Purpose The objective of this study was to determine the incidence of the MYB-MFIB fusion in salivary adenoid cystic carcinoma (ACC), to establish the clinicopathological significance of the fusion and to analyze the expression of MYB in ACCs in the context of the MYB-NFIB fusion. Experimental Design We performed an extensive analysis involving 123 cancers of the salivary gland, including primary and metastatic ACCs, and non-ACC salivary carcinomas. MYB-NFIB fusions were identified by reverse transcription-PCR (RT-PCR) and sequencing of the RT-PCR products, and confirmed by fluorescence in situ hybridization. MYB RNA expression was determined by quantitative RT-PCR and protein expression was analyzed by immunohistochemistry. Results The MYB-NFIB fusion was detected in 28% primary and 35% metastatic ACCs, but not in any of the non-ACC salivary carcinomas analyzed. Different exons in both MYB and NFIB genes were involved in the fusions, resulting in expression of multiple chimeric variants. Notably, MYB was overexpressed in the vast majority of the ACCs, although MYB expression was significantly higher in tumors carrying the MYB-NFIB fusion. The presence of the MYB-NFIB fusion was significantly associated (p = 0.03) with patients older than 50 years of age. No correlation with other clinicopathological markers, factors and survival was found. Conclusions We conclude that the MYB-NFIB fusion characterizes a subset of ACCs and contributes to MYB overexpression. Additional mechanisms may be involved in MYB overexpression in ACCs lacking the MYB-NFIB fusion. These findings suggest that MYB may be a specific novel target for tumor intervention in patients with ACC.

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BackgroundMeasuring similarity between diseases plays an important role in disease-related molecular function research. Functional associations between disease-related genes and semantic associations between diseases are often used to identify pairs of similar diseases from different perspectives. Currently, it is still a challenge to exploit both of them to calculate disease similarity. Therefore, a new method (SemFunSim) that integrates semantic and functional association is proposed to address the issue.MethodsSemFunSim is designed as follows. First of all, FunSim (Functional similarity) is proposed to calculate disease similarity using disease-related gene sets in a weighted network of human gene function. Next, SemSim (Semantic Similarity) is devised to calculate disease similarity using the relationship between two diseases from Disease Ontology. Finally, FunSim and SemSim are integrated to measure disease similarity.ResultsThe high average AUC (area under the receiver operating characteristic curve) (96.37%) shows that SemFunSim achieves a high true positive rate and a low false positive rate. 79 of the top 100 pairs of similar diseases identified by SemFunSim are annotated in the Comparative Toxicogenomics Database (CTD) as being targeted by the same therapeutic compounds, while other methods we compared could identify 35 or less such pairs among the top 100. Moreover, when using our method on diseases without annotated compounds in CTD, we could confirm many of our predicted candidate compounds from literature. This indicates that SemFunSim is an effective method for drug repositioning.

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Random Projection (RP) technique has been widely applied in many scenarios because it can reduce high-dimensional features into low-dimensional space within short time and meet the need of real-time analysis of massive data. There is an urgent need of dimensionality reduction with fast increase of big genomics data. However, the performance of RP is usually lower. We attempt to improve classification accuracy of RP through combining other reduction dimension methods such as Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Feature Selection (FS). We compared classification accuracy and running time of different combination methods on three microarray datasets and a simulation dataset. Experimental results show a remarkable improvement of 14.77% in classification accuracy of FS followed by RP compared to RP on BC-TCGA dataset. LDA followed by RP also helps RP to yield a more discriminative subspace with an increase of 13.65% on classification accuracy on the same dataset. FS followed by RP outperforms other combination methods in classification accuracy on most of the datasets.

BackgroundA number of databases have been developed to collect disease-related molecular, phenotypic and environmental features (DR-MPEs), such as genes, non-coding RNAs, genetic variations, drugs, phenotypes and environmental factors. However, each of current databases focused on only one or two DR-MPEs. There is an urgent demand to develop an integrated database, which can establish semantic associations among disease-related databases and link them to provide a global view of human disease at the biological level. This database, once developed, will facilitate researchers to query various DR-MPEs through disease, and investigate disease mechanisms from different types of data.MethodologyTo establish an integrated disease-associated database, disease vocabularies used in different databases are mapped to Disease Ontology (DO) through semantic match. 4,284 and 4,186 disease terms from Medical Subject Headings (MeSH) and Online Mendelian Inheritance in Man (OMIM) respectively are mapped to DO. Then, the relationships between DR-MPEs and diseases are extracted and merged from different source databases for reducing the data redundancy.ConclusionsA semantically integrated disease-associated database (SIDD) is developed, which integrates 18 disease-associated databases, for researchers to browse multiple types of DR-MPEs in a view. A web interface allows easy navigation for querying information through browsing a disease ontology tree or searching a disease term. Furthermore, a network visualization tool using Cytoscape Web plugin has been implemented in SIDD. It enhances the SIDD usage when viewing the relationships between diseases and DR-MPEs. The current version of SIDD (Jul 2013) documents 4,465,131 entries relating to 139,365 DR-MPEs, and to 3,824 human diseases. The database can be freely accessed from: http://mlg.hit.edu.cn/SIDD.

Software fault diagnosis is a process of locating the source of faults based on the testing result (pass or fail) of each test case. It plays an important role in software debugging. However, because of the continuous expansion in software size and complexity, it becomes more and more difficult to diagnose software faults quickly and effectively. Combinatorial testing (CT) is a widely used black-box testing method. Currently, there exist some fault diagnosis methods based on CT to locate the source of faults. But they have not made full use of all information resulted from the CT process, and thus have not been very cost-effective in fault localization. This paper studies how to locate faults based on the test results of CT using a method of Delta Debugging (called Isolation). Two isolation methods, Repetitive Isolation (RI) and Strengthened Repetitive Isolation (SRI), are proposed. These two algorithms differ in the amount of CT information utilized by them. A series of experiments show that comparing to existing debug algorithm, the SRI algorithm is more cost-effective.

Human papillomavirus (HPV) can activate Toll-like receptor (TLR)/nitric oxide (NO) signaling pathways; however, whether the TLR/NO pathway is involved in cervical cancer caused by high-risk HPV (HR-HPV) remains unclear. In this study, 43 HR-HPV-positive patients with cervical cancer (CC group), 39 HR-HPV-positive patients with a healthy cervix (HR-HPV group), and 33 HR-HPV-negative controls were recruited. NO concentration in cervical canal and expression of inducible NO synthase (iNOS) in cervical tissues were detected. Expressions of key TLR/NO pathway genes (TLR3/4/7/8, NF-κB p65, and iNOS) in cervical epithelial cells were detected by quantitative reverse transcription PCR. Expressions of TLR4, NF-κB p65, and iNOS in CaSki, HeLa, and C33a cells were determined by Western blot. NO concentration in cervical canal of CC group was significantly higher than in other groups (P < 0.05). Positive rates of iNOS in cervical tissues were 72.1%, 28.2%, and 3.1% in the CC group, HR-HPV group, and controls, respectively (P < 0.05). Levels of TLR3, TLR4, TLR7, TLR8, NF-κB p65, and iNOS in cervical epithelial cells were higher in CC group than in other groups (P < 0.05). Both mRNA and protein levels of TLR4, NF-κB p65, and iNOS were higher in HPV-positive HeLa and CaSki cells than in HPV-negative C33a cells (P < 0.05). Together, these results suggest that TLR/NO signaling pathway may be involved in pathogenesis of cervical cancer caused by HR-HPV.