A prognostic risk model for ovarian cancer based on gene expression profiles from gene expression omnibus database
Author:
Publisher
Springer Science and Business Media LLC
Subject
Genetics,Molecular Biology,General Medicine,Ecology, Evolution, Behavior and Systematics,Biochemistry
Link
https://link.springer.com/content/pdf/10.1007/s10528-022-10232-5.pdf
Reference42 articles.
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2. Bai R et al (2019) The NF-kappaB-modulated miR-19a-3p enhances malignancy of human ovarian cancer cells through inhibition of IGFBP-3 expression. Mol Carcinog 58:2254–2265. https://doi.org/10.1002/mc.23113
3. Bali A et al (2004) Cyclin D1, p53, and p21Waf1/Cip1 expression is predictive of poor clinical outcome in serous epithelial ovarian cancer. Clin Cancer Res 10:5168–5177. https://doi.org/10.1158/1078-0432.CCR-03-0751
4. Baumann S, Hennet T (2016) Collagen accumulation in osteosarcoma cells lacking GLT25D1 collagen galactosyltransferase. J Biol Chem 291:18514–18524. https://doi.org/10.1074/jbc.M116.723379
5. Ceccaroni M et al (2004) p53 expression, DNA ploidy and mitotic index as prognostic factors in patients with epithelial ovarian carcinoma. Tumori 90:600–606
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1. Identification of a G-protein coupled receptor-related gene signature through bioinformatics analysis to construct a risk model for ovarian cancer prognosis;Computers in Biology and Medicine;2024-08
2. Screening of Prognostic Molecular Markers and Establishment of Prognostic Model for G-protein Coupled Receptor-Related Genes in Epithelial Ovarian Serous Cancer Based on Machine Learning Method;2023-10-12
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