Open AccessIdentification of potential prognostic TF‐associated lncRNAs for predicting survival in ovarian cancer
Author(s) -
Guo Qiuyan,
He Yanan,
Sun Liyuan,
Kong Congcong,
Cheng Yan,
Wang Peng,
Zhang Guangmei
Publication year - 2019
Publication title -
journal of cellular and molecular medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.44
H-Index - 130
eISSN - 1582-4934
pISSN - 1582-1838
DOI - 10.1111/jcmm.14084
Subject(s) - ovarian cancer , computational biology , long non coding rna , rna , oncology , biology , survival analysis , bioinformatics , gene , medicine , cancer , genetics
The pathophysiology of ovarian cancer ( OV ) is complex and depends on multiple biological processes and pathways. Therefore, there is an urgent need to identify reliable prognostic biomarkers for predicting clinical outcomes and helping personalize treatment of OV . A long non‐coding RNA (lnc RNA )‐based risk score model was constructed to infer the prognostic efficacy of transcription factors ( TF s) based on the OV dataset from The Cancer Genome Atlas. The risk score model was further validated in other independent cohorts from Gene Expression Omnibus. Time‐dependent receiver operating characteristic curves were used to evaluate the survival prediction performance in comparison with other clinical and molecular variables. Our results revealed that the top‐ranked TF ‐associating lnc RNA s were significantly associated with overall survival, progression‐free survival and disease‐free survival. Stratification analysis according to clinical variables indicated the prognostic independence of POLR 2A‐associating lnc RNA s. In comparison, the signature of POLR 2A‐associating lnc RNA s was more sensitive and specific than existing clinical and molecular signatures. Functional and experimental analysis suggested that POLR 2A‐associating lnc RNA s may be involved in known biological processes and pathways of OV . Our findings revealed that the lnc RNA ‐based risk score model can provide helpful information on OV prognosis stratification and discovery of therapeutic biomarkers.