Study on the differentially expressed genes and signaling pathways in dermatomyositis using integrated bioinformatics method

Dermatomyositis is a common connective tissue disease. The occurrence and development of dermatomyositis is a result of multiple factors, but its exact pathogenesis has not been fully elucidated. Here, we used biological information method to explore and predict the major disease related genes of dermatomyositis and to find the underlying pathogenic molecular mechanism.The gene expression data of GDS1956, GDS2153, GDS2855, and GDS3417 including 94 specimens, 66 cases of dermatomyositis specimens and 28 cases of normal specimens, were obtained from the Gene Expression Omnibus database. The 4 microarray gene data groups were combined to get differentially expressed genes (DEGs). The gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs were operated by the database for annotation, visualization and integrated discovery and KEGG orthology based annotation system databases, separately. The protein-protein interaction networks of the DEGs were built from the STRING website. A total of 4097 DEGs were extracted from the 4 Gene Expression Omnibus datasets, of which 2213 genes were upregulated, and 1884 genes were downregulated. Gene ontology analysis indicated that the biological functions of DEGs focused primarily on response to virus, type I interferon signaling pathway and negative regulation of viral genome replication. The main cellular components include extracellular space, cytoplasm, and blood microparticle. The molecular functions include protein binding, double-stranded RNA binding and MHC class I protein binding. KEGG pathway analysis showed that these DEGs were mainly involved in the toll-like receptor signaling pathway, cytosolic DNA-sensing pathway, RIG-I-like receptor signaling pathway, complement and coagulation cascades, arginine and proline metabolism, phagosome signaling pathway. The following 13 closely related genes, XAF1, NT5E, UGCG, GBP2, TLR3, DDX58, STAT1, GBP1, PLSCR1, OAS3, SP100, IGK, and RSAD2, were key nodes from the protein-protein interaction network.This research suggests that exploring for DEGs and pathways in dermatomyositis using integrated bioinformatics methods could help us realize the molecular mechanism underlying the development of dermatomyositis, be of actual implication for the early detection and prophylaxis of dermatomyositis and afford reliable goals for the curing of dermatomyositis.

Novel comparison of evaluation metrics for gene ontology classifiers reveals drastic performance differences

Automated protein annotation using the Gene Ontology (GO) plays an important role in the biosciences. Evaluation has always been considered central to developing novel annotation methods, but little attention has been paid to the evaluation metrics themselves. Evaluation metrics define how well an annotation method performs and allows for them to be ranked against one another. Unfortunately, most of these metrics were adopted from the machine learning literature without establishing whether they were appropriate for GO annotations. We propose a novel approach for comparing GO evaluation metrics called Artificial Dilution Series (ADS). Our approach uses existing annotation data to generate a series of annotation sets with different levels of correctness (referred to as their signal level). We calculate the evaluation metric being tested for each annotation set in the series, allowing us to identify whether it can separate different signal levels. Finally, we contrast these results with several false positive annotation sets, which are designed to expose systematic weaknesses in GO assessment. We compared 37 evaluation metrics for GO annotation using ADS and identified drastic differences between metrics. We show that some metrics struggle to differentiate between different signal levels, while others give erroneously high scores to the false positive data sets. Based on our findings, we provide guidelines on which evaluation metrics perform well with the Gene Ontology and propose improvements to several well-known evaluation metrics. In general, we argue that evaluation metrics should be tested for their performance and we provide software for this purpose (https://bitbucket.org/plyusnin/ads/). ADS is applicable to other areas of science where the evaluation of prediction results is non-trivial.

The Gene Ontology Resource: 20 years and still GOing strong

The Gene Ontology resource (GO; http://geneontology.org) provides structured, computable knowledge regarding the functions of genes and gene products. Founded in 1998, GO has become widely adopted in the life sciences, and its contents are under continual improvement, both in quantity and in quality. Here, we report the major developments of the GO resource during the past two years. Each monthly release of the GO resource is now packaged and given a unique identifier (DOI), enabling GO-based analyses on a specific release to be reproduced in the future. The molecular function ontology has been refactored to better represent the overall activities of gene products, with a focus on transcription regulator activities. Quality assurance efforts have been ramped up to address potentially out-of-date or inaccurate annotations. New evidence codes for high-throughput experiments now enable users to filter out annotations obtained from these sources. GO-CAM, a new framework for representing gene function that is more expressive than standard GO annotations, has been released, and users can now explore the growing repository of these models. We also provide the 'GO ribbon' widget for visualizing GO annotations to a gene; the widget can be easily embedded in any web page.

Identification of key genes of papillary thyroid cancer using integrated bioinformatics analysis

OBJECTIVE

To identify novel clinically relevant genes in papillary thyroid carcinoma from public databases.

METHODS

Four original microarray datasets, GSE3678, GSE3467, GSE33630 and GSE58545, were downloaded. Differentially expressed genes (DEGs) were filtered from integrated data. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed, followed by protein-protein interaction (PPI) network construction. The CentiScape pug-in was performed to scale degree. The genes at the top of the degree distribution (≥ 95% percentile) in the significantly perturbed networks were defined as central genes. UALCAN and The Cancer Genome Atlas Clinical Explorer were used to verify clinically relevant genes and perform survival analysis.

RESULT

225 commonly changed DEGs (111 up-regulated and 114 down-regulated) were identified. The DEGs were classified into three groups by GO terms. KEGG pathway enrichment analysis showed DEGs mainly enriched in the PI3K-Akt signaling pathway, pathways in cancer, focal adhesion and proteoglycans in cancer. DEGs' protein-protein interaction (PPI) network complex was developed; six central genes (BCL2, CCND1, FN1, IRS1, COL1A1, CXCL12) were identified. Among them, BCL2, CCND1 and COL1A1 were identified as clinically relevant genes.

CONCLUSION

BCL2, CCND1 and COL1A1 may be key genes for papillary thyroid carcinoma. Further molecular biological experiments are required to confirm the function of the identified genes.

Monitoring changes in the Gene Ontology and their impact on genomic data analysis

Background

The Gene Ontology (GO) is one of the most widely used resources in molecular and cellular biology, largely through the use of "enrichment analysis." To facilitate informed use of GO, we present GOtrack (https://gotrack.msl.ubc.ca), which provides access to historical records and trends in the GO and GO annotations.

Findings

GOtrack gives users access to gene- and term-level information on annotations for nine model organisms as well as an interactive tool that measures the stability of enrichment results over time for user-provided "hit lists" of genes. To document the effects of GO evolution on enrichment, we analyzed more than 2,500 published hit lists of human genes (most older than 9 years ); 53% of hit lists were considered to yield significantly stable enrichment results.

Conclusions

Because stability is far from assured for any individual hit list, GOtrack can lead to more informed and cautious application of GO to genomics research.

The Human Phenotype Ontology: Semantic Unification of Common and Rare Disease

The Human Phenotype Ontology (HPO) is widely used in the rare disease community for differential diagnostics, phenotype-driven analysis of next-generation sequence-variation data, and translational research, but a comparable resource has not been available for common disease. Here, we have developed a concept-recognition procedure that analyzes the frequencies of HPO disease annotations as identified in over five million PubMed abstracts by employing an iterative procedure to optimize precision and recall of the identified terms. We derived disease models for 3,145 common human diseases comprising a total of 132,006 HPO annotations. The HPO now comprises over 250,000 phenotypic annotations for over 10,000 rare and common diseases and can be used for examining the phenotypic overlap among common diseases that share risk alleles, as well as between Mendelian diseases and common diseases linked by genomic location. The annotations, as well as the HPO itself, are freely available.