RNA sequencing and bioinformatics analysis revealed PACSIN3 as a potential novel biomarker for platinum resistance in epithelial ovarian cancer.

BACKGROUND: Failure to respond to treatment in epithelial ovarian cancer can often be attributed to platinum-based chemotherapy resistance. However, the possible mechanisms or candidate biomarkers associated with platinum resistance are yet to be elucidated, even though many researchers have performed related studies. METHODS: We performed RNA sequencing of clinical specimens obtained from patients with platinum-sensitive or resistant epithelial ovarian cancer (EOC). Furthermore, various bioinformatics approaches, including spatial analysis of functional enrichment, were used to identify key regulators and associated underlying mechanisms of platinum resistance in EOC. RESULTS: Through RNA-sequencing, we identified 263 differentially expressed genes (98 upregulated and 165 downregulated) and subjected them to Gene Oncology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses, which were characterized to the traditional platinum-resistant characteristics. Subsequently, the gene interaction network and module analysis by spatial analysis of functional enrichment software demonstrated protein kinase C and casein kinase substrate in neurons 3 (PACSIN3) as the only upregulated hub gene, and neurotensin (NTS) and KIAA0319 as downregulated hub genes in platinum-resistant EOC. We selected PACSIN3 for further analysis because it has not been studied in relation to response to platinum-based chemotherapy. PACSIN3 was significantly upregulated in ovarian cancer cells compared to immortalized human ovarian surface epithelial cells. In addition, cisplatin-induced apoptosis was measured in PACSIN3 knockout OVCA433 and BRCA-mutated EOC cell line, SNU251, by a fluorescence-activated cell sorting-based Annexin-V/propium iodide double staining assay, which revealed a significant increase in apoptosis. CONCLUSIONS: Taken together, the present study presents PACSIN3 as a promising predictive biomarker associated with platinum resistance, especially in BRCA-mutated epithelial ovarian cancers.

Evaluation of an Automated Screening Assay, Compared to Indirect Immunofluorescence, an Extractable Nuclear Antigen Assay, and a Line Immunoassay in a Large Cohort of Asian Patients with Antinuclear Antibody-Associated Rheumatoid Diseases: A Multicenter Retrospective Study.

We assessed the diagnostic utility of the connective tissue disease (CTD) screen as an automated screening test, in comparison with the indirect immunofluorescence (IIF), EliA extractable nuclear antigen (ENA), and line immunoassay (LIA) for patients with antinuclear antibody- (ANA-) associated rheumatoid disease (AARD). A total of 1115 serum samples from two university hospitals were assayed using these four autoantibody-based methods. The AARD group consisted of patients with systemic lupus erythematosus (SLE), systemic sclerosis (SSc), Sjögren's syndrome (SS), and mixed connective tissue disease (MCTD). The qualitative results of all four autoantibody assays showed a significant association with AARDs, compared to controls (P < 0.0001 for all). The areas under the receiver operating characteristic curves (ROC-AUCs) of the CTD screen for differentiating total AARDs, SLE, SSc, SS, and MCTD from controls were 0.89, 0.93, 0.73, 0.93, and 0.95, respectively. The ROC-AUCs of combination testing with LIA were slightly higher in patients with AARDs (0.92) than those of CTD screen alone. Multivariate analysis indicated that all four autoantibody assays could independently predict AARDs. CTD screening alone and in combination with IIF, EliA ENA, and LIA are potentially valuable diagnostic approaches for predicting AARDs. Combining CTD screen with LIA might be effective for AARD patients.

GWAB: a web server for the network-based boosting of human genome-wide association data.

During the last decade, genome-wide association studies (GWAS) have represented a major approach to dissect complex human genetic diseases. Due in part to limited statistical power, most studies identify only small numbers of candidate genes that pass the conventional significance thresholds (e.g. P ≤ 5 × 10-8). This limitation can be partly overcome by increasing the sample size, but this comes at a higher cost. Alternatively, weak association signals can be boosted by incorporating independent data. Previously, we demonstrated the feasibility of boosting GWAS disease associations using gene networks. Here, we present a web server, GWAB (www.inetbio.org/gwab), for the network-based boosting of human GWAS data. Using GWAS summary statistics (P-values) for SNPs along with reference genes for a disease of interest, GWAB reprioritizes candidate disease genes by integrating the GWAS and network data. We found that GWAB could more effectively retrieve disease-associated reference genes than GWAS could alone. As an example, we describe GWAB-boosted candidate genes for coronary artery disease and supporting data in the literature. These results highlight the inherent value in sub-threshold GWAS associations, which are often not publicly released. GWAB offers a feasible general approach to boost such associations for human disease genetics.

MUFFINN: cancer gene discovery via network analysis of somatic mutation data.

A major challenge for distinguishing cancer-causing driver mutations from inconsequential passenger mutations is the long-tail of infrequently mutated genes in cancer genomes. Here, we present and evaluate a method for prioritizing cancer genes accounting not only for mutations in individual genes but also in their neighbors in functional networks, MUFFINN (MUtations For Functional Impact on Network Neighbors). This pathway-centric method shows high sensitivity compared with gene-centric analyses of mutation data. Notably, only a marginal decrease in performance is observed when using 10 % of TCGA patient samples, suggesting the method may potentiate cancer genome projects with small patient populations.

Prioritizing candidate disease genes by network-based boosting of genome-wide association data.

Network "guilt by association" (GBA) is a proven approach for identifying novel disease genes based on the observation that similar mutational phenotypes arise from functionally related genes. In principle, this approach could account even for nonadditive genetic interactions, which underlie the synergistic combinations of mutations often linked to complex diseases. Here, we analyze a large-scale, human gene functional interaction network (dubbed HumanNet). We show that candidate disease genes can be effectively identified by GBA in cross-validated tests using label propagation algorithms related to Google's PageRank. However, GBA has been shown to work poorly in genome-wide association studies (GWAS), where many genes are somewhat implicated, but few are known with very high certainty. Here, we resolve this by explicitly modeling the uncertainty of the associations and incorporating the uncertainty for the seed set into the GBA framework. We observe a significant boost in the power to detect validated candidate genes for Crohn's disease and type 2 diabetes by comparing our predictions to results from follow-up meta-analyses, with incorporation of the network serving to highlight the JAK-STAT pathway and associated adaptors GRB2/SHC1 in Crohn's disease and BACH2 in type 2 diabetes. Consideration of the network during GWAS thus conveys some of the benefits of enrolling more participants in the GWAS study. More generally, we demonstrate that a functional network of human genes provides a valuable statistical framework for prioritizing candidate disease genes, both for candidate gene-based and GWAS-based studies.

Exception discovery: a novel method for the identification of differentially expressed proteins.

The identification of differentially expressed proteins (DEPs) observed under specific conditions is one of the key issues in proteomics research. There are currently several ways to detect the changes of a specific protein's expression level in two-dimensional electrophoresis (2-DE) gel images such as statistical analysis and graphical visualization. However, it is quite difficult to handle the information of an individual protein manually by these methods due to the large distortions of patterns in 2-DE images. This paper proposes a method of analyzing DEPs for a specific disease. In order to automatically extract meaningful DEPs in a set of 2-DE gel images, we have designed an exception function that is suitable to measure the anomalous change of the expression level of an individual protein. We present the comparison results of the proposed method versus a Wilcoxon paired t -test that is one of the widely used statistical analysis methods. Several experiments are performed to address not only the effectiveness of the exception function but also the fact that these two methods can compensate each other practically.

A landmark extraction method for protein 2DE gel images based on multi-dimensional clustering.

OBJECTIVE: Two-dimensional electrophoresis (2DE) is a separation technique that can identify target proteins existing in a tissue. Its result is represented by a gel image that displays an individual protein in a tissue as a spot. However, because the technique suffers from low reproducibility, a user should manually annotate landmark spots on each gel image to analyze the spots of different images together. This operation is an error-prone and tedious job. For this reason, this paper proposes a method of extracting landmark spots automatically by using a data mining technique. METHOD AND MATERIAL: A landmark profile which summarizes the characteristics of landmark spots in a set of training gel images of the same tissue is generated by extracting the common properties of the landmark spots. On the basis of the landmark profile, candidate landmark spots in a new gel image of the same tissue are identified, and final landmark spots are determined by the well-known A* search algorithm. RESULT AND CONCLUSIONS: The performance of the proposed method is analyzed through a series of experiments in order to identify its various characteristics.

An integrated proteome database for two-dimensional electrophoresis data analysis and laboratory information management system.

We describe an integrated proteome database, termed Yonsei Proteome Research Center Proteome Database (YPRC-PDB) which can store, retrieve and analyze various information including two-dimensional electrophoresis (2-DE) images and associated spot information that were obtained during studies of hepatocellular carcinoma (HCC). YPRC-PDB is also designed to perform as a laboratory information management system that manages sample information, clinical background, conditions of both sample preparation and 2-DE, and entire sets of experimental results. It also features query system and data-mining applications, which are amenable to automatically analyze expression level changes of a specific protein and directly link to clinical information. The user interface is web-based, so that the results from other laboratories can be shared effectively. In particular, the master gel image query is equipped with a graphic tool that can easily identify the relationship between the specific pathological stage of HCC and expression levels of a potential marker protein on the master gel image. Thus, YPRC-PDB is a versatile integrated database suitable for subsequent analyses. The information in YPRC-PDB is updated easily and it is available to authorized users on the World Wide Web (http://yprcpdb.proteomix.org/ approximately damduck/).