Identification of SnoRNAs Predicting Prognosis for Ovarian Cancer

Identification of SnoRNAs Predicting Prognosis for Ovarian Cancer | 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 Primary research Identification of SnoRNAs Predicting Prognosis for Ovarian Cancer Wenjing Zhu, Tao Zhang, Shaohong Luan, Qingnuan Kong, Wenmin Hu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1022969/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Background: Increasing evidence has been confirmed that small nucleolar RNAs (SnoRNAs) play critical roles in tumorigenesis and exhibit prognostic value in clinical practice. However, there is short of systematic research on SnoRNAs in ovarian cancer (OV). Material/methods: 379 OV patients with RNA-Seq and clinical parameters from TCGA database and 5 paired clinical OV tissues were embedded in our study. Cox regression analysis was used to identify prognostic SnoRNAs and construct prediction model. SNORic database was adopted to examine the copy number variation of snoRNAs. ROC curves and KM plot curves were applied to validate the prediction model. Besides, the model was validated in 5 paired clinical tissues by real-time PCR, H&E staining and immunohistochemistry. Results: A prognostic model was constructed on the basis of SnoRNAs in OV patients. Patients with higher RiskScore had poor clinicopathological parameters, including higher age, larger tumorsize, advanced stage and with tumor status. KM plot analysis confirmed that patients with high RiskScore had poorer prognosis in subgroup of age, tumor size and stage. 7 of 9 snoRNAs in the prognostic model had positive correlation with their host genes. Moreover, 5 of 9 snoRNAs in the prognostic model correlated with their CNVs, and SNORD105B had the strongest correction with its CNVs. ROC curve showed that the RiskScore had excellent specificity and accuracy. Further, H&E staining and immunohistochemistry of Ki67, P53 and P16 were confirmed that patients with higher RiskScore are more malignant. Conclusions: In summary, we identified a nine-snoRNAs signature as an independent indicator to predict prognosis of OV, providing a prospective prognostic biomarker and potential therapeutic targets for ovarian cancer. Molecular Biology Oncology General Biochemistry ovarian cancer snoRNA biomarker prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Background Ovarian cancer is one of the most common gynecologic malignancies with a poor prognosis[ 1 ]. Despite other cancers such as endometrial cancer having higher rates of incidence, mortality of ovarian cancer rates still listed to be high [ 2 ]. More than 75% of ovarian cancers are diagnosed at advanced or metastatic stage [ 3 ]. Besides, most women diagnosed with high-grade serous ovarian cancer develop recurrent disease and chemotherapy resistance, despite initially respond to this treatment, recurrence is likely to occur within a median of 16 months for patients who present with advanced stage disease [ 4 ]. At present, identifying and discovering effective biomarkers and realizing molecular targeted therapy are considered to be an effective treatment for ovarian cancer [ 5 ]. Consequently, finding effective therapeutic target molecules for ovarian cancer is an urgent problem to be solved. Small nucleolar RNAs (snoRNAs) are a class of non-coding RNAs with 60-300nt, and mainly divided into two classes: C/D box snoRNAs and H/ACA box snoRNAs [ 6 ]. Traditionally, they act as the role of modifying 2'-O-ribose methylation and pseudouridylation of ribosomal RNAs (rRNAs), respectively [ 6 ]. Emerging evidence has demonstrated that small nucleolar RNAs (snoRNAs) play significant roles in tumorigenesis [ 7 ]. Such as, snoRNA U3, a box of C/D RNA, could be processed to smaller RNAs just as miRNA and perform the function of miRNA in cancer [ 8 ]. Moreover, snoRNAs had been reported to play a critical determinants of leukaemic stem cell activity, and disruption of H/ACA snoRNA levels in stem cells impairs pluripotency [ 9 , 10 ]. Further, other research suggested that snoRNAs participated in the regulation of mRNA abundance, alternative splicing and metabolic and oxidative stress [ 11 ]. Recent study showed that snoRNAs could act as diagnostic markers, prognostic markers and therapeutic targets in various cancers [ 12 ]. The number of dysregulated snoRNAs in ovarian cancer is up to 462 [ 7 ], however, there is no study on snoRNA had been conducted in ovarian cancer. In our research, we screened prognostic snoRNAs, and constructed a risk model to predict the prognosis of ovarian cancer patients. This may provide new ideas and targets for the clinical treatment of ovarian cancer. 2 Methods 2.1 Data sets The data of patients with ovarian cancer in TCGA, including mRNA-Seq of transcriptome profiling data and clinical data, was downloaded by the GDC data portal: https://portal.gdc.cancer.gov/ . The detailed clinical pathological parameters of patients with ovarian cancer, including age, subdivision, lymphatic invasion, grade, race, stage, tumor size and venous invasion of ovarian cancer, were listed in the following Table 1 . Table 1 Clinical pathological parameters of ovarian cancer patients in TCGA database Clinical pathological parameters N % Age ≤60 206 54.9 >60 169 45.1 Subdivision left or right 50 28.6 Bilateral 125 71.4 Lymphatic invasion NO 20 30.3 YES 46 69.7 Grade G1‎+G2 21 11.5 G3+G4 162 88.5 Race Asian 4 2.2 Black or African American 15 8.2 White 163 89.6 Stage Stage1+2 12 6.5 Stage3+4 173 93.5 Tumor Size No Macroscopic disease 36 22.1 ≤20mm 94 57.7 ༞20mm 33 20.2 Venous invasion NO 19 36.2 YES 39 63.8 2.2 Patients and clinical specimens We recruited 5 pairs of matched ovarian cancer tissues and normal tissues from Chinese Institution. Among of them, three cases were diagnosed as high-grade serous carcinoma with pleomorphic nuclei, high N/C ratio and active mitosis. One case was diagnosed as low-grade serous carcinoma composed of small cellular nests containing multiple psammoma bodies, uniform nuclei with mild to moderate atypia. One case was diagnosed as endometrioid adenocarcinoma which displayed tubular pattern and nests. These tissue samples and corresponding clinical pathology data were collected from Qingdao Municipal Hospital. This study was approved by Institutional Review Board of Qingdao Municipal Hospital. The number of the approval of this study by the ethical committee is No.018. And the approval document was approved on September, 2021. 2.3 RNA isolation and quantitative real‑time PCR (qRT‑PCR) For tissue RNA isolation, 1 mL AG RNAex Pro Reagent (Accurate Biotechnology Co.) was added to 50 mg of tissue and total RNA samples were extracted according to the manufacturer’s instructions. Purified RNA was quantified resort to NanoVue (GE Healthcare Life Sciences). cDNAs were synthesized from total RNAs by using RT reagent Kit (Takara Co., LTD, Japan) and ReverTra Ace qPCR RT Kit (Toyobo Co., LTD, Japan). qRT-PCR of U6, SNORA11B, SNORA36C, SNORA58, SNORA70J, SNORA75B, SNORD105B, SNORD126, SNORD3C and SNORD89 was performed with the SYBR qPCR Mix (Toyobo Co., LTD, Japan). 10 µL reaction system was adopted according to the manufacturer’s instructions and amplified for 40 cycles. The expression levels were normalized by U6. Relative expression was calculated using the method of 2 −ΔΔCt and the expression levels of snoRNAs were calculated using the 2 −ΔCt method [ 13 ]. Primer names and primer sequences are listed in the following tables (Table 2 - Table 3 ). Quantification of U6 was performed with a stem-loop real time PCR miRNA kit (Ribobio Co., LTD, China). Table 2 The forward and reverse primer sequence of the SnoRNAs Primer Name Primer Sequence SNORA58 Forward TTGCCTGACTGTGCTCATGTC SNORA58 Reverse GGGAAATGTTTAGAGTCCTGCAAT SNORD89 Forward CAAGAAAAGGCCGAATTGCA SNORD89 Reverse TTCGCTTCAGGATATTTTGTCATC SNORA70J Forward GCCAATTAAGCCGACTGAGTTC SNORA70J Reverse ACAGGCTGCATATACTACCAAGGAA SNORD3C Forward CGAGGAAGAGAGGTAGCGTTTTC SNORD3C Reverse CGGAGAGAAGAACGATCATCAA SNORA75B Forward AGAAGAGAGAATTCACAGAACTAGCG SNORA75B Reverse AGTGCAGGGTCCGAGGTATT SNORD126 Forward GCCATGATGAAATGCATGTTAAGTCC SNORD126 Reverse AGTGCAGGGTCCGAGGTATT SNORD105B Forward GACAGCACTTCTGCTGAGACG SNORD105B Reverse AGTGCAGGGTCCGAGGTATT SNORA11B Forward CCTCCTCTGTTTACAACACACCCA SNORA11B Reverse AGTGCAGGGTCCGAGGTATT SNORA36C Forward GGCAGCTTCCCTGTTCTGTT SNORA36C Reverse AGTGCAGGGTCCGAGGTATT Primers of SNORA58, SNORD89, SNORA70J and SNORD3C synthesized by probe method. The other primers were synthesized by stem-roop method from Sangon Biotech Company, and the RT-Primers as follows: Table 3 The RT-primer sequence of the SnoRNAs Primer Name Primer Sequence SNORA75B GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACGAATGT SNORD126 GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACCCTAGC SNORD105B GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACCCTTCC SNORA11B GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACTGTGTA SNORA36C GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACTTTGTA 2.4 Statistical analysis Univariate Cox regression analysis was used to screen out prognostic genes with P values of < 0.05. Then, multivariate cox regression analysis was adopted to establish a prognostic risk score model. According to the prognostic risk score model, each ovarian cancer patient had unique RiskScore, and the RiskScore was calculated by the risk score formula = β1*expression of gene 1 + β2*expression of gene 2 + β3*expression of gene 3 + …. + βn *expression of gene N. Paired t test were used to compare gene expression in ovarian cancer tissues and normal tissues. According to the median RiskScore, ovarian cancer patients were divided into high-risk group and low-risk group. Receiver operating characteristic (ROC) curves and KM plot curves were used to validate the prognostic model. The Log-rank (Mantel–Cox) test was used for survival analysis by GraphPad Prism 7.0. Differences were considered statistically significant when the P -value was < 0.05. 3 Results 3.1 Construction of prognostic model for ovarian cancer patients 432 snoRNAs were detected in ovarian cancer patients from TCGA. Univariate Cox survival analysis showed that 14 snoRNAs had an effect on the prognosis of ovarian cancer patients (Table 4 ). Multivariate Cox survival analysis was adopted to conduct prognostic model, and finally 9 snoRNAs were screened out. RiskScore = -0.7390*SNORA11B + 0.8479*SNORA36C -0.6813*SNORA58 + 2.2898*SNORA70J + 2.4864*SNORA75B - 0.4467*SNORD105B + 1.1156*SNORD126 + 3.3939*SNORD3C + 0.4938*SNORD89. According to the RiskScore formula, all ovarian cancer patients had a unique RiskScore, and we ranked the patients according to their RiskScore (Figure 1 A). Scatter plot was used to analyze the RiskScore, survival time and survival state of ovarian cancer patients, and we found that patients with higher RiskScore had lower survival time and more deaths than that with lower RiskScore (Figure 1 B). The expression of snoRNAs in the prognostic model was compared in patients with low RiskScore versus high RiskScore (Figure 1 C). In the RiskScore model, three snoRNAs had negative coefficient, and among of them, snoRD3C had the largest weight coefficient in the prognostic model (Table 5 and Figure 1 D). Moreover, we compared the survival time of ovarian cancer patients with high RiskScore to low RiskScore. Patients with high RiskScore had poorer prognosis than that low RiskScore (Figure 1 E). Table 4 Univariate Cox survival analysis showed that 14 snoRNAs had an effect on the prognosis of ovarian cancer patients gene HR z P value SNORD126 3.03664 3.519229 0.000433 SNORA70J 9.488882 3.448622 0.000563 SNORD3C 23.99664 3.174154 0.001503 SNORA75B 65.38938 3.010749 0.002606 SNORA58 0.626393 -2.47562 0.0133 SNORA11B 0.501669 -2.23471 0.025436 SNORA36C 2.031193 2.141889 0.032202 SNORD105B 0.62715 -2.05778 0.039611 SNORD89 1.271647 2.000204 0.045478 SNORD116-25 2.672161 1.99728 0.045795 SNORA30B 10.92123 1.988977 0.046704 SNORD116-2 1.406725 1.98367 0.047293 SNORD105 0.61692 -1.96876 0.04898 Table 5 The results of multivariate Cox survival analysis Gene coef exp(coef) se(coef) z P value SNORA11B -0.7390 0.4776 0.3184 -2.321 0.020275 SNORA36C 0.8479 2.3347 0.3426 2.475 0.013326 SNORA58 -0.6813 0.5060 0.2057 -3.312 0.000925 SNORA70J 2.2898 9.8732 0.6960 3.290 0.001002 SNORA75B 2.4864 12.0180 1.3625 1.825 0.068017 SNORD105B -0.4467 0.6397 0.2290 -1.951 0.051102 SNORD126 1.1156 3.0514 0.3231 3.453 0.000554 SNORD3C 3.3939 29.7825 1.0515 3.228 0.001248 SNORD89 0.4938 1.6385 0.1400 3.528 0.000419 3.2 High riskscore exist in patients with poor clinicopathological stratification In order to determine whether the RiskScore is related to the clinicopathological parameters of ovarian cancer patients, we analyzed the level of the RiskScore in different subgroup of the clinicopathological parameters. Results showed that patients with higher age, larger tumorsize, advanced stage and with tumor status had higher RiskScore versus the other subgroup ( P <0.05, Figure 2 A, 2 B, 2 D, 2 E). Although there is no statistic statistical significance, patients with lymphatic invasion had higher RiskScore (Figure 2 C). 3.3 snoRNAs in the prognostic model co-expressed with their host genes SnoRNAs exists in the introns of mRNA or LncRNA, and some of them co-expressed with their host genes[ 14 ]. We compared the correlation of snoRNAs and their host genes in ovarian cancer tissues. Results showed that 7 snoRNAs in the prognostic model had positive correlation with their host genes (Figure 3 A- 3 G). Among of them, the expression abundance of snoRA70J, alike its host gene, is very low in ovarian cancer tissues (Figure 3 G). Moreover, copy number variation (CNV) is a key regulator of gene expression, and some snoRNAs were significantly associated with their CNVs in various cancers [ 15 ]. SNORic database ( http://bioin fo.life.hust.edu.cn/SNORic) was used to examine the correlation between snoRNAs and their copy number variation (CNV). 5 of 9 snoRNAs in the prognostic model correlated with their CNVs, and SNORD105B had the strongest correction with its CNVs (Figure 3 H). 3.4 RiskScore is an independent prognostic factors of ovarian cancer patients In order to validate the accuracy and specificity of the RiskScore derived from the prognostic model we constructed, ROC curve was adopted. Results showed that the prognostic accuracy of the signature was 0.664, 0.653, 0.739 and 0.785 for 1, 3, 5 and 7 years in entire series which increased with time prolonging (Figure 4 A). Hence, the RiskScore has the greatest accuracy and specificity when predicting for 7 years. Further, univariate and multivariate Cox survival analysis were conducted to analyze factors that had effect on the prognosis of ovarian cancer patients. Univariate Cox survival analysis showed that age, RiskScore, Tumor Size were the dependent prognostic factors in ovarian cancer patients (Figure 4 B). Multivariate Cox survival analysis showed that RiskScore was the independent prognostic factors in ovarian cancer patients (Figure 4 C). Taken together, the RiskScore from nine snoRNA signature is a potentially helpful biomarker for predicting the prognosis for ovarian cancer patients. 3.5 RiskScore can be a good indicator for prognosis in different clinical subgroups In order to confirm whether the RiskScore in different clinical subgroups can be a good indicator for prognosis, KM plot analysis was used. Cancer status have an effect on the prognosis of patients, hence, we first stratified patients into, with tumor and tumor free, two groups. Then, each group was divided into high- and low-risk groups according to their median RiskScore. As results shown in Figure 5 A, patients in high-risk group had significantly shorter OS than those in low-risk group in with tumor group (Figure 5 A- 5 B). In addition, patients with high RiskScore in lymphatic invasion group had poorer prognosis (Figure 5 D- 5 E). However, RiskScore cannot discriminate tumor free group and no lymphatic invasion group (Figure 5 C and 5 F). These results showed that RiskScore can predict the prognosis of patients with tumor and lymphatic invasion better than the other relevant group. Besides, age, tumor size and stage are critical clinicopathological parameter affecting the prognosis of ovarian cancer patients [ 16 ]. Therefore, we divided the patients according these clinicopathological parameters, and then compared the prognosis of high RiskScore group to low RiskScore group. As results shown in Figure 5 G- 5 I, patients in high RiskScore group had significantly shorter OS than those in low RiskScore group no matter in age60 group ( P < 0.05, Figure 5 G- 5 I). Alike, the results in different tumor size group and stage group, patients with high RiskScore had poorer prognosis versus to patients with low RiskScore ( P < 0.05, Figure 5 J- 5 O). 3.6 Validation of the prognostic model derived from snoRNAs To further validate the prognostic value of the RiskScore derived from nine-snoRNAs for ovarian cancer, we randomly divided the patients into two groups. 125 and 250 cases included in the test group and validation group. Ovarian cancer patients of each group were ranked and divided into two groups according to the median RiskScore (Figure 6 A and 6 E). Scatter plot show that patients with high RiskScore had shorter overall survival and higher deaths (Figure 6 B and 6 F). Moreover, the expression of snoRNAs in the prognostic model was compared in test group and validation group (Figure 6 C and 6 G). And, KM plot analysis showed that patients with high RiskScore in test group and validation group had poorer prognosis versus to patients with low RiskScore (Figure 6 D and 6 H). Further, we recruited 5 paired clinical tissues to verify our research. Among of them, three cases were diagnosed as high-grade serous carcinoma with pleomorphic nuclei, high N/C ratio and active mitosis. Two cases was diagnosed as low-grade serous carcinoma composed of small cellular nests containing multiple psammoma bodies, uniform nuclei with mild to moderate atypia (Table 6 ). Table 6. Clinical pathological parameters of 5 paired ovarian cancer from clinical patients Sample RiskScore Histological Type P53 genotype 1 46.47163 high-grade serous carcinoma non-sense mutation 2 2.449066 low-grade serous carcinoma wild type 3 10.048 low-grade serous carcinoma wild type 4 21.526 high-grade serous carcinoma missense mutation 5 39.456 high-grade serous carcinoma missense mutation We tested the expression of snoRNAs in the 5 paired clinical tissues, 7 of 9 snoRNAs in the prognostic model, including SNORA11B, SNORA36C, SNORA58, SNORA70J, SNORA75B, SNORD3C, SNORD89, SNORD105B and SNORD126, down regulated in tumor tissues versus their paired normal tissues (Figure 6I). In addition, we performed H&E staining on tumor tissues, and immunohistochemistry was used to detect the expression of Ki67, P53 and P16 in tumor tissues. The multiplication capacity of tumor tissues were indicated through Ki-67 expression measured by immunohistochemistry assays. Among of the five clinical patients, sample 1 and sample 2 have the highest and lowest risk values, respectively (Table 6). And, results of H&E staining and immunohistochemistry in sample 1 and sample 2 are exhibited in the figure 6J. P53 protein was mutated in high-grade serous carcinoma with non-sense mutation in 1 case and missense mutation in 2 cases. The low-grade serous carcinoma exhibited wild type P53 expression. P16 block expression was found in high-grade serous carcinoma in contrast to mottled expression in low-grade serous carcinoma (Figure 6J). The positive rate of Ki67 in sample 1 was 64.9%, while the positive rate of Ki67 in sample 2 was 20.5% (Figure 6K). 4 Discussion Ovarian cancer is the second most common cause of gynecologic cancer death in women around the world but accounts for the highest mortality rate among these cancers [ 3 ]. According to the Global Cancer Observatory (GCO, https://gco.iarc.fr/ ), there are a total of 313,959 patients with ovarian cancer patients and 207,252 cases died from it. In recent years, the potential of snoRNA as biomarkers has been graduated recognized, for example, SNORD89 was identified as a prognostic biomarker and prospective therapeutic in ovarian cancer patients and breast cancer patients [ 17 , 18 ]. However, there is a lack of systematic and comprehensive research on snoRNA in ovarian cancer. In our study, we comprehensively analyzed the snoRNA in patients with ovarian cancer, and screened out 14 prognostic snoRNAs by Univariate Cox survival analysis (Table 4 ). Then, prognostic model was constructed by Multivariate Cox survival analysis, and 9 snoRNAs were included in the prognostic model (Table 5 ). Each patients with ovarian cancer has a unique RiskScore, and patients with high RiskScore had higher deaths and lower overall survival time (Figure 1 A, 1 B and 1 E). A good prognostic marker is often associated with multiple clinicopathological parameters. Hence, we analyzed the correlation of the RiskScore derived from the prognostic model with age, tumor size, lymphatic invasion, stage and tumor status of ovarian cancer patients. Results showed that RiskScore was significantly increased in patients with high-age group, large tumor, high-grade and with tumor status (Figure 2 A, 2 B, 2 D- 2 E). These results suggested that the RiskScore was higher in the group of ovarian cancer patients with high risk factors for the prognosis. In our research, we analyzed the correlation of snoRNAs in the prognostic model with their hostgenes. Among of them, 7 of 9 snoRNAs had positive correlation with their hostgenes in ovarian cancer tissues (Figure 3 ). The expression abundance of SNORA70J is very low, alike its host gene SNORA70J. CNV has been reported occurred in various cancers, and some snoRNAs were associated with their CNVs [ 19 ]. In our research, 5 of 9 snoRNAs in our prognostic model had correlation with their CNVs (Figure 4 A). And, the specificity and sensitivity of the RiskScore were verified by ROC curve, and we found that the area of 7 years achieved 0.785. These results showed that the model has the best effect in predicting the prognosis of 7 years in ovarian cancer patients. Univariate and multivariate Cox survival analysis showed the RiskScore was an independent prognostic factor in ovarian cancer patients (Figure 4 ). Stratified analysis of survival according to different clinical parameters were conducted. We found that RiskScore predict prognosis well in diverse ages and tumor size. However, RiskScore, in tumor free and no lymphatic invasion patients, could not predict patients’ prognosis well (Figure 5 ). We speculated that these results may be caused by the small number of experimental cases. Moreover, patients with ovarian cancer were randomly divided into two groups, and validate the RiskScore in each subgroup. All of the results showed that patients with high RiskScore had poorer prognosis versus to patients with low RiskScore (Figure 6 ). Further, we detected the expression of snoRNAs in 7 paired tissues, all of them, except SNORD3C and SNORD89, down regulated in ovarian cancer tissues compared to ovarian normal tissues (Figure 6 I). And, this result in accord with the previous research [ 17 ]. The RiskScore of sample 1 and sample 2 are 46.47 and 2.469, and this result indicate sample 1 had poorer prognosis versus sample 2. Moreover, the results of H&E staining and immunohistochemistry of Ki67, P53 and P16 confirmed that patients with high RiskScore are more malignant. The positive rate of Ki67 in sample 1 was 64.9%, and higher than that 20.5% in sample 2 (Figure 6 K). And, P16 block expression was found in sample 1 in contrast to mottled expression in sample2 (Figure 6 J). 5 Conclusions In summary, we identified a nine-snoRNAs signature as an independent indicator to predict prognosis of ovarian cancer patients, providing a prospective prognostic biomarker and potential therapeutic targets for ovarian cancer. Declarations Acknowledgements We would like to acknowledge TCGA for free use. Authors’ contributions WJZ designed the experiments. TZ, SHL, QNK and WMH performed the experiments. XZ and WJZ analyzed the experimental data. WH, FBZ and WJZ wrote and reviewed the manuscript. All authors read and approved the final manuscript. Funding Not applicable. Availability of data and materials The datasets analyzed during the current study are available in the TCGA repository, https://cancergeno me.nih.gov/. 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Zhao, Y., et al., Expression signature of six-snoRNA serves as novel non-invasive biomarker for diagnosis and prognosis prediction of renal clear cell carcinoma . J Cell Mol Med, 2020. 24 (3): p. 2215–2228. Cite Share Download PDF Status: Under Revision Version 1 posted Review # 1 received at journal 14 Nov, 2021 Reviewer # 2 agreed at journal 07 Nov, 2021 Reviews received at journal 03 Nov, 2021 Reviewer # 1 agreed at journal 02 Nov, 2021 Reviewers invited by journal 01 Nov, 2021 Editor invited by journal 29 Oct, 2021 Editor assigned by journal 27 Oct, 2021 First submitted to journal 27 Oct, 2021 Submission checks completed at journal 26 Oct, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-1022969","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Primary research","associatedPublications":[],"authors":[{"id":60229181,"identity":"a190354a-8874-4e88-9ad1-abc932ffbb5a","order_by":0,"name":"Wenjing Zhu","email":"","orcid":"https://orcid.org/0000-0003-0958-0905","institution":"Qingdao Municipal Hospital Group","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wenjing","middleName":"","lastName":"Zhu","suffix":""},{"id":60229182,"identity":"5df5f6d2-08a0-409d-afa0-16d5959b45d5","order_by":1,"name":"Tao Zhang","email":"","orcid":"","institution":"Qingdao Municipal Hospital Group","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Zhang","suffix":""},{"id":60229183,"identity":"d19feef4-b020-45ea-80dc-893deaea62e4","order_by":2,"name":"Shaohong Luan","email":"","orcid":"","institution":"Qingdao Municipal Hospital Group","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shaohong","middleName":"","lastName":"Luan","suffix":""},{"id":60229184,"identity":"66399f75-8891-4843-834c-be2cd6970a8f","order_by":3,"name":"Qingnuan Kong","email":"","orcid":"","institution":"Qingdao Municipal Hospital Group","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qingnuan","middleName":"","lastName":"Kong","suffix":""},{"id":60229185,"identity":"7ff9a9ad-a2b2-421d-a910-1583be0d005c","order_by":4,"name":"Wenmin Hu","email":"","orcid":"","institution":"Ocean University of China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wenmin","middleName":"","lastName":"Hu","suffix":""},{"id":60229186,"identity":"095fc595-8157-4b8e-9bea-ba2bc940d272","order_by":5,"name":"Xin Zou","email":"","orcid":"","institution":"Dalian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Zou","suffix":""},{"id":60229187,"identity":"8196f280-d5dd-4a6a-a6ee-113b49d0f241","order_by":6,"name":"Feibo Zheng","email":"","orcid":"","institution":"Qingdao Municipal Hospital Group","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Feibo","middleName":"","lastName":"Zheng","suffix":""},{"id":60229188,"identity":"7d5f388c-7b4a-4cdf-94a7-bbfa3d39a556","order_by":7,"name":"Wei Han","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqklEQVRIiWNgGAWjYBACAxDxgYEZziZOC+MMkrUw85CkxZz97DFpmzLrxAb25m0SDDV3CGux7MlLk845l57YwHOsTILh2DMiHHYgx+x2btvhxAaJHDMJxobDRGg5/8bstiVIi/wbYrXcANrCCLaFh2gtb8x/9pxLN27jSSu2SDhGlMNyjA1+lFnL9rMf3njjQw0RWiCADYwYGBKI1QBVPwpGwSgYBaMABwAAZ2Q3pcR9RMEAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-3508-0103","institution":"Qingdao University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Han","suffix":""}],"badges":[],"createdAt":"2021-10-27 08:07:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1022969/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1022969/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":15104513,"identity":"eb7a020e-916e-443c-bb38-7fa4c9a5fb16","added_by":"auto","created_at":"2021-11-01 17:54:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":136854,"visible":true,"origin":"","legend":"The prognostic model of ovarian cancer patients. A: the RiskScore of ovarian cancer patients in TCGA database was ranked from low to high, B: Scatter plot was drawn including RiskScore, survival time and survival state of ovarian cancer patients, C: the expression heatmap of snoRNAs in patients with low RiskScore and high RiskScore, D: the coefficient of snoRNAs in the prognostic model, E: K-M plot survival curve of ovarian cancer patients with low RiskScore versus high RiskScore.","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-1022969/v1/9b3c66f41841f7956751c681.png"},{"id":15104510,"identity":"ff9a32d2-bbdd-441d-b2e7-e3c551789348","added_by":"auto","created_at":"2021-11-01 17:54:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":63291,"visible":true,"origin":"","legend":"The riskscore in patients with different clinicopathological stratification. A, B: patients with higher age and larger tumorsize had higher RiskScore versus the other subgroup (P\u003c0.05), C: patients with lymphatic invasion had higher RiskScore (P\u003e0.05), D, E: patients with advanced stage and with tumor status had higher RiskScore versus the other subgroup (P\u003c0.05).","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-1022969/v1/39523aeef6f06c470c8cab74.png"},{"id":15104656,"identity":"9af689c3-7af5-4fd9-8e1c-0568741983c6","added_by":"auto","created_at":"2021-11-01 17:57:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":109590,"visible":true,"origin":"","legend":"7 snoRNAs in the prognostic model co-expressed with their host genes. A: SNORA36C, B: SNORA11B, C: SNORD105B, D: SNORA58, E: SNORD89, F: SNORD126, G: SNORA70J, H: 5 snoRNAs in the prognostic model correlated with their CNVs.","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-1022969/v1/99cfd6bd18892efc4dae04e2.png"},{"id":15104512,"identity":"71fcb6a4-359e-48a8-919f-58516154a0f7","added_by":"auto","created_at":"2021-11-01 17:54:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":35981,"visible":true,"origin":"","legend":"RiskScore derived from the prognostic model is an independent prognostic factors of ovarian cancer patients. A: ROC curves of the RiskScore in 1, 3, 5 and 7 years, B and C: univariate and multivariate Cox survival analysis were conducted to analyze factors that had effect on the prognosis of ovarian cancer patients.","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-1022969/v1/d430dff4888fddfd1efba0c7.png"},{"id":15104515,"identity":"9577bb2b-14ef-44ad-bdfc-c26ecb531099","added_by":"auto","created_at":"2021-11-01 17:54:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":191004,"visible":true,"origin":"","legend":"KM plot analysis of RiskScore in different clinical subgroups. A-C: KM plot analysis of RiskScore in different tumor status, D-F: KM plot analysis of RiskScore in different lymphatic invasion status, G-O: KM plot analysis of RiskScore in different age, tumor size and stage.","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-1022969/v1/e313eabd942123196fc81c46.png"},{"id":15104657,"identity":"cb8062e5-d5a8-4130-9607-4af949e03791","added_by":"auto","created_at":"2021-11-01 17:57:03","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":465881,"visible":true,"origin":"","legend":"Validation of the prognostic model derived from snoRNAs. A and E: The RiskScore of patients in test group and validation group, B and F: Scatter plot was drawn including RiskScore, survival time and survival state of ovarian cancer patients in test group and validation group, C and G: the expression heatmap of snoRNAs in patients with low RiskScore and high RiskScore, D and H: K-M plot survival curve of ovarian cancer patients with low RiskScore versus high RiskScore in test group and validation group, I: the expression of snoRNAs in clinical tumor tissues versus normal tissues, J: HE staining of clinical tumor tissues, and immunohistochemistry was used to detect the expression of Ki67, P53 and P16 in tumor tissues, K: the percentage of Ki67 positive cells in sample 1 versus sample 2. * P\u003c0.05, **P\u003c0.01, ***P\u003c0.001.","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-1022969/v1/4b955c7de4ed0c0a39f7918e.png"},{"id":15104659,"identity":"037af6a6-ccd6-499a-b2ee-cbf076140e6c","added_by":"auto","created_at":"2021-11-01 17:57:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1969905,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1022969/v1/7e1dd79b-cb3b-4979-afa1-e026f02d09f3.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eIdentification of SnoRNAs Predicting Prognosis for Ovarian Cancer\u003c/p\u003e","fulltext":[{"header":"1 Background","content":"\u003cp\u003eOvarian cancer is one of the most common gynecologic malignancies with a poor prognosis[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite other cancers such as endometrial cancer having higher rates of incidence, mortality of ovarian cancer rates still listed to be high [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. More than 75% of ovarian cancers are diagnosed at advanced or metastatic stage [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Besides, most women diagnosed with high-grade serous ovarian cancer develop recurrent disease and chemotherapy resistance, despite initially respond to this treatment, recurrence is likely to occur within a median of 16 months for patients who present with advanced stage disease [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. At present, identifying and discovering effective biomarkers and realizing molecular targeted therapy are considered to be an effective treatment for ovarian cancer [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Consequently, finding effective therapeutic target molecules for ovarian cancer is an urgent problem to be solved.\u003c/p\u003e \u003cp\u003eSmall nucleolar RNAs (snoRNAs) are a class of non-coding RNAs with 60-300nt, and mainly divided into two classes: C/D box snoRNAs and H/ACA box snoRNAs [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Traditionally, they act as the role of modifying 2'-O-ribose methylation and pseudouridylation of ribosomal RNAs (rRNAs), respectively [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Emerging evidence has demonstrated that small nucleolar RNAs (snoRNAs) play significant roles in tumorigenesis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Such as, snoRNA U3, a box of C/D RNA, could be processed to smaller RNAs just as miRNA and perform the function of miRNA in cancer [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Moreover, snoRNAs had been reported to play a critical determinants of leukaemic stem cell activity, and disruption of H/ACA snoRNA levels in stem cells impairs pluripotency [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Further, other research suggested that snoRNAs participated in the regulation of mRNA abundance, alternative splicing and metabolic and oxidative stress [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent study showed that snoRNAs could act as diagnostic markers, prognostic markers and therapeutic targets in various cancers [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The number of dysregulated snoRNAs in ovarian cancer is up to 462 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], however, there is no study on snoRNA had been conducted in ovarian cancer.\u003c/p\u003e \u003cp\u003eIn our research, we screened prognostic snoRNAs, and constructed a risk model to predict the prognosis of ovarian cancer patients. This may provide new ideas and targets for the clinical treatment of ovarian cancer.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003ch2\u003e2.1 Data sets\u003c/h2\u003e\n \u003cp\u003eThe data of patients with ovarian cancer in TCGA, including mRNA-Seq of transcriptome profiling data and clinical data, was downloaded by the GDC data portal: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003c/span\u003e. The detailed clinical pathological parameters of patients with ovarian cancer, including age, subdivision, lymphatic invasion, grade, race, stage, tumor size and venous invasion of ovarian cancer, were listed in the following Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eClinical pathological parameters of ovarian cancer patients in TCGA database\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClinical pathological parameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubdivision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eleft or right\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBilateral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLymphatic invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG1\u0026lrm;+G2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG3+G4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack or African American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage1+2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage3+4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo Macroscopic disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;20mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e༞20mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVenous invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec4\"\u003e\n \u003ch2\u003e2.2 Patients and clinical specimens\u003c/h2\u003e\n \u003cp\u003eWe recruited 5 pairs of matched ovarian cancer tissues and normal tissues from Chinese Institution. Among of them, three cases were diagnosed as high-grade serous carcinoma with pleomorphic nuclei, high N/C ratio and active mitosis. One case was diagnosed as low-grade serous carcinoma composed of small cellular nests containing multiple psammoma bodies, uniform nuclei with mild to moderate atypia. One case was diagnosed as endometrioid adenocarcinoma which displayed tubular pattern and nests.\u003c/p\u003e\n \u003cp\u003eThese tissue samples and corresponding clinical pathology data were collected from Qingdao Municipal Hospital. This study was approved by Institutional Review Board of Qingdao Municipal Hospital. The number of the approval of this study by the ethical committee is No.018. And the approval document was approved on September, 2021.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec5\"\u003e\n \u003ch2\u003e2.3 RNA isolation and quantitative real‑time PCR (qRT‑PCR)\u003c/h2\u003e\n \u003cp\u003eFor tissue RNA isolation, 1 mL AG RNAex Pro Reagent (Accurate Biotechnology Co.) was added to 50 mg of tissue and total RNA samples were extracted according to the manufacturer\u0026rsquo;s instructions. Purified RNA was quantified resort to NanoVue (GE Healthcare Life Sciences). cDNAs were synthesized from total RNAs by using RT reagent Kit (Takara Co., LTD, Japan) and ReverTra Ace qPCR RT Kit (Toyobo Co., LTD, Japan).\u003c/p\u003e\n \u003cp\u003eqRT-PCR of U6, SNORA11B, SNORA36C, SNORA58, SNORA70J, SNORA75B, SNORD105B, SNORD126, SNORD3C and SNORD89 was performed with the SYBR qPCR Mix (Toyobo Co., LTD, Japan). 10 \u0026micro;L reaction system was adopted according to the manufacturer\u0026rsquo;s instructions and amplified for 40 cycles. The expression levels were normalized by U6. Relative expression was calculated using the method of 2\u003csup\u003e\u0026minus;\u0026Delta;\u0026Delta;Ct\u003c/sup\u003e and the expression levels of snoRNAs were calculated using the 2\u003csup\u003e\u0026minus;\u0026Delta;Ct\u003c/sup\u003e method [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. Primer names and primer sequences are listed in the following tables (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e - Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Quantification of U6 was performed with a stem-loop real time PCR miRNA kit (Ribobio Co., LTD, China).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe forward and reverse primer sequence of the SnoRNAs\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePrimer Name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePrimer Sequence\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORA58 Forward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTTGCCTGACTGTGCTCATGTC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORA58 Reverse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGGGAAATGTTTAGAGTCCTGCAAT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORD89 Forward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAAGAAAAGGCCGAATTGCA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORD89 Reverse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTTCGCTTCAGGATATTTTGTCATC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORA70J Forward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGCCAATTAAGCCGACTGAGTTC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORA70J Reverse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACAGGCTGCATATACTACCAAGGAA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORD3C Forward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCGAGGAAGAGAGGTAGCGTTTTC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORD3C Reverse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCGGAGAGAAGAACGATCATCAA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORA75B Forward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAGAAGAGAGAATTCACAGAACTAGCG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORA75B Reverse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAGTGCAGGGTCCGAGGTATT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORD126 Forward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGCCATGATGAAATGCATGTTAAGTCC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORD126 Reverse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAGTGCAGGGTCCGAGGTATT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORD105B Forward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGACAGCACTTCTGCTGAGACG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORD105B Reverse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAGTGCAGGGTCCGAGGTATT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORA11B Forward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCTCCTCTGTTTACAACACACCCA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORA11B Reverse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAGTGCAGGGTCCGAGGTATT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORA36C Forward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGGCAGCTTCCCTGTTCTGTT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORA36C Reverse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAGTGCAGGGTCCGAGGTATT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003ePrimers of SNORA58, SNORD89, SNORA70J and SNORD3C synthesized by probe method. The other primers were synthesized by stem-roop method from Sangon Biotech Company, and the RT-Primers as follows:\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe RT-primer sequence of the SnoRNAs\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePrimer Name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePrimer Sequence\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORA75B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACGAATGT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORD126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACCCTAGC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORD105B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACCCTTCC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORA11B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACTGTGTA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORA36C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACTTTGTA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec6\"\u003e\n \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e\n \u003cp\u003eUnivariate Cox regression analysis was used to screen out prognostic genes with \u003cem\u003eP\u003c/em\u003e values of \u0026lt; 0.05. Then, multivariate cox regression analysis was adopted to establish a prognostic risk score model. According to the prognostic risk score model, each ovarian cancer patient had unique RiskScore, and the RiskScore was calculated by the risk score formula\u0026thinsp;=\u0026thinsp;\u0026beta;1*expression of gene 1\u0026thinsp;+\u0026thinsp;\u0026beta;2*expression of gene 2\u0026thinsp;+\u0026thinsp;\u0026beta;3*expression of gene 3 + \u0026hellip;.\u0026thinsp;+\u0026thinsp;\u0026beta;n *expression of gene N. Paired t test were used to compare gene expression in ovarian cancer tissues and normal tissues. According to the median RiskScore, ovarian cancer patients were divided into high-risk group and low-risk group. Receiver operating characteristic (ROC) curves and KM plot curves were used to validate the prognostic model. The Log-rank (Mantel\u0026ndash;Cox) test was used for survival analysis by GraphPad Prism 7.0. Differences were considered statistically significant when the \u003cem\u003eP\u003c/em\u003e-value was \u0026lt; 0.05.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv class=\"Section2\" id=\"Sec8\"\u003e\n \u003ch2\u003e3.1 Construction of prognostic model for ovarian cancer patients\u003c/h2\u003e\n \u003cp\u003e432 snoRNAs were detected in ovarian cancer patients from TCGA. Univariate Cox survival analysis showed that 14 snoRNAs had an effect on the prognosis of ovarian cancer patients (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Multivariate Cox survival analysis was adopted to conduct prognostic model, and finally 9 snoRNAs were screened out. RiskScore = -0.7390*SNORA11B + 0.8479*SNORA36C -0.6813*SNORA58 + 2.2898*SNORA70J + 2.4864*SNORA75B - 0.4467*SNORD105B + 1.1156*SNORD126 + 3.3939*SNORD3C + 0.4938*SNORD89.\u003c/p\u003e\n \u003cp\u003eAccording to the RiskScore formula, all ovarian cancer patients had a unique RiskScore, and we ranked the patients according to their RiskScore (Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). Scatter plot was used to analyze the RiskScore, survival time and survival state of ovarian cancer patients, and we found that patients with higher RiskScore had lower survival time and more deaths than that with lower RiskScore (Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). The expression of snoRNAs in the prognostic model was compared in patients with low RiskScore versus high RiskScore (Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC). In the RiskScore model, three snoRNAs had negative coefficient, and among of them, snoRD3C had the largest weight coefficient in the prognostic model (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD). Moreover, we compared the survival time of ovarian cancer patients with high RiskScore to low RiskScore. Patients with high RiskScore had poorer prognosis than that low RiskScore (Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab4\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnivariate Cox survival analysis showed that 14 snoRNAs had an effect on the prognosis of ovarian cancer patients\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003egene\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ez\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORD126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.03664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.519229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000433\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORA70J\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.488882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.448622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000563\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORD3C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.99664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.174154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001503\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORA75B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.38938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.010749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002606\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORA58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.626393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.47562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORA11B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.501669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.23471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.025436\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORA36C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.031193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.141889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.032202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORD105B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.05778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.039611\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORD89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.271647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.000204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.045478\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORD116-25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.672161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.99728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.045795\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORA30B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.92123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.988977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.046704\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORD116-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.406725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.98367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.047293\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORD105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.96876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04898\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab5\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe results of multivariate Cox survival analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ecoef\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eexp(coef)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ese(coef)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ez\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORA11B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.7390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.020275\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORA36C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.3347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.013326\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORA58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.6813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000925\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORA70J\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.2898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.8732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORA75B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.4864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.0180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.3625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.068017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORD105B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.4467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.051102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORD126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.1156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.0514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000554\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORD3C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.3939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.7825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001248\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNORD89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.6385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000419\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec9\"\u003e\n \u003ch2\u003e3.2 High riskscore exist in patients with poor clinicopathological stratification\u003c/h2\u003e\n \u003cp\u003eIn order to determine whether the RiskScore is related to the clinicopathological parameters of ovarian cancer patients, we analyzed the level of the RiskScore in different subgroup of the clinicopathological parameters. Results showed that patients with higher age, larger tumorsize, advanced stage and with tumor status had higher RiskScore versus the other subgroup (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB, \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD, \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE). Although there is no statistic statistical significance, patients with lymphatic invasion had higher RiskScore (Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec10\"\u003e\n \u003ch2\u003e3.3 snoRNAs in the prognostic model co-expressed with their host genes\u003c/h2\u003e\n \u003cp\u003eSnoRNAs exists in the introns of mRNA or LncRNA, and some of them co-expressed with their host genes[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]. We compared the correlation of snoRNAs and their host genes in ovarian cancer tissues. Results showed that 7 snoRNAs in the prognostic model had positive correlation with their host genes (Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA-\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eG). Among of them, the expression abundance of snoRA70J, alike its host gene, is very low in ovarian cancer tissues (Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eG). Moreover, copy number variation (CNV) is a key regulator of gene expression, and some snoRNAs were significantly associated with their CNVs in various cancers [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]. SNORic database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioin\u003c/span\u003e\u003c/span\u003e fo.life.hust.edu.cn/SNORic) was used to examine the correlation between snoRNAs and their copy number variation (CNV). 5 of 9 snoRNAs in the prognostic model correlated with their CNVs, and SNORD105B had the strongest correction with its CNVs (Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eH).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec11\"\u003e\n \u003ch2\u003e3.4 RiskScore is an independent prognostic factors of ovarian cancer patients\u003c/h2\u003e\n \u003cp\u003eIn order to validate the accuracy and specificity of the RiskScore derived from the prognostic model we constructed, ROC curve was adopted. Results showed that the prognostic accuracy of the signature was 0.664, 0.653, 0.739 and 0.785 for 1, 3, 5 and 7 years in entire series which increased with time prolonging (Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). Hence, the RiskScore has the greatest accuracy and specificity when predicting for 7 years.\u003c/p\u003e\n \u003cp\u003eFurther, univariate and multivariate Cox survival analysis were conducted to analyze factors that had effect on the prognosis of ovarian cancer patients. Univariate Cox survival analysis showed that age, RiskScore, Tumor Size were the dependent prognostic factors in ovarian cancer patients (Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). Multivariate Cox survival analysis showed that RiskScore was the independent prognostic factors in ovarian cancer patients (Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC). Taken together, the RiskScore from nine snoRNA signature is a potentially helpful biomarker for predicting the prognosis for ovarian cancer patients.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec12\"\u003e\n \u003ch2\u003e3.5 RiskScore can be a good indicator for prognosis in different clinical subgroups\u003c/h2\u003e\n \u003cp\u003eIn order to confirm whether the RiskScore in different clinical subgroups can be a good indicator for prognosis, KM plot analysis was used. Cancer status have an effect on the prognosis of patients, hence, we first stratified patients into, with tumor and tumor free, two groups. Then, each group was divided into high- and low-risk groups according to their median RiskScore. As results shown in Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA, patients in high-risk group had significantly shorter OS than those in low-risk group in with tumor group (Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA-\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB). In addition, patients with high RiskScore in lymphatic invasion group had poorer prognosis (Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eD-\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eE). However, RiskScore cannot discriminate tumor free group and no lymphatic invasion group (Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC and \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eF). These results showed that RiskScore can predict the prognosis of patients with tumor and lymphatic invasion better than the other relevant group.\u003c/p\u003e\n \u003cp\u003eBesides, age, tumor size and stage are critical clinicopathological parameter affecting the prognosis of ovarian cancer patients [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. Therefore, we divided the patients according these clinicopathological parameters, and then compared the prognosis of high RiskScore group to low RiskScore group. As results shown in Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eG-\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eI, patients in high RiskScore group had significantly shorter OS than those in low RiskScore group no matter in age\u0026lt;=60 or age\u0026gt;60 group (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eG-\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eI). Alike, the results in different tumor size group and stage group, patients with high RiskScore had poorer prognosis versus to patients with low RiskScore (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eJ-\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eO).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec13\"\u003e\n \u003ch2\u003e3.6 Validation of the prognostic model derived from snoRNAs\u003c/h2\u003e\n \u003cp\u003eTo further validate the prognostic value of the RiskScore derived from nine-snoRNAs for ovarian cancer, we randomly divided the patients into two groups. 125 and 250 cases included in the test group and validation group. Ovarian cancer patients of each group were ranked and divided into two groups according to the median RiskScore (Figure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA and \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eE). Scatter plot show that patients with high RiskScore had shorter overall survival and higher deaths (Figure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB and \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eF). Moreover, the expression of snoRNAs in the prognostic model was compared in test group and validation group (Figure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC and \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eG). And, KM plot analysis showed that patients with high RiskScore in test group and validation group had poorer prognosis versus to patients with low RiskScore (Figure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eD and \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eH).\u003c/p\u003e\n \u003cp\u003eFurther, we recruited 5 paired clinical tissues to verify our research. Among of them, three cases were diagnosed as high-grade serous carcinoma with pleomorphic nuclei, high N/C ratio and active mitosis. Two cases was diagnosed as low-grade serous carcinoma composed of small cellular nests containing multiple psammoma bodies, uniform nuclei with mild to moderate atypia (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 6. Clinical pathological parameters of 5 paired ovarian cancer from clinical patients\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.93859649122807%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.44736842105263%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRiskScore\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.473684210526315%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistological Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.140350877192983%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP53 genotype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.93859649122807%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.44736842105263%\"\u003e\n \u003cp\u003e46.47163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.473684210526315%\"\u003e\n \u003cp\u003ehigh-grade serous carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.140350877192983%\"\u003e\n \u003cp\u003enon-sense mutation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.93859649122807%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.44736842105263%\"\u003e\n \u003cp\u003e2.449066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.473684210526315%\"\u003e\n \u003cp\u003elow-grade serous carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.140350877192983%\"\u003e\n \u003cp\u003ewild type\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.93859649122807%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.44736842105263%\"\u003e\n \u003cp\u003e10.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.473684210526315%\"\u003e\n \u003cp\u003elow-grade serous carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.140350877192983%\"\u003e\n \u003cp\u003ewild type\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.93859649122807%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.44736842105263%\"\u003e\n \u003cp\u003e21.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.473684210526315%\"\u003e\n \u003cp\u003ehigh-grade serous carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.140350877192983%\"\u003e\n \u003cp\u003emissense mutation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.93859649122807%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.44736842105263%\"\u003e\n \u003cp\u003e39.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.473684210526315%\"\u003e\n \u003cp\u003ehigh-grade serous carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.140350877192983%\"\u003e\n \u003cp\u003emissense mutation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eWe tested the expression of snoRNAs in the 5 paired clinical tissues, 7 of 9 snoRNAs in the prognostic model, including SNORA11B, SNORA36C, SNORA58, SNORA70J, SNORA75B, SNORD3C, SNORD89, SNORD105B and SNORD126, down regulated in tumor tissues versus their paired normal tissues (Figure 6I). In addition, we performed H\u0026amp;E staining on tumor tissues, and immunohistochemistry was used to detect the expression of Ki67, P53 and P16 in tumor tissues. The multiplication capacity of tumor tissues were indicated through Ki-67 expression measured by immunohistochemistry assays. Among of the five clinical patients, sample 1 and sample 2 have the highest and lowest risk values, respectively (Table 6). And, results of H\u0026amp;E staining and immunohistochemistry in sample 1 and sample 2 are exhibited in the figure 6J. P53 protein was mutated in high-grade serous carcinoma with non-sense mutation in 1 case and missense mutation in 2 cases. The low-grade serous carcinoma exhibited wild type P53 expression. P16 block expression was found in high-grade serous carcinoma in contrast to mottled expression in low-grade serous carcinoma (Figure 6J). The positive rate of Ki67 in sample 1 was 64.9%, while the positive rate of Ki67 in sample 2 was 20.5% (Figure 6K).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eOvarian cancer is the second most common cause of gynecologic cancer death in women around the world but accounts for the highest mortality rate among these cancers [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. According to the Global Cancer Observatory (GCO, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gco.iarc.fr/\u003c/span\u003e\u003c/span\u003e), there are a total of 313,959 patients with ovarian cancer patients and 207,252 cases died from it.\u003c/p\u003e \u003cp\u003eIn recent years, the potential of snoRNA as biomarkers has been graduated recognized, for example, SNORD89 was identified as a prognostic biomarker and prospective therapeutic in ovarian cancer patients and breast cancer patients [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, there is a lack of systematic and comprehensive research on snoRNA in ovarian cancer. In our study, we comprehensively analyzed the snoRNA in patients with ovarian cancer, and screened out 14 prognostic snoRNAs by Univariate Cox survival analysis (Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Then, prognostic model was constructed by Multivariate Cox survival analysis, and 9 snoRNAs were included in the prognostic model (Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Each patients with ovarian cancer has a unique RiskScore, and patients with high RiskScore had higher deaths and lower overall survival time (Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eA good prognostic marker is often associated with multiple clinicopathological parameters. Hence, we analyzed the correlation of the RiskScore derived from the prognostic model with age, tumor size, lymphatic invasion, stage and tumor status of ovarian cancer patients. Results showed that RiskScore was significantly increased in patients with high-age group, large tumor, high-grade and with tumor status (Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). These results suggested that the RiskScore was higher in the group of ovarian cancer patients with high risk factors for the prognosis.\u003c/p\u003e \u003cp\u003eIn our research, we analyzed the correlation of snoRNAs in the prognostic model with their hostgenes. Among of them, 7 of 9 snoRNAs had positive correlation with their hostgenes in ovarian cancer tissues (Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The expression abundance of SNORA70J is very low, alike its host gene SNORA70J. CNV has been reported occurred in various cancers, and some snoRNAs were associated with their CNVs [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In our research, 5 of 9 snoRNAs in our prognostic model had correlation with their CNVs (Figure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eAnd, the specificity and sensitivity of the RiskScore were verified by ROC curve, and we found that the area of 7 years achieved 0.785. These results showed that the model has the best effect in predicting the prognosis of 7 years in ovarian cancer patients. Univariate and multivariate Cox survival analysis showed the RiskScore was an independent prognostic factor in ovarian cancer patients (Figure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStratified analysis of survival according to different clinical parameters were conducted. We found that RiskScore predict prognosis well in diverse ages and tumor size. However, RiskScore, in tumor free and no lymphatic invasion patients, could not predict patients\u0026rsquo; prognosis well (Figure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). We speculated that these results may be caused by the small number of experimental cases.\u003c/p\u003e \u003cp\u003eMoreover, patients with ovarian cancer were randomly divided into two groups, and validate the RiskScore in each subgroup. All of the results showed that patients with high RiskScore had poorer prognosis versus to patients with low RiskScore (Figure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Further, we detected the expression of snoRNAs in 7 paired tissues, all of them, except SNORD3C and SNORD89, down regulated in ovarian cancer tissues compared to ovarian normal tissues (Figure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI). And, this result in accord with the previous research [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The RiskScore of sample 1 and sample 2 are 46.47 and 2.469, and this result indicate sample 1 had poorer prognosis versus sample 2. Moreover, the results of H\u0026amp;E staining and immunohistochemistry of Ki67, P53 and P16 confirmed that patients with high RiskScore are more malignant. The positive rate of Ki67 in sample 1 was 64.9%, and higher than that 20.5% in sample 2 (Figure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eK). And, P16 block expression was found in sample 1 in contrast to mottled expression in sample2 (Figure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eJ).\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eIn summary, we identified a nine-snoRNAs signature as an independent indicator to predict prognosis of ovarian cancer patients, providing a prospective prognostic biomarker and potential therapeutic targets for ovarian cancer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge TCGA for free use.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWJZ designed the experiments. TZ, SHL, QNK and WMH performed the experiments. XZ and WJZ analyzed the experimental data. WH, FBZ and WJZ wrote and reviewed the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available in the TCGA repository, https://cancergeno me.nih.gov/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patients consented to the institutional review board which allows comprehensive analysis of tumor specimens.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKuroki, L. and S.R. 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J Cell Mol Med, 2020. \u003cb\u003e24\u003c/b\u003e(3): p.\u0026nbsp;2215\u0026ndash;2228.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cancer-cell-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ccin","sideBox":"Learn more about [Cancer Cell International](http://cancerci.biomedcentral.com/)","snPcode":"12935","submissionUrl":"https://submission.nature.com/new-submission/12935/3","title":"Cancer Cell International","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"ovarian cancer, snoRNA, biomarker, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-1022969/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1022969/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eIncreasing evidence has been confirmed that small nucleolar RNAs (SnoRNAs) play critical roles in tumorigenesis and exhibit prognostic value in clinical practice. However, there is short of systematic research on SnoRNAs in ovarian cancer (OV).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMaterial/methods: \u003c/strong\u003e379 OV patients with RNA-Seq and clinical parameters from TCGA database and 5 paired clinical OV tissues were embedded in our study. Cox regression analysis was used to identify prognostic SnoRNAs and construct prediction model. SNORic database was adopted to examine the copy number variation of snoRNAs. ROC curves and KM plot curves were applied to validate the prediction model. Besides, the model was validated in 5 paired clinical tissues by real-time PCR, H\u0026amp;E staining and immunohistochemistry. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA prognostic model was constructed on the basis of SnoRNAs in OV patients.\u003c/p\u003e\u003cp\u003ePatients with higher RiskScore had poor clinicopathological parameters, including higher age, larger tumorsize, advanced stage and with tumor status. KM plot analysis confirmed that patients with high RiskScore had poorer prognosis in subgroup of age, tumor size and stage. 7 of 9 snoRNAs in the prognostic model had positive correlation with their host genes. Moreover, 5 of 9 snoRNAs in the prognostic model correlated with their CNVs, and SNORD105B had the strongest correction with its CNVs. ROC curve showed that the RiskScore had excellent specificity and accuracy. Further, H\u0026amp;E staining and immunohistochemistry of Ki67, P53 and P16 were confirmed that patients with higher RiskScore are more malignant. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eIn summary, we identified a nine-snoRNAs signature as an independent indicator to predict prognosis of OV, providing a prospective prognostic biomarker and potential therapeutic targets for ovarian cancer.\u003c/p\u003e","manuscriptTitle":"Identification of SnoRNAs Predicting Prognosis for Ovarian Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-11-01 17:54:01","doi":"10.21203/rs.3.rs-1022969/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2021-11-15T00:00:00+00:00","index":1,"fulltext":"Recommendation: Reviewer's comments unavailable due to the journal's policy.\n"},{"type":"reviewerAgreed","content":"","date":"2021-11-08T00:00:00+00:00","index":2,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-11-03T13:35:28+00:00","index":0,"fulltext":""},{"type":"reviewerAgreed","content":"","date":"2021-11-03T00:00:00+00:00","index":1,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-11-01T09:09:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Cancer Cell International","date":"2021-10-29T05:39:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-10-27T13:49:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Cell International","date":"2021-10-27T04:06:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2021-10-26T23:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-cell-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ccin","sideBox":"Learn more about [Cancer Cell International](http://cancerci.biomedcentral.com/)","snPcode":"12935","submissionUrl":"https://submission.nature.com/new-submission/12935/3","title":"Cancer Cell International","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ed26700b-0de8-4175-80b1-e9661db8600c","owner":[],"postedDate":"November 1st, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":8235902,"name":"Molecular Biology"},{"id":8235903,"name":"Oncology"},{"id":8235904,"name":"General Biochemistry"}],"tags":[],"updatedAt":"2022-07-02T04:25:08+00:00","versionOfRecord":[],"versionCreatedAt":"2021-11-01 17:54:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1022969","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1022969","identity":"rs-1022969","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}