Identification of SnoRNAs Predicting Prognosis for Ovarian Cancer

• DOI: 10.21203/rs.3.rs-1022969/v1
• Publication Date: 2021-11-01T17:54:08Z
• Authors: Wenjing Zhu; Tao Zhang; Shaohong Luan; Qingnuan Kong; Wenmin Hu; Xin Zou; Feibo Zheng; Wei Han
• Keywords: Small nucleolar RNA

Abstract Background: Increasing evidence has been confirmed that small nucleolar RNAs (SnoRNAs) play critical roles in tumorigenesis and exhibit prognostic value clinical practice. However, there is short of systematic research on SnoRNAs ovarian cancer (OV). Material/methods: 379 OV patients with RNA-Seq parameters from TCGA database 5 paired tissues were embedded our study. Cox regression analysis was used to identify construct prediction model. SNORic adopted examine the copy number variation snoRNAs. ROC curves KM plot applied validate Besides, model validated by real-time PCR, H&E staining immunohistochemistry. Results: A constructed basis patients.Patients higher RiskScore had poor clinicopathological parameters, including age, larger tumorsize, advanced stage tumor status. high poorer prognosis subgroup size stage. 7 9 snoRNAs positive correlation their host genes. Moreover, correlated CNVs, SNORD105B strongest correction its CNVs. curve showed excellent specificity accuracy. Further, immunohistochemistry Ki67, P53 P16 are more malignant. Conclusions: In summary, we identified a nine-snoRNAs signature as an independent indicator predict OV, providing prospective biomarker potential therapeutic targets for cancer.