Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UNIPROT:P41002 (CCNF)
 32 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The cell cycle is regulated via important biological mechanisms. Controlled expression of cell cycle regulatory proteins is crucial to maintain cell cycle progression. However, unbalanced protein expression leads to many diseases, such as cancer. Previous research suggests that SCYL1-BP1 function might be related to cell cycle progression and SCYL1-BP1 dysfunction to diseases through undefined mechanisms. In this research, an unbiased yeast two-hybrid screen was used to find protein(s) with potential biological relevance to SCYL1-BP1 function, and a novel interaction was recognized between SCYL1-BP1 and Cyclin F. This interaction was chosen as a paradigm to study SCYL1-BP1 function in cell cycle progression and its possible role in tumorigenesis. We found that SCYL1-BP1 binds to Cyclin F both in vivo and in vitro. SCYL1-BP1 overexpression promoted expression of the CCNF gene and simultaneously delayed Cyclin F protein degradation. SCYL1-BP1 knockdown reduced the expression of endogenous Cyclin F. It was also demonstrated in functional assays that SCYL1-BP1 overexpression induces G2/M arrest in cultured liver cells. Furthermore, SCYL1-BP1 sustained RRM2 protein expression by reducing its ubiquitination. Thus, we propose that SCYL1- BP1 affects the cell cycle through increasing steady state levels of Cyclin F and RRM2 proteins, thus constituting a dual regulatory circuit. This study provides a possible mechanism for SCYL1-BP1-mediated cell cycle regulation and related diseases.
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PMID:SCYL1-BP1 affects cell cycle arrest in human hepatocellular carcinoma cells via Cyclin F and RRM2. 2598 Aug 18

Kidney renal cell carcinoma (KIRC), which is the most common subtype of kidney cancer, has a poor prognosis and a high mortality rate. In this study, a multi-omics analysis is performed to build a multi-gene prognosis signature for KIRC. A combination of a DNA methylation analysis and a gene expression data analysis revealed 863 methylated differentially expressed genes (MDEGs). Seven MDEGs (BID, CCNF, DLX4, FAM72D, PYCR1, RUNX1, and TRIP13) were further screened using LASSO Cox regression and integrated into a prognostic risk score model. Then, KIRC patients were divided into high- and low-risk groups. A univariate cox regression analysis revealed a significant association between the high-risk group and a poor prognosis. The time-dependent receiver operating characteristic (ROC) curve shows that the risk group performs well in predicting overall survival. Furthermore, the risk group is contained in the best multivariate model that was obtained by a multivariate stepwise analysis, which further confirms that the risk group can be used as a potential prognostic biomarker. In addition, a nomogram was established for the best multivariate model and shown to perform well in predicting the survival of KIRC patients. In summary, a seven-MDEG signature is a powerful prognosis factor for KIRC patients and may provide useful suggestions for their personalized therapy.
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PMID:A Gene Signature of Survival Prediction for Kidney Renal Cell Carcinoma by Multi-Omic Data Analysis. 3173 30