Dataset Information

Identification of potential prognostic TF-associated lncRNAs for predicting survival in ovarian cancer.

ABSTRACT: 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 (lncRNA)-based risk score model was constructed to infer the prognostic efficacy of transcription factors (TFs) 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 lncRNAs were significantly associated with overall survival, progression-free survival and disease-free survival. Stratification analysis according to clinical variables indicated the prognostic independence of POLR2A-associating lncRNAs. In comparison, the signature of POLR2A-associating lncRNAs was more sensitive and specific than existing clinical and molecular signatures. Functional and experimental analysis suggested that POLR2A-associating lncRNAs may be involved in known biological processes and pathways of OV. Our findings revealed that the lncRNA-based risk score model can provide helpful information on OV prognosis stratification and discovery of therapeutic biomarkers.

SUBMITTER: Guo Q

PROVIDER: S-EPMC6378234 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Identification of potential prognostic TF-associated lncRNAs for predicting survival in ovarian cancer.

Guo Qiuyan Q He Yanan Y Sun Liyuan L Kong Congcong C Cheng Yan Y Wang Peng P Zhang Guangmei G

Journal of cellular and molecular medicine 20181213 3

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 (lncRNA)-based risk score model was constructed to infer the prognostic efficacy of transcription factors (TFs) based on the OV dataset from The Cancer Genome Atlas. The risk score model was further validated in o ...[more]

PMID: 30549251

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Dataset Information

Identification of potential prognostic TF-associated lncRNAs for predicting survival in ovarian cancer.

Publications

Identification of potential prognostic TF-associated lncRNAs for predicting survival in ovarian cancer.

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