Circulating micro-RNAs as potential blood-based markers for early stage breast cancer detection

Abstract

Introduction: MicroRNAs (miRNAs, miRs) are a class of small, non-coding RNA molecules with relevance as regulators of gene expression thereby affecting crucial processes in cancer development. MiRNAs offer great potential as biomarkers for cancer detection due to their remarkable stability in blood and their characteristic expression in many different diseases. We investigated whether microarray-based miRNA profiling on whole blood could discriminate between early stage breast cancer patients and healthy controls.

Methods: We performed microarray-based miRNA profiling on whole blood of 48 early stage breast cancer patients at diagnosis along with 57 healthy individuals as controls. This was followed by a real-time semi-quantitative Polymerase Chain Reaction (RT-qPCR) validation in a separate cohort of 24 early stage breast cancer patients from a breast cancer screening unit and 24 age matched controls using two differentially expressed miRNAs (miR-202, miR-718).

Results: Using the significance level of p<0.05, we found that 59 miRNAs were differentially expressed in whole blood of early stage breast cancer patients compared to healthy controls. 13 significantly up-regulated miRNAs and 46 significantly down-regulated miRNAs in our microarray panel of 1100 miRNAs and miRNA star sequences could be detected. A set of 240 miRNAs that was evaluated by radial basis function kernel support vector machines and 10-fold cross validation yielded a specificity of 78.8%, and a sensitivity of 92.5%, as well as an accuracy of 85.6%. Two miRNAs were validated by RT-qPCR in an independent cohort. The relative fold changes of the RT-qPCR validation were in line with the microarray data for both miRNAs, and statistically significant differences in miRNA-expression were found for miR-202.

Conclusions: MiRNA profiling in whole blood has potential as a novel method for early stage breast cancer detection, but there are still challenges that need to be addressed to establish these new biomarkers in clinical use.

Figure 1. Representative example of a classification result using trained support vector machines.

The graph shows the logarithm of the quotient of the probability to be BC sample and the probability to be a control sample (log odds) of all samples analyzed with miRNA-microarrays. 1… Controls; 2…BC patient.

Figure 2. RT-qPCR validation of miR-202.

Significant different expression of circulating miR-202 in whole blood of BC patients versus Controls (up-regulation of miR-202). Data derived from RT-qPCR and presented as delta-Ct values, with higher values standing for lower miRNA-expression.

Figure 3. Area under the receiver operating characteristic curve (AUC) for miR-202 based on the RT-qPCR data.