No abstract

Pancreatic cancer is currently the most lethal tumor entity and case numbers are rising. It will soon be the second most frequent cause of cancer-related death in the Western world. Mortality is close to incidence and patient survival after diagnosis stands at about five months. Blood-based diagnostics could be one crucial factor for improving this dismal situation and is at a stage that could make this possible. Here, we are reviewing the current state of affairs with its problems and promises, looking at various molecule types. Reported results are evaluated in the overall context. Also, we are proposing steps toward clinical utility that should advance the development toward clinical application by improving biomarker quality but also by defining distinct clinical objectives and the respective diagnostic accuracies required to achieve them. Many of the discussed points and conclusions are highly relevant to other solid tumors, too.

No abstract

Atopic dermatitis (AD) is a common skin condition that affects both children and adults. Adipokines have been shown to play a role in the pathogenesis of AD. In the current study, the association between adiponectin gene (ADIPOQ) polymorphisms and AD was investigated. In addition, changes in serum adiponectin levels in AD patients were examined. Restriction fragment length polymorphism-PCR technique was used to genotype ADIPOQ SNPs. The ELISA assay was used to measure serum Adiponectin levels. A total of 324 participants (162 AD and 162 healthy controls) were included in the study. The frequency of the GG genotype of rs3774261 was higher in the AD group (44.5%) than in the control group (32.7%, P<0.05). Regarding the rs2241766 SNP, the frequency of the GG genotype was higher in the AD group (10.5%) than in the control group (3.1%), while the frequency of the TT genotype was lower (P<0.001) in the AD group (35.8%) than the control group (57.4%). Moreover, the GG haplotype of rs3774261 and rs2241766 significantly increased the risk of AD by about 2-fold (P<0.05). Finally, serum adiponectin levels were lower in the AD group than in the control group (P<0.05). These results indicate an association of the rs2241766 and rs3774261 SNPs with the risk of developing AD among the population examined.

Purpose: Intraductal papillary mucinous neoplasm (IPMN) is a precursor of pancreatic ductal adenocarcinoma. Low-grade dysplasia has a relatively good prognosis, whereas high-grade dysplasia and IPMN invasive carcinoma require surgical intervention. However, diagnostic distinction is difficult. We aimed to identify biomarkers in peripheral blood for accurate discrimination. Experimental Design: Sera were obtained from 302 patients with IPMNs and 88 healthy donors. For protein biomarkers, serum samples were analyzed on microarrays made of 2,977 antibodies. A support vector machine (SVM) algorithm was applied to define classifiers, which were validated on a separate sample set. For microRNA biomarkers, a PCR-based screen was performed for discovery. Biomarker candidates confirmed by quantitative PCR were used to train SVM classifiers, followed by validation in a different sample set. Finally, a combined SVM classifier was established entirely independent of the earlier analyses, again using different samples for training and validation. Results: Panels of 26 proteins or seven microRNAs could distinguish high- and low-risk IPMN with an area under the curve (AUC) value of 95% and 94%, respectively. Upon combination, a panel of five proteins and three miRNAs yielded an AUC of 97%. These values were much better than those obtained in the same patient cohort by using the guideline criteria for discrimination. In addition, accurate discrimination was achieved between other patient subgroups. Conclusions: Protein and microRNA biomarkers in blood allow precise diagnosis and risk stratification of IPMN cases, which should improve patient management and thus the prognosis of IPMN patients.

<p>Diagnostic performance of serum protein panels.</p>

<p>Diagnostic performance obtained by four independent repetitions of creating a SVM classifier on the basis of the clinical parameters recorded for the 302 IPMN patients.</p>

<p>List of combined protein-miRNA classifiers.</p>