Identification of a 10-pseudogenes signature as a novel prognosis biomarker for ovarian cancer

作     者：YONGHUI YU SONGHUI XU ERYONG ZHAO YONGSHUN DONG JINBIN CHEN BOQI RAO JIE ZENG LEI YANG JIACHUN LU FUMAN QIU 

作者机构：State Key Laboratory of Respiratory DiseaseInstitute for Chemical CarcinogenesisCollaborative Innovation Center for Environmental ToxicityGuangzhou Medical UniversityGuangzhou510182China Research Center of Medical SciencesGuangdong Academy of Medical SciencesGuangzhou510080China Department of Obstetrics and GynecologyGuangzhou Women and Children’s Medical CenterGuangzhou510000China Department of Obstetrics and GynecologyThe Third Affiliated HospitalGuangzhou Medical UniversityGuangzhou510150China 

出 版 物：《BIOCELL》 (生物细胞（英文）)

年 卷 期：2022年第46卷第4期

页      面：999-1011页

核心收录：

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

基　　金：supported by the National Natural Science Foundation of China Grants 81872127,81602289(FQ) 81872694,81673267,81473040(JL) 81402753,81672303,81871876(LY) Guangzhou Science Research Program General Project Grant 201707010123(FQ) Guangzhou Municipal Scientific Research Project Grant 1201630073(FQ) Guangdong High School Young Innovative Talents Project Grant 2015KQNCX136(FQ) 

主　　题：Pseudogene Ovarian cancer Prognosis Risk signature Immune infiltration 

摘      要：The outcomes of ovarian cancer are complicated and usually unfavorable due to their diagnoses at a late *** the efficient prognostic biomarkers to improve the survival of ovarian cancer is urgently *** survival-related pseudogenes retrieved from the Cancer Genome Atlas database were screened by univariate Cox regression analysis and further assessed by least absolute shrinkage and selection operator(LASSO)method.A risk score model based on the prognostic pseudogenes was also *** pseudogene-mRNA regulatory networks were established using correlation analysis,and their potent roles in the ovarian cancer progression were uncovered by functional enrichment ***,ssGSEA and ESTIMATE algorithms was used to evaluate the levels of immune cell infiltrations in cancer tissues and explore their relationship with risk signature.A prediction model of 10-pseudogenes including RPL10P6,AC026688.1,FAR2P4,AL391840.2,AC068647.2,FAM35BP,GBP1P1,ARL4AP5,RPS3AP2,and AMD1P1 was *** 10-pseudogenes signature was demonstrated to be an independent prognostic factor in patient with ovarian cancer in the random set(hazard ratio[HR]=2.512,95%confidence interval[CI]=2.03–3.11,P0.001)and total set(HR=1.71,95%CI=1.472–1.988,P0.001).When models integrating with age,grade,stage,and risk signature,the Area Under Curve(AUC)of the 1-year,3-year,5-year and 10-year Receiver Operating Characteristic curve in the random set and total set were 0.854,0.824,0.855,0.805 and 0.679,0.697,0.739,0.790,*** results of functional enrichment analysis indicated that the underlying mechanisms by which these pseudogenes influence cancer prognosis may involve the immune-related biological processes and signaling *** analysis showed that risk signature was significantly correlated with immune cell infiltration and immune *** identified a novel 10-pseudogenes signature to predict the survival of patients with ovarian cancer,and that