One of the aims of high-throughput gene/protein profiling experiments is the identification of biological processes altered between two or more conditions. Pathway analysis is an umbrella term for a multitude of computational approaches used for this purpose. While in the beginning pathway analysis relied on enrichment-based approaches, a newer generation of methods is now available, exploiting pathway topologies in addition to gene/protein expression levels. However, little effort has been invested in their critical assessment with respect to their performance in different experimental setups. Here, we assessed the performance of seven representative methods identifying differentially expressed pathways between two groups of interest based on gene expression data with prior knowledge of pathway topologies: SPIA, PRS, CePa, TAPPA, TopologyGSA, Clipper and DEGraph. We performed a number of controlled experiments that investigated their sensitivity to sample and pathway size, threshold-based filtering of differentially expressed genes, ability to detect target pathways, ability to exploit the topological information and the sensitivity to different pre-processing strategies. We also verified type I error rates and described the influence of overexpression of single genes, gene sets and topological motifs of various sizes on the detection of a pathway as differentially expressed. The results of our experiments demonstrate a wide variability of the tested methods. We provide a set of recommendations for an informed selection of the proper method for a given data analysis task.

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BackgroundPathway analysis methods, in which differentially expressed genes are mapped to databases of reference pathways and relative enrichment is assessed, help investigators to propose biologically relevant hypotheses. The last generation of pathway analysis methods takes into account the topological structure of a pathway, which helps to increase both specificity and sensitivity of the findings. Simultaneously, the RNA-Seq technology is gaining popularity and becomes widely used for gene expression profiling. Unfortunately, majority of topological pathway analysis methods remains without implementation and if an implementation exists, it is limited in various factors.ResultsWe developed a new R/Bioconductor package ToPASeq offering uniform interface to seven distinct topology-based pathway analysis methods, of which three we implemented de-novo and four were adjusted from existing implementations. Apart this, ToPASeq offers a set of tailored visualization functions and functions for importing and manipulating pathways and their topologies, facilitating the application of the methods on different species. The package can be used to compare the differential expression of pathways between two conditions on both gene expression microarray and RNA-Seq data. The package is written in R and is available from Bioconductor 3.2 using AGPL-3 license.ConclusionToPASeq is a novel package that offers seven distinct methods for topology-based pathway analysis, which are easily applicable on microarray as well as RNA-Seq data, both in human and other species. At the same time, it provides specific tools for visualization of the results.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0763-1) contains supplementary material, which is available to authorized users.

Lung adenocarcinoma (LAC) is the most common form of lung cancer that increases in non-smokers at younger age. Altered protein glycosylation is one of the hallmarks of malignancy, its role in cancer progression is still poorly understood. In this study, we report mass spectrometric (MS) analysis of N-glycans released from fresh or defrosted tissue specimens from 24 patients with LAC. Comparison of cancerous versus adjacent healthy tissues revealed substantial differences in N-glycan profiles associated with disease. The significant increase in paucimannose and high-mannose glycans with 6-9 mannose residues and decline in the sialylated complex biantenary core fucosylated glycan with composition NeuAcGal 2 GlcNAc 2-Man 3 GlcNAc 2 Fuc were general features of tumors. In addition, 42 new N-glycan compositions were detected in cancerous tissues. The prominent changes in advanced disease stages were mostly observed in core fucosylated N-glycans with additional fucose (Fuc) residue/s and enhanced branching with non-galactosylated N-acetyl-glucosamine (GlcNAc) units. Both of these monosaccharide types were linked preferably on the 6-antenna. Importantly, as compared with noncancerous tissues, a number of these significant changes were clearly detectable early on in stage I. Application of N-glycan data obtained from tissues was next assessed and validated for evaluation of small sized biopsies obtained via bronchoscopy. In summary, observed alterations and data of newly detected N-glycans expand knowledge about the glycosylation in LAC and may contribute to research in more tailored therapies. Moreover, the results demonstrate effectiveness of the presented approach for utility in rapid discrimination of cancerous from healthy lung tissues.

BackgroundMultiple myeloma (MM) is a low proliferative tumor of postgerminal center plasma cell (PC). Centrosome amplification (CA) is supposed to be one of the mechanisms leading to chromosomal instability. Also, CA is associated with deregulation of cell cycle, mitosis, DNA repair and proliferation. The aim of our study was to evaluate the prognostic significance and possible role of CA in pathogenesis and analysis of mitotic genes as mitotic disruption markers.Design and methodsA total of 173 patients were evaluated for this study. CD138+ cells were separated by MACS. Immunofluorescent labeling of centrin was used for evaluation of centrosome amplification in PCs. Interphase FISH with cytoplasmic immunoglobulin light chain staining (cIg FISH) and qRT-PCR were performed on PCs.ResultsBased on the immunofluorescent staining results, all patients were divided into two groups: CA positive (38.2%) and CA negative (61.8%). Among the newly diagnosed patients, worse overall survival was indicated in the CA negative group (44/74) in comparison to the CA positive group (30/74) (P = 0.019).Gene expression was significantly down-regulated in the CA positive group in comparison to CA negative in the following genes: AURKB, PLK4, TUBG1 (P < 0.05). Gene expression was significantly down-regulated in newly diagnosed in comparison to relapsed patients in the following genes: AURKA, AURKB, CCNB1, CCNB2, CETN2, HMMR, PLK4, PCNT, and TACC3 (P < 0.05).ConclusionsOur findings indicate better prognosis for CA positive newly diagnosed patients. Considering revealed clinical and gene expression heterogeneity between CA negative and CA positive patients, there is a possibility to characterize centrosome amplification as a notable event in multiple myeloma pathogenesis.