Chronic lymphocytic leukemia (CLL) is known as the most common lymphoid malignancy in the Western world. MicroRNAs (miRNAs) are a class of small noncoding RNAs with pivotal roles in cellular and molecular processes related to different malignancies including CLL. Recently, some studies have shown that miR-192 plays a key role in CLL pathogenesis through increasing CDKN1A/p21 levels, suppression of Bcl-2 and enhancement of wild-type P53 and cell cycle arrest. Forty samples, including 20 patients with CLL, diagnosed in Omid hospital (Isfahan, Iran) and 20 healthy controls were sampled during a period of 4 months. Using real-time PCR method, expression of miR-192 was analyzed in peripheral blood mononuclear cells (PBMCs) of CLL patients in comparison with healthy subjects. In silico molecular signaling pathway enrichment analysis was also performed on validated and predicted targets (targetome) of miR-192 in DAVID database to explore possible role of miR-192 in some pathways. The expression of miR-192 was found to be significantly reduced (~2.5-folds) in CLL patients compared with healthy subjects (P=0.002). In silico molecular signaling pathway enrichment analysis detected cell indicated signaling pathway as one of the most statistically relevant pathway with miR-192 targetome. Our findings showed that miR-192 could be a biomarker for early diagnosis of CLL.

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In conclusion, PlncRNA-1 and TUG1 genes may play a critical role in GC progression and may serve as potential diagnostic biomarkers in GC patients.

The findings obtained in the present study support the induction of an indel mutation in the KLF1 gene leading to a null allele. As a result, the effect of KLF1 on the expression of BCL11A is decreased and its inhibitory effect on γ-globin gene expression is removed. Application of CRISPR technology to induce an indel in the KLF1 gene in adult erythroid progenitors may provide a method for activating fetal hemoglobin expression in individuals with β-thalassemia or sickle cell disease.

BackgroundOne of the best and most accurate methods for identifying disease-causing genes is monitoring gene expression values in different samples using microarray technology. One of the shortcomings of microarray data is that they provide a small quantity of samples with respect to the number of genes. This problem reduces the classification accuracy of the methods, so gene selection is essential to improve the predictive accuracy and to identify potential marker genes for a disease. Among numerous existing methods for gene selection, support vector machine-based recursive feature elimination (SVMRFE) has become one of the leading methods, but its performance can be reduced because of the small sample size, noisy data and the fact that the method does not remove redundant genes.MethodsWe propose a novel framework for gene selection which uses the advantageous features of conventional methods and addresses their weaknesses. In fact, we have combined the Fisher method and SVMRFE to utilize the advantages of a filtering method as well as an embedded method. Furthermore, we have added a redundancy reduction stage to address the weakness of the Fisher method and SVMRFE. In addition to gene expression values, the proposed method uses Gene Ontology which is a reliable source of information on genes. The use of Gene Ontology can compensate, in part, for the limitations of microarrays, such as having a small number of samples and erroneous measurement results.ResultsThe proposed method has been applied to colon, Diffuse Large B-Cell Lymphoma (DLBCL) and prostate cancer datasets. The empirical results show that our method has improved classification performance in terms of accuracy, sensitivity and specificity. In addition, the study of the molecular function of selected genes strengthened the hypothesis that these genes are involved in the process of cancer growth.ConclusionsThe proposed method addresses the weakness of conventional methods by adding a redundancy reduction stage and utilizing Gene Ontology information. It predicts marker genes for colon, DLBCL and prostate cancer with a high accuracy. The predictions made in this study can serve as a list of candidates for subsequent wet-lab verification and might help in the search for a cure for cancers.