Selection of endogenous control and identification of significant microRNA deregulations in cervical cancer

IntroductionCervical cancer causes approximately 350,000 deaths each year. The availability of sensitive and specific diagnostic tests to detect cervical cancer in its early stages is essential to improve survival rates.MethodsIn this study, we compared two strategies for selecting endogenous controls: miRNA profiling by small-RNA sequencing and a commercially available microfluidic card with 30 recommended endogenous controls preloaded by the manufacturer. We used the RefFinder algorithm and coefficient of variation to select endogenous controls. We selected the combination of miR-181a-5p and miR-423-3p as the most optimal normalizer. In the second part of this study, we determined the differential expression (between tumor/non-tumor groups) of microRNA in cervical cancer FFPE tissue samples. We determined the comprehensive miRNA expression profile using small-RNA sequencing technology and verified the results by real-time PCR. We determined the relative expression of selected miRNAs using the 2-ΔΔCt method.ResultsWe detected statistically significant upregulation of miR-320a-3p, miR-7704, and downregulation of miR-26a-5p in the tumor group compared to the control group. The combination of these miRNAs may have the potential to be utilized as a diagnostic panel for cervical cancer. Using ROC curve analysis, the proposed panel showed 93.33% specificity and 96.97% sensitivity with AUC = 0.985.ConclusionsWe proposed a combination of miR-181a-5p and miR-423-3p as optimal endogenous control and detected potentially significant miRNAs (miR-320a-3p, miR-7704, miR-26a-5p). After further validation of our results, these miRNAs could be used in a diagnostic panel for cervical cancer

RAS mutation prevalence among patients with metastatic colorectal cancer: A meta-analysis of real-world data

Aim: A confirmed wild-type RAS tumor status is commonly required for prescribing anti-EGFR treatment for metastatic colorectal cancer. This noninterventional, observational research project estimated RAS mutation prevalence from real-world sources. Materials & methods: Aggregate RAS mutation data were collected from 12 sources in three regions. Each source was analyzed separately; pooled prevalence estimates were then derived from meta-analyses. Results: The pooled RAS mutation prevalence from 4431 tumor samples tested for RAS mutation status was estimated to be 43.6% (95% CI: 38.8-48.5%); ranging from 33.7% (95% CI: 28.4-39.3%) to 54.1% (95% CI: 51.7-56.5%) between sources. Conclusion: The RAS mutation prevalence estimates varied among sources. The reasons for this are not clear and highlight the need for further research. © 2017 Future Medicine Ltd