The need to analyze high-dimension biological data is driving the development of new data mining methods. Biclustering algorithms have been successfully applied to gene expression data to discover local patterns, in which a subset of genes exhibit similar expression levels over a subset of conditions. However, it is not clear which algorithms are best suited for this task. Many algorithms have been published in the past decade, most of which have been compared only to a small number of algorithms. Surveys and comparisons exist in the literature, but because of the large number and variety of biclustering algorithms, they are quickly outdated. In this article we partially address this problem of evaluating the strengths and weaknesses of existing biclustering methods. We used the BiBench package to compare 12 algorithms, many of which were recently published or have not been extensively studied. The algorithms were tested on a suite of synthetic data sets to measure their performance on data with varying conditions, such as different bicluster models, varying noise, varying numbers of biclusters and overlapping biclusters. The algorithms were also tested on eight large gene expression data sets obtained from the Gene Expression Omnibus. Gene Ontology enrichment analysis was performed on the resulting biclusters, and the best enrichment terms are reported. Our analyses show that the biclustering method and its parameters should be selected based on the desired model, whether that model allows overlapping biclusters, and its robustness to noise. In addition, we observe that the biclustering algorithms capable of finding more than one model are more successful at capturing biologically relevant clusters.
Programmed death-1 (PD-1) and programmed death-ligand 1 (PD-L1) are new targets in cancer immunotherapy in recent years. The aim of this study is to evaluate the PD-1/PD-L1 expressions in sarcomas and to determine association between PD-1/PD-L1 expressions and clinical/pathological properties in some sarcoma subtypes. Formalin-fixed, paraffin-embedded tissue samples from 65 cases with sarcomas were analyzed. Immunohistochemical staining was performed to detect the PD-1 and PD-L1 expressions in tumor tissue and microenvironment, separately. PD-1 expression in tumor tissue and microenvironment was detected in 11 (17 %) and 8 (12 %) cases, respectively. PD-L1 expression in tumor tissue and microenvironment was detected in 19 (29 %) and 20 cases (30 %), respectively. None of the 5 Ewing sarcomas involving bone showed PD-1/PD-L1 expression, while 2 of 3 cases with Ewing sarcomas involving soft tissue showed PD-1 and PD-L1 expression. Among 5 cases with Kaposi sarcoma, four showed PD-1 and/or PD-L1 expression in tumor or microenvironment. PD-1/PD-L1 expressions were detected 3 of 6 cases with pleomorphic sarcoma, 2 of 4 cases with peripheral nerve sheath tumors and 1 of 4 cases with synovial sarcoma. Interestingly, strongest PD-1/PD-L1 expressions in our study group were detected in 2 sarcoma cases with the history of giant cell tumor. PD-1 and PD-L1 expressions are up to 30 % of the cases with sarcomas. It may be rational to target programmed death pathway in Kaposi sarcoma, pleomorphic sarcoma and peripheral nerve sheath tumors. Strong expression of PD-1/PD-L1 in cases with previous giant cell bone tumor has been found to be interesting and must be studied in giant cell tumor samples.
BackgroundMicroRNAs (miRNAs) are short (19-23 nucleotides) non-coding RNAs that bind to sites in the 3’untranslated regions (3’UTR) of a targeted messenger RNA (mRNA). Binding leads to degradation of the transcript or blocked translation resulting in decreased expression of the targeted gene. Single nucleotide polymorphisms (SNPs) have been found in 3’UTRs that disrupt normal miRNA binding or introduce new binding sites and some of these have been associated with disease pathogenesis. This raises the importance of detecting miRNA targets and predicting the possible effects of SNPs on binding sites. In the last decade a number of studies have been conducted to predict the location of miRNA binding sites. However, there have been fewer algorithms published to analyze the effects of SNPs on miRNA binding. Moreover, the existing software has some shortcomings including the requirement for significant manual labor when working with huge lists of SNPs and that algorithms work only for SNPs present in databases such as dbSNP. These limitations become problematic as next-generation sequencing is leading to large numbers of novel variants in 3’UTRs.ResultIn order to overcome these issues, we developed a web-server named mrSNP which predicts the impact of a SNP in a 3’UTR on miRNA binding. The proposed tool reduces the manual labor requirements and allows users to input any SNP that has been identified by any SNP-calling program. In testing the performance of mrSNP on SNPs experimentally validated to affect miRNA binding, mrSNP correctly identified 69% (11/16) of the SNPs disrupting binding.ConclusionsmrSNP is a highly adaptable and performing tool for predicting the effect a 3’UTR SNP will have on miRNA binding. This tool has advantages over existing algorithms because it can assess the effect of novel SNPs on miRNA binding without requiring significant hands on time.
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