ArticleOncology letters2019
Article in Oncology letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 15 citations in OpenAlex.
- The emerging roles and potential applications of circular RNAs in ovarian cancer: a comprehensive review.Journal of cancer research and clinical oncology · 2023Review
- KAZN as a diagnostic marker in ovarian cancer: a comprehensive analysis based on microarray, mRNA-sequencing, and methylation data.BMC cancer · 2022Article
- CircSETDB1 knockdown inhibits the malignant progression of serous ovarian cancer through miR-129-3p-dependent regulation of MAP3K3.Journal of ovarian research · 2021Article
- CircLNPEP promotes the progression of ovarian cancer through regulating miR-876-3p/ WNT5A axis.Cell cycle (Georgetown, Tex.) · 2021Article
- Downregulation of hsa_circ_0026123 suppresses ovarian cancer cell metastasis and proliferation through the miR‑124‑3p/EZH2 signaling pathway.International journal of molecular medicine · 2021Article
- circRNA-UBAP2 promotes the proliferation and inhibits apoptosis of ovarian cancer though miR-382-5p/PRPF8 axis.Journal of ovarian research · 2020Article
- Review
- Differentially Expressed mRNAs and Their Long Noncoding RNA Regulatory Network withBioMed research international · 2020Article
- p28GANK overexpression is associated with chemotherapy resistance and poor prognosis in ovarian cancer.Oncology letters · 2020Article
- Circular RNAs in cancer: new insights into functions and implications in ovarian cancer.Journal of ovarian research · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ovarian cancer (OC) is the most common and lethal gynecologic malignancy. The pathophysiology of OC tumor development is complex and involves numerous biological pathways. Previous studies suggest that circular (circ)RNAs serve important roles in OC tumor pathology. In the present study, a re-annotation strategy was performed to evaluate the expression level of circRNAs based on a microarray dataset obtained from the Gene Expression Omnibus database. Univariate and multivariate Cox regression analyses were performed to evaluate the association between survival and expression of circRNAs in each OC cohort. An expression-based risk score model was constructed to extrapolate the prognostic efficacy of this signature. In the GSE9891 dataset, the 278 OC patients were randomly divided into training and validating groups. A six-circRNA signature was significantly associated with overall survival in the training and validating datasets. The risk score model was further validated in GSE63885 and GSE26193 datasets. The six-circRNA signature was also significantly associated with patient progression-free survival and disease-free survival. Further investigation revealed that the signature had higher area under the curve values than the existing clinical and other molecular signatures in predicting survival. In conclusion, the present study revealed that the six-circRNA signature may serve as a potential prognostic biomarker of OC.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.