BackgroundIrisin is a recently discovered myokine, involved in the browning of white adipose tissue. To date, its function has been mainly associated with energy homeostasis and metabolism, and it has been proposed as a promising therapeutic target for obesity and metabolic diseases. This is the first study investigating the role of irisin in human breast cancer.MethodsParticipants included one hundred and one (101) female patients with invasive ductal breast cancer and fifty one (51) healthy women. Serum levels of irisin, leptin, adiponectin and resistin were quantified in duplicates by ELISA. Serum levels of CEA, CA 15–3 and Her-2/neu were measured on an immunology analyzer. The association between irisin and breast cancer was examined by logistic regression analysis. The feasibility of serum irisin in discriminating breast cancer patients was assessed by ROC curve analysis. Potential correlations with demographic, anthropometric and clinical parameters, with markers of adiposity and with breast tumor characteristics were also investigated.ResultsSerum levels of irisin were significantly lower in breast cancer patients compared to controls (2.47 ± 0.57 and 3.24 ± 0.66 μg/ml, respectively, p < 0.001). A significant independent association between irisin and breast cancer was observed by univariate and multivariate analysis (p < 0.001). It was estimated that a 1 unit increase in irisin levels leads to a reduction in the probability of breast cancer by almost 90 %. Irisin could effectively discriminate breast cancer patients at a cut-off point of 3.21 μg/ml, with 62.7 % sensitivity and 91.1 % specificity. A positive association with tumor stage and marginal associations with tumor size and lymph node metastasis were observed (p < 0.05, p < 0.01, p < 0.01, respectively).ConclusionsOur novel findings implicate irisin in breast cancer and suggest its potential application as a new diagnostic indicator of the presence of disease.

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The marginal statistics for the diffused ultrasound speckle echo has been postulated as exhibiting circularly symmetric Gaussian behavior similar to the laser speckle for monochromatic illumination under the assumption of a large number of unresolvable scatterers per resolution cell. This is known in the literature as the Rayleigh scattering condition. This paper presents a formal statistical test, the Kolmogorov-Smirnov nonparametric goodness of fit statistical test, to test the hypothesis that the unresolvable part (diffuse part) of the backscatter echo follows a Rayleigh scattering condition, and obtain numerical values for the scatterer concentration required for the Rayleigh condition to be valid. In addition, it presents a formal statistical test, the Kolmogorov-Smirnov nonparametric homogeneity statistical test, to compare two regions of interest with different scattering concentrations without prior knowledge of the nature of the scattering conditions (Rayleigh or non-Rayleigh scattering). Unlike all previous parametric testing methods that treat the A-scan or B-scan echo as a random sample, the authors' method presents formal tests based on the colored nature of the diffuse backscattered echo which is a more realistic model of the diffuse scattering component. The tests are demonstrated on simulations of RF scans with different scatterer concentrations per resolution cell as well as on phantom data which mimic tissue.