BackgroundGastric cancer (GC) is one of the most common malignancy and primary cause of death in Chinese cancer patients. Recurrence is a major factor leading to treatment failure and low level of 5-year survival rate in GC patients following surgical resection. Therefore, identification of biomarkers with potential in predicting recurrence risk is the key problem of the prognosis in GC patients.Patients and MethodsA total of 74 GC patients were selected for systematic analysis, consisting of 31 patients with recurrence and 43 patients without recurrence. Firstly, miRNAs microarray and bioinformatics methods were used to characterize differential expressed miRNAs from primary tumor samples. Following, we used a ROC method to select signature with best sensitivity and specificity. Finally, we validated the signature in GC samples (frozen fresh and blood samples) using quantitative PCR.ResultsWe have identified 12 differential miRNAs including 7 up-regulated and 5 down-regulated miRNAs in recurrence group. Using ROC method, we further ascertained hsa-miR-335 as a signature to recognize recurrence and non-recurrence cases in the training samples. Moreover, we validated this signature using quantitative PCR method in 64 test samples with consistent result with training set. A high frequency recurrence and poor survival were observed in GC cases with high level of hsa-miR-335 (P<0.001). In addition, we evaluated that hsa-miR-335 were involved in regulating target genes in several oncogenic signal-pathways, such as p53, MAPK, TGF-β, Wnt, ERbB, mTOR, Toll-like receptor and focal adhesion.ConclusionOur results indicate that the hsa-miR-335 has the potential to recognize the recurrence risk and relate to the prognosis of GC patients.

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Abstract. Colon cancer is the third most common cancer and one of the leading causes of cancer-related death in the world. Therefore, identification of biomarkers with potential in recognizing the biological characteristics is a key problem for early diagnosis of colon cancer patients. In this study, we used a random forest approach to discover biomarkers based on a set of oligonucleotide microarray data of colon cancer. Realtime PCR was used to validate the related expression levels of biomarkers selected by our approach. Furthermore, ROC curves were used to analyze the sensitivity and specificity of each biomarker in both training and test sample sets. Finally, we analyzed the clinical significance of each biomarker based on their differential expression. A single classifier consisting of 4 genes (IL8, WDR77, MYL9 and VIP) was selected by random forests with an average sensitivity and specificity of 83.75 and 76.15%. The differential expression levels of each biomarker was validated by real-time PCR in 48 test colon cancer samples compared to the matched normal tissues. Patients with high expression of IL8 and WDR77, and low expression of MYL9 and VIP had a significantly reduced median survival rate compared to colon cancer patients. The results indicate that our approach can be employed for biomarker identification based on microarray data. These 4 genes identified by our approach have the potential to act as clinical biomarkers for the early diagnosis of colon cancer. IntroductionColon cancer is the third most common cancer, and one of the leading causes of morbidity and mortality in the world (1). According to the United States' statistics released in 2010 the incidence rate of colon has decreased (2). Over the last decade, many studies have proposed various kinds of statistical methods to analyze gene expression patterns and identify new biomarkers for prognostic and/or predictive information in relation to human diseases (3,4). However, most of the early studies applied unsupervised approaches to data-mining and identification of differential gene expressed profiling of certain diseases, such as hierarchical clustering for class discovering, taking an unbiased approach to searching for subgroups in the data (5). Along with the statistical methods extensively penetrated into the field of biomedicine, many supervised clustering analysis and machine learning approaches were adopted to deal with gene expression profiling data and sieved feature genes which contained more information to classify different kinds of diseases or subclasses of the same disease.Various methods of statistics and machine learning, including clustering (6,7), Bayesian algorithms (8), and support vector machines (9), have been proposed to analyze microarray data generated through high-throughput experiments. Over the last few years, the technology of multiclassifier fusion developed substantially, and became very successful in improving the accuracy of certain classifiers. Random forests (RF) (10,11), a tree-based method of classificat...

The dermal papilla plays an important role in the regulation of hair follicle matrix cell proliferation and hair fiber production, at least in part through mesenchymal-epithelial interactions. In the present study, we have investigated the regulation of interleukin-1 (IL-1) production by protein kinase C in cultured human dermal papilla cells. Treatment of dermal papilla cells with 12-O-tetradecanoylphorbol-13-acetate (TPA) elicited the rapid and transient production of mature (17 kDa) cytosolic IL-1beta protein, but not IL-1alpha, with maximal levels achieved after 12 h. Rapid secretion of IL-1beta into the medium occurred subsequent to increased intracellular cytokine levels, after which medium IL-1beta protein levels were stable for 4 days. Northern blot analysis showed that TPA treatment elicited a transient induction of IL-1beta mRNA expression, maximal after 12 h, indicating that TPA regulates dermal papilla cell IL-1beta production at the transcriptional level. Pretreatment of dermal papilla cells with Ro 31-7549, a selective protein kinase C inhibitor, dose dependently and completely reversed phorbol-induced IL-1beta protein production. In addition, we demonstrated that IL-1beta is a highly potent inhibitor of the growth of human hair follicles in whole-organ culture, with an IC50 value of approximately 5 pg/ml. These findings, taken together with a previous report that follicular matrix cells express type I IL-1 receptors but dermal papilla cells do not, raise the possibility that dermal papilla cell-derived IL-1beta may act as a negative paracrine factor in the regulation of matrix cell proliferation.

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