsp;5A\u003c/b\u003e). A significant positive correlation was observed between the levels of pro-tumorigenic Treg cells and SNORA33. Conversely, elevated expression levels of SNORA33 are associated with a decrease in the infiltration of Tgd anti-cancer cells and Th17 cells, and this was validated through the analysis of its association with cellular markers (Treg cells markers: FOXP3 and TNFRSF18, \u003cb\u003eFig.\u0026nbsp;5B and 5C\u003c/b\u003e). The imbalance between Tregs and Th17 cells had emerged as a critical factor in the progression of cancer [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The ssGSEA scores for the ESTIMATE score, Immune score, and Stromal score of each sample from TCGA ccRCC patients were presented in the form of ssGSEA scores, and box plots were used to show the differences between the high and low SNORA33 subgroups. The results indicated that the subpopulation with elevated SNORA33 expression exhibited higher ESTIMATE and Immune scores, while no significant difference was observed in stromal scores (\u003cb\u003eFig.\u0026nbsp;5F\u003c/b\u003e). Furthermore, high immune scores were strongly associated with poor patient survival (\u003cb\u003eFig.\u0026nbsp;5G\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Investigation of SNORA33-related tumour mutations and comprehensive pan-cancer analysis\u003c/h2\u003e \u003cp\u003eSubsequently, we conducted an analysis of SNOR33 mutations in ccRCC, and we found that the likelihood of a Catenin Beta 1 (CTNNB1) gene mutation is markedly elevated in patients exhibiting high expression of SNORA33 (\u003cb\u003eFig.\u0026nbsp;6A\u003c/b\u003e). Nevertheless, copy number variation of CTNNB1 did not exert a significant influence on the survival outcomes of patients diagnosed with ccRCC (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), the expression level of CTNNB1 was closely linked to prognosis, and its high expression often indicated a better prognosis of ccRCC patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cb\u003eFig.\u0026nbsp;6B and 6C\u003c/b\u003e). Subsequently, we conducted an investigation into the differential expression of SNORA33 in 33 cancerous tissues and normal tissues, and performed a more in-depth analysis of paired samples from 23 of these cancer types (\u003cb\u003eFig.\u0026nbsp;6D and 6E\u003c/b\u003e). The results indicated that SNORA33 demonstrates significant expression differences in BLCA (Bladder Urothelial Carcinoma), CHOL (Cholangiocarcinoma), COAD (Colon Adenocarcinoma), LIHC (Liver Hepatocellular Carcinoma) and STAD (Stomach Adenocarcinoma), as well as in several other malignancies. We selected OS to assess the prognostic significance of SNORA33 across pan-cancer. High levels of SNORA33 expression were linked to poor OS in both adrenocortical carcinoma (ACC) (p\u0026thinsp;=\u0026thinsp;0.005, HR\u0026thinsp;=\u0026thinsp;3.27) and kidney renal clear cell carcinoma (KIRC) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, HR\u0026thinsp;=\u0026thinsp;2.67). In contrast, elevated SNORA33 expression was associated with favorable OS in thymoma (THYM) (p\u0026thinsp;=\u0026thinsp;0.047, HR\u0026thinsp;=\u0026thinsp;0.20, \u003cb\u003eFig.\u0026nbsp;6F\u003c/b\u003e). In various cancer types, SNORA33 exhibits a close association with Tgd cells, mast cells, macrophages, neytrophils and other immune cell populations (\u003cb\u003eFig.\u0026nbsp;6G\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.7 The immune-related gene expression profile of SNORA33 and its implications for drug prediction\u003c/h2\u003e \u003cp\u003eThe expression levels of immune-related genes can serve as indicators of the immune status in different patients and their potential responsiveness to immunotherapy. We conducted a comparative analysis of gene expression across various subgroups within a comprehensive immune-related gene set. The high SNORA33 expression group exhibited elevated expression levels of interferon response-related genes, whereas no significant differences were observed in the expression levels of antigen presentation-related genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-B). Additionally, we observed that certain immunostimulatory molecules, including CD27, CD80, TNFRSF14 and TNFRSF25, exhibited elevated expression levels within the high SNORA33 expression group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Among the immunosuppressive molecules, the expression levels of several immune checkpoint molecules, including CTLA4, PDCD1, TIGIT, IDO2 and SPDL1 were significantly elevated in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). In the analysis of SNORA33 co-expression, it was observed that the majority of immune-related genes exhibited positive expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e7\u003c/span\u003eE-H). To assess the impact of SNORA33 expression on drug response and identify suitable treatment options, we noted that within the IMmotion151 cohort undergoing sunitinib therapy, the expression level of SNORA33 was significantly lower in patients who complete response (CR) and partial response (PR) compared to those with progressive disease (PD) and stable disease (SD) within the IMmotion151 cohort (\u003cb\u003eFig. S3\u003c/b\u003e). Subsequently, we employed the cell ability assay to further elucidate the impact of SNORA33 on the sensitivity of ccRCC cells to sunitinib and performed a quantitative analysis. The results showed that in comparison to vector 786-O cells, the overexpressed SNORA33 cells demonstrated significantly enhanced cell viability and increased IC50 values under various concentrations of sunitinib treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e7\u003c/span\u003eI). And the cell viability and IC50 values were significantly lower in the si-SNORA33 group compared to the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e7\u003c/span\u003eJ). This further indicated a close link between the expression of SNORA33 and resistance to sunitinib.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.8 SNORA33 promotes the proliferation, invasion and migration of ccRCC cells\u003c/h2\u003e \u003cp\u003eBuilding on our previous studies, we identified that the SNORA33 gene plays a significant role in the development and progression of ccRCC. A series of in vitro experiments further substantiated our hypothesis. To investigate the biological function of SNORA33 in ccRCC, we employed small interfering RNA (siRNA) to downregulate its expression and plasmid transfection to enhance its expression (OE-SNORA33). After transfecting 786-O and A498 cells with si-SNORA33 and constructing plasmid-based transfection, the significant reduction and overexpression of SNORA33 validated the successful establishment of these cells \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003eA \u003cb\u003eand B)\u003c/b\u003e. Then, we investigated the impact of SNORA33 overexpression on ccRCC cells. The Transwell assay demonstrated that SNORA33 overexpression significantly enhanced the invasive and migratory capacities of 786-O and A498 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003eE and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003eF). The statistical results are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003eI, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003eJ and supplemental figure. Furthermore, both CCK-8 and clone formation assay indicated that SNORA33 overexpression markedly promoted the proliferation of ccRCC cells \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003eC-D and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003eG-H). Additionally, we focused on the effects of SNORA33 gene knockdown on ccRCC. The CCK-8 assay results demonstrated that cellular proliferation ability was significantly impaired in the si-SNORA33 group relative to the negative control group (\u003cb\u003eFig.\u0026nbsp;9A and 9B\u003c/b\u003e). And Colony formation assays revealed that reduced SNORA33 expression significantly diminished both the number and size of colonies in 786-O cells compared to the Control group (\u003cb\u003eFig.\u0026nbsp;9E and 9F\u003c/b\u003e). Migration and invasion are critical biological characteristics of renal cancer cells. Transwell assay results showed that silencing SNORA33 substantially decreased both invasive and migratory capacities of these cells (\u003cb\u003eFig.\u0026nbsp;9C and 9D\u003c/b\u003e). The statistical results are illustrated in \u003cb\u003eFig.\u0026nbsp;9G, 9H\u003c/b\u003e and supplemental figure. Flow cytometry analysis revealed that SNORA33 knockdown significantly promoted apoptosis in 786-O and A498 cells (\u003cb\u003eFig.\u0026nbsp;9I\u003c/b\u003e). The results of the western blot analysis indicated that the knockdown of the SNORA33 gene result in elevated expression levels of the pro-apoptotic protein bax (\u003cb\u003eFig.\u0026nbsp;9J and 9K\u003c/b\u003e). In addition, Collectively, these findings suggest that SNORA33 facilitates the proliferation, migration, and invasion of ccRCC cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn the past few years, a lot of work has been done to study the expression profiles and prognostic roles of non-coding RNAs, including lncRNAs, microRNA, circRNAs and etc, in different types of cancer. However, other classes of small non-coding RNAs, such as snoRNAs, have been infrequently investigated in this context, particularly within the realm of ccRCC. In the present study, we screened a variety of snoRNAs from the TCGA database, we for the first time discovered that the expression of SNORA33 was significantly higher in ccRCC tissues than in normal kidney tissues, and we further examined the expression of SNORA33 in our ccRCC cells, the result of which was still the case. We postulated that the elevated SNORA33 was correlated with the poor prognosis and lower survival rates, and was associated with an advanced clinical stage and distant metastasis in ccRCC patients. As anticipated, through loss of function and functional acquisition analysis, we have verified that SNORA33 can facilitate the proliferation, invasion, and metastasis of ccRCC cells. This indicates that the expression of SNORA33 could act as a reliable prognostic indicator for ccRCC. Meanwhile, via the cell viability assay, we discovered that the upregulation of SNORA33 might facilitate the resistance of ccRCC cells to sunitinib. Mechanistically, we first demonstrated that SNORA33 is significantly positively correlated with the JAK/STAT3 pathway in ccRCC cells. More importantly, the upregulation of SNORA33 lead to the development of ccRCC and resistance to sunitinib, which can be accomplished by activating the JAK/STAT3 pathway.\u003c/p\u003e \u003cp\u003eSnoRNAs constitute a class of non-coding RNAs that exert critical regulatory functions in various physiological and pathological events[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Previous studies have indicated that snoRNAs possess more stable characteristics and are technically more amenable to enrichment and detection compared with miRNAs, ctDNA (circulating tumor DNA), and exosome[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Consequently, in recent years, an increasing amount of attention has been devoted to the role of snoRNAs in the development of cancer[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. On the one hand, snoRNAs exert their influences via classical modes of action such as ribosome binding and catalyzing pseudouridylation, among others. SNORD89 modifies Bim through 2'-O-methylation, influencing the Bim-mediated Bcl-2/Bax signaling pathway and facilitating the progression of endometrial cancer[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. SNORA56-mediated pseudouridylation of 28 S rRNA suppresses ferroptosis and promotes colorectal cancer proliferation by enhancing GCLC translation[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. SNORD88C-guided 2\u0026prime;-O-methylation of 28S rRNA regulates SCD1 translation to suppress autophagy and facilitate growth and metastasis in non-small cell lung cancer[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. On the other hand, snoRNAs not only play a role in the modification of rRNA, but it could also bind to specific tumor-related proteins and directly participate in the molecular regulatory network of cancer. SNORD17 interacts with NPM1 and MYBBP1A to suppress p53 activation and thereby drive the progression of HCC[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. SnoRNAs are capable of binding to PARP-1, stimulating PARP-1 self-modification and facilitating the interaction between PARP-1 and DDX21. This subsequently promotes rDNA transcription and cell growth in breast cancer [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDue to its significant role in tumor cell survival, proliferation and invasion, JAK/STAT signaling has emerged as a preferred target for drug development and cancer therapy[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The JAK/STAT signaling pathway was initially identified in the research of interferon-related transcription activation. Subsequently, over approximately 20 years, a general outline of the components and pathogenesis of the JAK/STAT signaling pathway was gradually accomplished [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. More than 50 types of cytokines, such as hormones, growth factors, and chemokines, have been demonstrated to be correlated with JAK/STAT3 activation, resulting in cell differentiation, metabolism, survival, as well as the development, recurrence, anticancer drug resistance, and cancer stem cell generation of malignant tumors [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Among them, recent studied have indicated that the EPOR, as a constituent of the cytokine receptor superfamily, plays a crucial role in the activation of the JAK/STAT pathway, facilitating the advancement of malignant melanoma and head and neck squamous cell carcinoma (HNSCC), among others [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. From the results of our bioinformatics analysis indicated that the expression of SNORA33 is significantly and positively correlated with EPOR, which was further verified by western blot analysis in ccRCC cells. Meanwhile, GSEA analysis and western blot demonstrated that in ccRCC, SNORA33 resulted in a significant upregulation of the JAK/STAT pathway. Wu et al. have demonstrated that EPOR was related to the growth, invasion, and survival of RCC cells and was capable of counteracting the anti-tumor effects of sunitinib on ccRCC cells [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. These results suggest that SNORA33 may exert a promoting effect on ccRCC by participating in EPOR/JAK/STAT axis and may potentially serve as a therapeutic target for ccRCC.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLimitations of the study\u003c/b\u003e \u003c/p\u003e \u003cp\u003eHere, we have determined that SNORA33 promotes the progression and resistance of ccRCC by influencing the expression of key proteins within the JAK/STAT pathway. Nevertheless, the mechanism through which SNORA33 exerts its effects, whether by means of modification or direct binding to pathway proteins, has not been fully clarified, further research is required to elucidate this. There is still much room for improvement in our research.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn summary, we investigate the H/ACA box snoRNA SNORA33, which is upregulated in ccRCC cells and associated with poor prognosis. SNORA33 could function as a reliable prognostic marker for ccRCC. Our findings demonstrate that SNORA33 promotes the development and progression of ccRCC by modulating the JAK/STAT3 signaling pathway, and enhancing resistance to sunitinib. SNORA33 has the potential to serve as a novel biomarker for the prognosis and treatment of ccRCC in the future, offering new strategies for diagnosis and therapy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"595\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003cp\u003eACC\u003c/p\u003e\n \u003cp\u003eBLCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003earea under the curve\u003c/p\u003e\n \u003cp\u003eadrenocortical carcinoma\u003c/p\u003e\n \u003cp\u003eBladder Urothelial Carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eCTNNB1 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eCatenin Beta\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eccRCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eClear Cell Renal Cell Carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eCHOL\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCOAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eCholangiocarcinoma\u003c/p\u003e\n \u003cp\u003eColon Adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003cp\u003eDSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003ecomplete response\u003c/p\u003e\n \u003cp\u003edisease specific survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eEPOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eerythropoietin receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eGSEA\u003c/p\u003e\n \u003cp\u003eHNSCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eGene set enrichment analysis\u003c/p\u003e\n \u003cp\u003ehead and neck squamous cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eJAK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003ejanus kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eKIRC \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003ekidney renal clear cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eOverall Survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003ePFI\u003c/p\u003e\n \u003cp\u003esnoRNPs\u0026nbsp;\u003c/p\u003e\n \u003cp\u003esnoRNAs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eProgress Free Interval\u003c/p\u003e\n \u003cp\u003esmall nucleolar ribosonucleoproteins\u003c/p\u003e\n \u003cp\u003esmall nucleolar RNAs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eSTAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003esignal transducer and activator of transcription \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eTCGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eThe Cancer Genome Atlas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (Grant Number 82102999).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFormal analysis, Qinglong Du, Shuo Zhao and Jiajia Sun; Funding acquisition, Yidong Fan and Jikai Liu; Methodology, Jiajia Sun and Qinglong Du; Project administration, Jikai Liu; Software, Qinzheng Chang; Supervision, Yidong Fan and Jikai Liu; Visualization, Qinglong Du and Qinzheng Chang; Writing \u0026ndash; original draft, Qinglong Du, Shuo Zhao, Jiajia Sun, Wei Guo, Lin Yang and Laiyuan Qiu; Writing \u0026ndash; review \u0026amp; editing, Nianzhao Zhang, Yidong Fan and Jikai Liu. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResource availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLead contact\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFurther information and requests for reagents and resources should be directed to and will be fulfilled by the lead contact, Jikai Liu ([email protected]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not generate new unique reagents.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors appreciate study investigators and staff who participated in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll datasets used in this study were downloaded from the TCGA, GEO, ICGC and SNORic databases and the manuscript\u0026apos;s Methods section offers comprehensive details on the sources for data downloads. The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no potential conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article encompassed no research concerning human or animal samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe have obtained consent to publish this paper from all the participants of this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel, R.L., et al., Cancer statistics, 2022. CA Cancer J Clin, 2022. \u003cstrong\u003e72\u003c/strong\u003e(1): 7-33.\u003c/li\u003e\n\u003cli\u003eHsieh, J.J., et al., Renal cell carcinoma. 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Clin Epigenetics, 2023. \u003cstrong\u003e15\u003c/strong\u003e(1): 19.\u003c/li\u003e\n\u003cli\u003eSudhakar Reddy, P., et al., Comprehensive evaluation of candidate reference genes for real-time quantitative PCR (RT-qPCR) data normalization in nutri-cereal finger millet [Eleusine Coracana (L.)]. PLoS One, 2018. \u003cstrong\u003e13\u003c/strong\u003e(10): e0205668.\u003c/li\u003e\n\u003cli\u003eZhao, S., et al., CTCF-activated FUCA1 functions as a tumor suppressor by promoting autophagy flux and serum \u0026alpha;-L-fucosidase serves as a potential biomarker for prognosis in ccRCC. Cancer Cell Int, 2024. \u003cstrong\u003e24\u003c/strong\u003e(1): 327.\u003c/li\u003e\n\u003cli\u003eRini, B.I., et al., Atezolizumab plus bevacizumab versus sunitinib in patients with previously untreated metastatic renal cell carcinoma (IMmotion151): a multicentre, open-label, phase 3, randomised controlled trial. Lancet, 2019. \u003cstrong\u003e393\u003c/strong\u003e(10189): 2404-2415.\u003c/li\u003e\n\u003cli\u003eH\u0026auml;nzelmann, S., R. Castelo, and J. Guinney, GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics, 2013. \u003cstrong\u003e14\u003c/strong\u003e: 7.\u003c/li\u003e\n\u003cli\u003eLin, H., et al., Fatty acids metabolism affects the therapeutic effect of anti-PD-1/PD-L1 in tumor immune microenvironment in clear cell renal cell carcinoma. J Transl Med, 2023. \u003cstrong\u003e21\u003c/strong\u003e(1): 343.\u003c/li\u003e\n\u003cli\u003eXie, L., et al., TTC13 expression and STAT3 activation may form a positive feedback loop to promote ccRCC progression. PeerJ, 2023. \u003cstrong\u003e11\u003c/strong\u003e: e16316.\u003c/li\u003e\n\u003cli\u003eKnochelmann, H.M., et al., When worlds collide: Th17 and Treg cells in cancer and autoimmunity. Cell Mol Immunol, 2018. \u003cstrong\u003e15\u003c/strong\u003e(5): 458-469.\u003c/li\u003e\n\u003cli\u003eToffano-Nioche, C., D. Gautheret, and F. Leclerc, Revisiting the structure/function relationships of H/ACA(-like) RNAs: a unified model for Euryarchaea and Crenarchaea. Nucleic Acids Res, 2015. \u003cstrong\u003e43\u003c/strong\u003e(16): 7744-61.\u003c/li\u003e\n\u003cli\u003eSong, J., et al., Clinical significance and prognostic value of small nucleolar RNA SNORA38 in breast cancer. Front Oncol, 2022. \u003cstrong\u003e12\u003c/strong\u003e: 930024.\u003c/li\u003e\n\u003cli\u003eLiang, J., et al., Small Nucleolar RNAs: Insight Into Their Function in Cancer. Front Oncol, 2019. \u003cstrong\u003e9\u003c/strong\u003e: 587.\u003c/li\u003e\n\u003cli\u003eBao, H.J., et al., Box C/D snoRNA SNORD89 influences the occurrence and development of endometrial cancer through 2\u0026apos;-O-methylation modification of Bim. Cell Death Discov, 2022. \u003cstrong\u003e8\u003c/strong\u003e(1): 309.\u003c/li\u003e\n\u003cli\u003eXu, C., et al., SNORA56-mediated pseudouridylation of 28 S rRNA inhibits ferroptosis and promotes colorectal cancer proliferation by enhancing GCLC translation. J Exp Clin Cancer Res, 2023. \u003cstrong\u003e42\u003c/strong\u003e(1): 331.\u003c/li\u003e\n\u003cli\u003eWang, K., et al., SNORD88C guided 2\u0026apos;-O-methylation of 28S rRNA regulates SCD1 translation to inhibit autophagy and promote growth and metastasis in non-small cell lung cancer. Cell Death Differ, 2023. \u003cstrong\u003e30\u003c/strong\u003e(2): 341-355.\u003c/li\u003e\n\u003cli\u003eLiang, J., et al., Non-coding small nucleolar RNA SNORD17 promotes the progression of hepatocellular carcinoma through a positive feedback loop upon p53 inactivation. Cell Death Differ, 2022. \u003cstrong\u003e29\u003c/strong\u003e(5): 988-1003.\u003c/li\u003e\n\u003cli\u003eKim, D.S., et al., Activation of PARP-1 by snoRNAs Controls Ribosome Biogenesis and Cell Growth via the RNA Helicase DDX21. Mol Cell, 2019. \u003cstrong\u003e75\u003c/strong\u003e(6): 1270-1285.e14.\u003c/li\u003e\n\u003cli\u003eGroner, B. and V. von Manstein, Jak Stat signaling and cancer: Opportunities, benefits and side effects of targeted inhibition. Mol Cell Endocrinol, 2017. \u003cstrong\u003e451\u003c/strong\u003e: 1-14.\u003c/li\u003e\n\u003cli\u003eJin, W., Role of JAK/STAT3 Signaling in the Regulation of Metastasis, the Transition of Cancer Stem Cells, and Chemoresistance of Cancer by Epithelial-Mesenchymal Transition. Cells, 2020. \u003cstrong\u003e9\u003c/strong\u003e(1).\u003c/li\u003e\n\u003cli\u003eMirmohammadsadegh, A., et al., Role of erythropoietin receptor expression in malignant melanoma. J Invest Dermatol, 2010. \u003cstrong\u003e130\u003c/strong\u003e(1): 201-10.\u003c/li\u003e\n\u003cli\u003eLai, S.Y., et al., Erythropoietin-mediated activation of JAK-STAT signaling contributes to cellular invasion in head and neck squamous cell carcinoma. Oncogene, 2005. \u003cstrong\u003e24\u003c/strong\u003e(27): 4442-9.\u003c/li\u003e\n\u003cli\u003eWu, P., et al., The erythropoietin/erythropoietin receptor signaling pathway promotes growth and invasion abilities in human renal carcinoma cells. PLoS One, 2012. \u003cstrong\u003e7\u003c/strong\u003e(9): e45122.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"SNORA33, clear cell renal cell carcinoma, sunitinib, jak/stat pathway","lastPublishedDoi":"10.21203/rs.3.rs-6241591/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6241591/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccumulating evidence has confirmed that snoRNAs exert a role in a variety of cancer, however, less known in ccRCC. This study was aimed at elucidating the role and mechanism of snoRNAs in the tumorigenesis and progression of ccRCC. The snoRNAs expression matrices were obtained from the public TCGA and SNORic databases. The Kaplan-Meier analysis and Cox univariate and multivariate analyses confirmed the prognostic value of SNORA33 in ccRCC. A series of in vitro experiments were conducted to explore the functional role of SNORA33 in ccRCC. GSEA and western blot were used to explore and validate the involved mechanisms. SNORA33 was highly expressed in patients with ccRCC and was correlated with poor prognosis. The findings of in vitro experiments indicated that SNORA33 was capable of promoting the proliferation, invasion, migration, and resistance to sunitinib in ccRCC. SNORA33 is capable of attaining these effects through regulating the JAK/STAT signaling pathway.\u003c/p\u003e","manuscriptTitle":"SNORA33 promotes clear cell renal cell carcinoma development and resistance to sunitinib through triggering the JAK/STAT pathway","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 03:29:46","doi":"10.21203/rs.3.rs-6241591/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"df0b84d7-1d6a-4c7d-b30a-d4e8310ac74d","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47779272,"name":"Biological sciences/Cancer"},{"id":47779273,"name":"Health sciences/Biomarkers"},{"id":47779274,"name":"Health sciences/Urology"}],"tags":[],"updatedAt":"2025-11-11T12:24:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 03:29:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6241591","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6241591","identity":"rs-6241591","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}