MiR-34a and miR-206 act as novel prognostic and therapy biomarkers in cervical cancer

Abstract Background Recent evidence indicated that the aberrant expression of microRNA plays a crucial role in the development of cervical cancer. The overall shorter survival was strongly related to the abnormal expression of microRNA-34a (miR-34a) and microRNA-206 (miR-206), which target B cell lymphoma-2(Bcl2) and c-Met. Hepatocyte growth factor (HGF)/c-Met pathway is related to the occurrence, development and prognosis of cervical cancer, and c-Met is significantly overexpressed in cervical squamous cell carcinoma. Bcl2 is also considered to be a promising target for developing novel anticancer treatments. Methods In this study, we detect the expression of miR-34a and miR-206 in the cervical cancer tissue through quantificational real-time polymerase chain reaction (qRT-PCR) assay, and the expression of Bcl2 and c-Met from cervical cancer tissue were detected by immunohistochemistry. Results The expression of miR-34a and miR-206 were down-regulated in the cervical cancer tissue through qRT-PCR assay. As target genes of miR-34a and miR-206, Bcl2 and c-Met were up-regulated in cervical cancer tissues through qRT-PCR assay and immunohistochemistry. Kaplan–Meier and log-rank analysis revealed that down-regulated expression of miR-34a and miR-206 were strongly related to shorter overall survival. Multivariate Cox proportional hazards model for all variables that were statistically significant in the univariate analysis demonstrated that miR-34a (P = 0.038) and miR-206 (P = 0.008) might be independent prognostic factors for overall survival of patients suffering from cervical cancer. Conclusions The up-regulation of Bcl2 and c-Met promotes the cervical cancer’s progress, and the expression of miR-34a and miR-206 significantly correlated with the progression and prognosis in cervical cancer. All of these suggested that miR-34a and miR-206 might be the novel prognostic and therapy tools in cervical cancer

Circulating Cell Free DNA as the Diagnostic Marker for Ovarian Cancer: A Systematic Review and Meta-Analysis

<div><p>Background</p><p>Quantitative analyses of circulating cell-free DNA (cfDNA) are potential methods for the detection of ovarian cancer. Many studies have evaluated these approaches, but the results were too inconsistent to be conclusive. This study is the first to systematically evaluate the accuracy of circulating cfDNA for the diagnosis of ovarian cancer by conducting meta-analysis.</p><p>Methods</p><p>We searched PubMed, Embase, Cochrane Library and the Chinese National Knowledge Infrastructure (CNKI) databases systematically for relevant literatures up to December 10, 2015. All analyses were conducted using Meta-DiSc1.4 and Stata 12.0 software. Sensitivity, specificity and other measures of accuracy of circulating cfDNA for the diagnosis of ovarian cancer were pooled. Meta-regression was performed to identify the sources of heterogeneity.</p><p>Results</p><p>This meta-analysis included a total of 9 studies, including 462 ovarian cancer patients and 407 controls. The summary estimates for quantitative analysis of circulating cfDNA in ovarian cancer screen were as follows: sensitivity, 0.70 (95% confidence interval (CI), 0.65–0.74); specificity, 0.90 (95% CI, 0.87–0.93); positive likelihood ratio, 6.60 (95% CI, 3.90–11.17); negative likelihood ratio, 0.34 (95% CI, 0.25–0.47); diagnostic odds ratio, 26.05 (95% CI, 14.67–46.26); and area under the curve, 0.89 (95% CI, 0.83–0.95), respectively. There was no statistical significance for the evaluation of publication bias.</p><p>Conclusions</p><p>Current evidence suggests that quantitative analysis of cfDNA has unsatisfactory sensitivity but acceptable specificity for the diagnosis of ovarian cancer. Further large-scale prospective studies are required to validate the potential applicability of using circulating cfDNA alone or in combination with conventional markers as diagnostic biomarker for ovarian cancer and explore potential factors that may influence the accuracy of ovarian cancer diagnosis.</p></div

The SROC curve for quantitative analysis of circulating cell free DNA in the diagnosis of ovarian cancer.

<p>The SROC curve for quantitative analysis of circulating cell free DNA in the diagnosis of ovarian cancer.</p

Quality assessment of the studies selected for the meta-analysis (QUADAS-2).

<p>Quality assessment of the studies selected for the meta-analysis (QUADAS-2).</p

Forest plot of estimated DOR for quantitative analysis of circulating cell free DNA in the diagnosis of ovarian cancer.

<p>Forest plot of estimated DOR for quantitative analysis of circulating cell free DNA in the diagnosis of ovarian cancer.</p

Summary of Studies Included in the Meta-Analysis.

<p>Summary of Studies Included in the Meta-Analysis.</p

The Deeks’ funnel plot for the assessment of potential publication bias of the included studies.

<p>The Deeks’ funnel plot for the assessment of potential publication bias of the included studies.</p

Forest plot of estimated PLR for quantitative analysis of circulating cell free DNA in the diagnosis of ovarian cancer.

<p>Forest plot of estimated PLR for quantitative analysis of circulating cell free DNA in the diagnosis of ovarian cancer.</p

Results of the subgroup analyses performed to identify potential sources of heterogeneity.

<p>Results of the subgroup analyses performed to identify potential sources of heterogeneity.</p

Forest plot of estimated specificity for quantitative analysis of circulating cell free DNA in the diagnosis of ovarian cancer.

<p>CI = confidence interval.</p