Circulating miR-618 Has Prognostic Significance in Patients with Metastatic Colon Cancer

The present study evaluated the prognostic role of circulating miRNA-618 in patients with metastatic colon cancer (mCC) and whether miR-618 gene rs2682818 single nucleotide polymorphisms (SNP) are associated with colon cancer susceptibility and expression levels of mature miR-618. In total, 104 patients with mCC before starting the chemotherapy were investigated. The expression status of circulating miR-618 in mCC was evaluated by quantitative PCR. TaqMan PCR assay was used for rs2682818 SNP genotyping. miR-618 was overexpressed in serum of mCC patients. Patients with high and intermediate expression of miR-618 had a significantly longer mean overall survival (OS) of 21 months than patients with low expression—16 months. In addition, multivariate Cox regression analysis confirmed the association between high/intermediate levels of miRNA-618 and longer OS, HR = 0.51, 95% CI: 0.30–0.86, p = 0.012. miR-618 rs2682818 SNP significantly decreased the risk of colon cancer susceptibility in both heterozygous codominant (AC vs. CC, OR = 0.39, 95% CI: 0.17–0.88, p = 0.024) and overdominant (AC vs. CC + AA, OR = 0.37, 95% CI: 0.16–0.85, p = 0.018) genetic models. Our data suggest that circulating miRNA-618 could be useful as a prognostic biomarker in mCC. Patients harboring AC rs2682818 genotype have a decreased risk for colon cancer in comparison with patients with CC and AA genotypes.     

Intestinal helminths and protozoan infections in patients with colorectal cancer: prevalence and possible association with cancer pathogenesis

Parasitology Research - The purpose of the present study was to determine the prevalence of intestinal helminths and protozoa in colorectal cancer (CRC) patients and to evaluate the possible...     

Dermoscopic diagnosis of melanoma in a 4D space constructed by active contour extracted features

Dermoscopy, also known as epiluminescence microscopy, is a major imaging technique used in the assessment of melanoma and other diseases of skin. In this study we propose a computer aided method and tools for fast and automated diagnosis of malignant skin lesions using non-linear classifiers. The method consists of three main stages: (1) skin lesion features extraction from images; (2) features measurement and digitization; and (3) skin lesion binary diagnosis (classification), using the extracted features. A shrinking active contour (S-ACES) extracts color regions boundaries, the number of colors, and lesion's boundary, which is used to calculate the abrupt boundary. Quantification methods for measurements of asymmetry and abrupt endings in skin lesions are elaborated to approach the second stage of the method. The total dermoscopy score (TDS) formula of the ABCD rule is modeled as linear support vector machines (SVM). Further a polynomial SVM classifier is developed. To validate the proposed framework a dataset of 64 lesion images were selected from a collection with a ground truth. The lesions were classified as benign or malignant by the TDS based model and the SVM polynomial classifier. Comparing the results, we showed that the latter model has a better f-measure then the TDS-based model (linear classifier) in the classification of skin lesions into two groups, malignant and benign.