Circulating microRNAs as biomarkers - True Blood?

MicroRNAs are post-transcriptional regulators that are involved in many physiological and pathophysiological conditions. A recent study compared the expression profiles of hundreds of blood-borne microRNAs across a variety of nonmalignant and malignant diseases to identify disease-specific expression patterns. The resulting microRNA expression data could be used to discriminate disease samples with a high level of accuracy, demonstrating the potential for using microRNA signatures for the blood-based diagnosis of disease.

The first connection between microRNAs (miRNAs) and the pathogenesis of cancer was described by Calin et al. in 2002 [1]. Since then, it has become clear that miRNA expression patterns reflect the identities of both normal and malignant cells. The majority of reported miRNA expression data have been generated from solid tissues, but differentially expressed microRNAs have also been identified in body fluids, such as serum and cerebrospinal fluid [2]. This finding has led researchers to explore the diagnostic and predictive relevance of miRNAs in whole blood, the most accessible organ of the human body.
In an article recently published in Nature Methods, Keller and colleagues [3] addressed this issue by investi gating the miRNA transcriptome (which they call the 'miRNome') of blood samples from patients with a variety of diseases, including lung cancer and myocardial infarc tion. A total of 454 blood samples, taken from individuals affected by one of 14 selected diseases (Box 1) or from healthy controls, were profiled using miRNA microarray technology. This led to the identification of multiple miRNAs that are deregulated in the disease state, that is, their expression was either up or downregulated in patient samples compared with matched controls. The dysregulated miRNA profiles could be used to discri mi nate between healthy controls and patient samples with an average accuracy of 88.5%, or 72.5% using only two miRNAs. Considering the lack of diagnostic markers for many diseases, the work of Keller et al. [3] highlights the potential for the future integration of bloodbased miRNAs into routine diagnostics.

Serum, plasma or whole blood?
Despite the high discriminative potential, the predictive value of the total miRNome amounted on average to only 67.45%. Confounding variables for this experimental approach include existing comorbidities and varying number of cells in the blood (blood counts), which might distort diseasespecific miRNA profiles. The hetero geneity of blood, which is a mixture of platelets, erythrocytes, myeloid cells and lymphoid cells, might be another source of confounding factors.
Until now, most published studies focused on the detection of miRNAs from serum. However, the study by Keller and colleagues [3] demonstrate that miRNomes of blood cells can reflect the presence of diseases that are not necessarily bloodborne. It is not fully understood how the various types of blood cell change their miRNome in response to disease; neither is it understood which cells in the hematopoietic system alter to reflect individual disease. Could it be that the miRNome of monocytes is altered following myocardial infarction, a necrotic condition, whereas immune diseases induce changes in the miRNome of B lymphocytes? To answer this question, a direct comparison between the miRNomes of serum, whole blood samples and hematopoietic sub populations, such as lymphocytes, from different diseases would be helpful. The use of serum might decrease the above mentioned confounding variables, possibly allow ing the detection of a more concentrated diseaserelated miRNome by decreasing blood cellrelated background expression of miRNAs through comorbidities, for example. In addition, most published works focused on the comparison between serumderived miRNomes of healthy controls with one particular disease. However, a comparison between whole blood and serum across multiple malignant and nonmalignant diseases is still

Abstract
MicroRNAs are post-transcriptional regulators that are involved in many physiological and pathophysiological conditions. A recent study compared the expression profiles of hundreds of blood-borne microRNAs across a variety of nonmalignant and malignant diseases to identify disease-specific expression patterns. The resulting microRNA expression data could be used to discriminate disease samples with a high level of accuracy, demonstrating the potential for using microRNA signatures for the blood-based diagnosis of disease.
missing, leaving unanswered the question which approach has more of a predictive miRNome.

miRNAs: new tools for the pathologist?
Many studies have demonstrated that miRNA expression patterns can classify cancers and form cancerspecific networks describing the developmental lineage and differentiation state of tumors [4,5]. However, is this enough to justify the integration of miRNAs into routine diagnostics? The answer depends on the type of cancer. A major problem lies in the varying number of miRNAs necessary to distinguish and classify specific cancer entities. For example, the number of miRNAs that are predictors for different classes of acute myeloid leukemias (AML) with genetic abnormalities varies between 7 and 48 using a realtime PCR approach [6], whereas a signature involving only 4 miRNAs (miR128a, miR128b, miR223 and let7b) accurately discriminates between AMLs and acute lymphoblastic leukemias (ALLs) [7]. In addition, expression levels of a single miRNA, miR181a, constitute a prognostic factor for overall survival in normal karyotype leukemia. If cancer is considered to be a developmental process similar to hematopoiesis [8,9], these findings might imply that miRNA expression alters hierarchically to reflect different stages within cancer development. Multiple miRNA predictors have been developed to classify differentiation stages of cancers; a useful tool, especially for cancers of unknown primary origin [10]. Yet it is still not clear whether these findings using tissue samples are mirrored by serumbased miRNA markers.

Challenges for miRNA-based diagnostics
The study of Keller et al. [3] strengthens the concept of bloodborne miRNAs as biomarkers for disease.
Tech nical limitations include a lack of existing standards that would allow crosscomparisons between laboratories and patient samples from various locations. In addition, diagnostic tests for miRNAs need to be simplified and streamlined for rapid readout. This would allow a better integration into established pathological scoring systems that would eventually benefit patients. To this end, more diseases have to be screened for bloodborne miRNA expression profiles and correlated with clinical features. For now, miRNAs are only a fragment of the truth.

Competing interests
The authors declare that they have no competing interests.