Pathway crosstalk analysis based on protein-protein network analysis in ovarian cancer

Bile Ductal Transcriptome Identifies Key Pathways and Hub Genes in Clonorchis sinensis-Infected Sprague-Dawley Rats

Clonorchis sinensis is a food-borne trematode that infects more than 15 million people. The liver fluke causes clonorchiasis and chronical cholangitis, and promotes cholangiocarcinoma. The underlying molecular pathogenesis occurring in the bile duct by the infection is little known. In this study, transcriptome profile in the bile ducts infected with C. sinensis were analyzed using microarray methods. Differentially expressed genes (DEGs) were 1,563 and 1,457 at 2 and 4 weeks after infection. Majority of the DEGs were temporally dysregulated at 2 weeks, but 519 DEGs showed monotonically changing expression patterns that formed seven distinct expression profiles. Protein-protein interaction (PPI) analysis of the DEG products revealed 5 sub-networks and 10 key hub proteins while weighted co-expression network analysis (WGCNA)-derived gene-gene interaction exhibited 16 co-expression modules and 13 key hub genes. The DEGs were significantly enriched in 16 Kyoto Encyclopedia of Genes and Genomes pathways, which were related to original systems, cellular process, environmental information processing, and human diseases. This study uncovered a global picture of gene expression profiles in the bile ducts infected with C. sinensis, and provided a set of potent predictive biomarkers for early diagnosis of clonorchiasis.

Multiple Testing of Submatrices of a Precision Matrix with Applications to Identification of Between Pathway Interactions

Making accurate inference for gene regulatory networks, including inferring about pathway by pathway interactions, is an important and difficult task. Motivated by such genomic applications, we consider multiple testing for conditional dependence between subgroups of variables. Under a Gaussian graphical model framework, the problem is translated into simultaneous testing for a collection of submatrices of a high-dimensional precision matrix with each submatrix summarizing the dependence structure between two subgroups of variables. A novel multiple testing procedure is proposed and both theoretical and numerical properties of the procedure are investigated. Asymptotic null distribution of the test statistic for an individual hypothesis is established and the proposed multiple testing procedure is shown to asymptotically control the false discovery rate (FDR) and false discovery proportion (FDP) at the pre-specified level under regularity conditions. Simulations show that the procedure works well in controlling the FDR and has good power in detecting the true interactions. The procedure is applied to a breast cancer gene expression study to identify between pathway interactions.

SKP2/P27Kip1 pathway is associated with Advanced Ovarian Cancer in Saudi Patients

BACKGROUND

Ovarian cancer is the most common gynecological malignancy and constitutes the fifth leading cause of female cancer death. Some biological parameters have prognostic roles in patients with advanced ovarian cancer and their expression may contribute to tumor progression. The aim of this study was to investigate the potential prognostic value of SKP2, genes P27Kip1, K-ras, c-Myc, COX2 and HER2 genes expression in ovarian cancer.

MATERIALS AND METHODS

This study was performed on two hundred formalin fixed paraffin embedded ovarian cancer and normal adjacent tissues (NAT). Gene expression levels were assessed using real time PCR and Western blotting.

RESULTS

Elevated expression levels of SKP2, K-ras, c-Myc, HER2 and COX2 genes were observed in 61.5% (123/200), 92.5% (185/200), 74% (148/200), 96 % (192/200), 90% (180/200) and 78.5% (157/200) of cancer tissues, respectively. High expression of SKP2 and down-regulation of P27 was associated with advanced stages of cancer.

CONCLUSIONS

The association between high expression of c-Myc and SKP2 with low expression of P27 suggested that the Skp2-P27 pathway may play an important role in ovarian carcinogenesis. Reduced expression of P27 is associated with advanced stage of cancer and can be used as a biological marker in clinical routine assessment and management of women with advanced ovarian cancer.

Integrative meta-analysis of multiple gene expression profiles in acquired gemcitabine-resistant cancer cell lines to identify novel therapeutic biomarkers

In molecular-targeted cancer therapy, acquired resistance to gemcitabine is a major clinical problem that reduces its effectiveness, resulting in recurrence and metastasis of cancers. In spite of great efforts to reveal the overall mechanism of acquired gemcitabine resistance, no definitive genetic factors have been identified that are absolutely responsible for the resistance process. Therefore, we performed a cross-platform meta-analysis of three publically available microarray datasets for cancer cell lines with acquired gemcitabine resistance, using the R-based RankProd algorithm, and were able to identify a total of 158 differentially expressed genes (DEGs; 76 up- and 82 down-regulated) that are potentially involved in acquired resistance to gemcitabine. Indeed, the top 20 up- and down-regulated DEGs are largely associated with a common process of carcinogenesis in many cells. For the top 50 up- and down-regulated DEGs, we conducted integrated analyses of a gene regulatory network, a gene co-expression network, and a protein-protein interaction network. The identified DEGs were functionally enriched via Gene Ontology hierarchy and Kyoto Encyclopedia of Genes and Genomes pathway analyses. By systemic combinational analysis of the three molecular networks, we could condense the total number of DEGs to final seven genes. Notably, GJA1, LEF1, and CCND2 were contained within the lists of the top 20 up- or down-regulated DEGs. Our study represents a comprehensive overview of the gene expression patterns associated with acquired gemcitabine resistance and theoretical support for further clinical therapeutic studies.

NRP1 is targeted by miR-130a and miR-130b, and is associated with multidrug resistance in epithelial ovarian cancer based on integrated gene network analysis

Multidrug resistance (MDR) in epithelial ovarian cancer (EOC) remains a public health issue for women worldwide, and its molecular mechanisms remain to be fully elucidated. The present study aimed to predict the potential genes involved in MDR, and examine the mechanisms underlying MDR in EOC using bioinformatics techniques. In the present study, four public microarray datasets, including GSE41499, GSE33482, GSE15372 and GSE28739, available in Gene Expression Omnibus were downloaded, and 11 microRNAs (miRNA; miRs), including miR-130a, miR-214, let-7i, miR-125b, miR-376c, miR-199a, miR-93, miR-141, miR-130b, miR-193b^* and miR-200c, from previously published reports in PubMed were used to perform a comprehensive bioinformatics analysis through gene expression analysis, signaling pathway analysis, literature co-occurrence and miRNA-mRNA interaction networks. The results demonstrated that the expression of neuropilin 1 (NRP1) was upregulated, thereby acting as the most important hub gene in the integrated gene network. NRP1 was targeted by miR-130a and miR-130b at the binding site of chromosome 10: 33466864-3466870, which was involved in the axon guidance signaling pathway. These results suggested that alteration of the gene expression levels of NRP1 expression may contribute to MDR in EOC. These data provide important information for further experimental investigations of the drug resistance-associated functions of NRP1 in EOC.

A Hybrid of Metabolic Flux Analysis and Bayesian Factor Modeling for Multiomic Temporal Pathway Activation

The growing availability of multiomic data provides a highly comprehensive view of cellular processes at the levels of mRNA, proteins, metabolites, and reaction fluxes. However, due to probabilistic interactions between components depending on the environment and on the time course, casual, sometimes rare interactions may cause important effects in the cellular physiology. To date, interactions at the pathway level cannot be measured directly, and methodologies to predict pathway cross-correlations from reaction fluxes are still missing. Here, we develop a multiomic approach of flux-balance analysis combined with Bayesian factor modeling with the aim of detecting pathway cross-correlations and predicting metabolic pathway activation profiles. Starting from gene expression profiles measured in various environmental conditions, we associate a flux rate profile with each condition. We then infer pathway cross-correlations and identify the degrees of pathway activation with respect to the conditions and time course using Bayesian factor modeling. We test our framework on the most recent metabolic reconstruction of Escherichia coli in both static and dynamic environments, thus predicting the functionality of particular groups of reactions and how it varies over time. In a dynamic environment, our method can be readily used to characterize the temporal progression of pathway activation in response to given stimuli.

Protein-protein interaction network analyses for elucidating the roles of LOXL2-delta72 in esophageal squamous cell carcinoma

Lysyl oxidase-like 2 (LOXL2), a member of the lysyl oxidase (LOX) family, is a copper-dependent enzyme that catalyzes oxidative deamination of lysine residues on protein substrates. LOXL2 was found to be overexpressed in esophageal squamous cell carcinoma (ESCC) in our previous research. We later identified a LOXL2 splicing variant LOXL2-delta72 and we overexpressed LOXL2-delta72 and its wild type counterpart in ESCC cells following microarray analyses. First, the differentially expressed genes (DEGs) of LOXL2 and LOXL2-delta72 compared to empty plasmid were applied to generate protein-protein interaction (PPI) sub-networks. Comparison of these two sub-networks showed hundreds of different proteins. To reveal the potential specific roles of LOXL2- delta72 compared to its wild type, the DEGs of LOXL2-delta72 vs LOXL2 were also applied to construct a PPI sub-network which was annotated by Gene Ontology. The functional annotation map indicated the third PPI sub-network involved hundreds of GO terms, such as "cell cycle arrest", "G1/S transition of mitotic cell cycle", "interphase", "cell-matrix adhesion" and "cell-substrate adhesion", as well as significant "immunity" related terms, such as "innate immune response", "regulation of defense response" and "Toll signaling pathway". These results provide important clues for experimental identification of the specific biological roles and molecular mechanisms of LOXL2-delta72. This study also provided a work flow to test the different roles of a splicing variant with high-throughput data.