Open Access

ARTICLE

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

YONGHUI YU^1,#, SONGHUI XU^2,#, ERYONG ZHAO^3,#, YONGSHUN DONG¹, JINBIN CHEN¹, BOQI RAO¹, JIE ZENG⁴, LEI YANG¹, JIACHUN LU¹, FUMAN QIU^1,4,*

1 State Key Laboratory of Respiratory Disease, Institute for Chemical Carcinogenesis, Collaborative Innovation Center for Environmental Toxicity, Guangzhou Medical University, Guangzhou, 510182, China
2 Research Center of Medical Sciences, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
3 Department of Obstetrics and Gynecology, Guangzhou Women and Children’s Medical Center, Guangzhou, 510000, China
4 Department of Obstetrics and Gynecology, The Third Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510150, China

* Corresponding Author:FUMAN QIU. Email:
# These authors contributed equally to this work

(This article belongs to the Special Issue: Noncoding RNAs & Associated Human Diseases)

BIOCELL 2022, 46(4), 999-1011. https://doi.org/10.32604/biocell.2022.017004

Received 19 April 2021; Accepted 07 June 2021; Issue published 15 December 2021

Abstract

The outcomes of ovarian cancer are complicated and usually unfavorable due to their diagnoses at a late stage. Identifying the efficient prognostic biomarkers to improve the survival of ovarian cancer is urgently warranted. The 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 constructed. The pseudogene-mRNA regulatory networks were established using correlation analysis, and their potent roles in the ovarian cancer progression were uncovered by functional enrichment analysis. Lastly, 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 established. The 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, P < 0.001) and total set (HR = 1.71, 95% CI = 1.472–1.988, P < 0.001). 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, respectively. The 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 pathways. Correlation analysis showed that risk signature was significantly correlated with immune cell infiltration and immune score. We identified a novel 10-pseudogenes signature to predict the survival of patients with ovarian cancer, and that may serve as novel possible prognostic biomarkers and therapeutic targets for ovarian cancer.

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