Magic™ Anti-RPB1 (C-terminal)(phospho S5) polyclonal antibody

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.

Background: Relative quantification is a commonly used method for assessing gene expression, however its accuracy and reliability is dependent upon the choice of an optimal endogenous control gene, and such choice cannot be made a priori. There is limited information available on suitable reference genes to be used for studies involving human epicardial adipose tissue. The objective of the current study was to evaluate and identify optimal reference genes for use in the relative quantification of gene expression in human epicardial fat depots of lean, overweight and obese subjects. Methodology/Principal Findings: Some of the commonly used reference genes including 18S, ACTB, RPL27, HPRT, CYCA, GAPDH, RPLPO, POLR2A and B2M were quantified using real-time PCR analysis. The expression stability of these genes was evaluated using Genorm, Normfinder and Bestkeeper algorithms. In addition, the effect of sample size on the validation process was studied by randomly categorizing subjects in two cohorts of n = 2 and n = 33. Conclusions/Significance: CYCA, GAPDH and RPL27 were identified as the most stable genes common to all three algorithms and both sample sizes. Their use as reference gene pairs might contribute to the enhanced robustness of relative quantification in the studies involving the human epicardial adipose tissue.