scholarly journals Establishing a consensus for the hallmarks of cancer based on gene ontology and pathway annotations

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yi Chen ◽  
Fons. J. Verbeek ◽  
Katherine Wolstencroft
Keyword(s):  
Gene Ontology ◽  
Enrichment Analysis ◽  
Biological Data ◽  
Hallmarks Of Cancer ◽  
High Throughput Analysis ◽  
Knowledge Resources ◽  
Gene Set ◽  
Cancer Hallmarks ◽  
Starting Point ◽  
High Level

Abstract Background The hallmarks of cancer provide a highly cited and well-used conceptual framework for describing the processes involved in cancer cell development and tumourigenesis. However, methods for translating these high-level concepts into data-level associations between hallmarks and genes (for high throughput analysis), vary widely between studies. The examination of different strategies to associate and map cancer hallmarks reveals significant differences, but also consensus. Results Here we present the results of a comparative analysis of cancer hallmark mapping strategies, based on Gene Ontology and biological pathway annotation, from different studies. By analysing the semantic similarity between annotations, and the resulting gene set overlap, we identify emerging consensus knowledge. In addition, we analyse the differences between hallmark and gene set associations using Weighted Gene Co-expression Network Analysis and enrichment analysis. Conclusions Reaching a community-wide consensus on how to identify cancer hallmark activity from research data would enable more systematic data integration and comparison between studies. These results highlight the current state of the consensus and offer a starting point for further convergence. In addition, we show how a lack of consensus can lead to large differences in the biological interpretation of downstream analyses and discuss the challenges of annotating changing and accumulating biological data, using intermediate knowledge resources that are also changing over time.

Establishing Consensus Annotation for the Hallmarks of Cancer

2020 ◽  
Author(s):  
Yi Chen ◽  
Fons Verbeek ◽  
Katherine Wolstencroft
Keyword(s):  
Biological Knowledge ◽  
Biological Processes ◽  
Hallmarks Of Cancer ◽  
Knowledge Resources ◽  
Cancer Hallmarks ◽  
The Core ◽  
High Level ◽  
Pathway Databases ◽  
Over Time ◽  
Highly Cited

Abstract Motivation: The hallmarks of cancer provide a highly cited and well-used conceptual framework for describing the processes involved in cancer cell development and tumourigenesis. However, methods for translating these high-level hallmarks concepts into data level-links between individual genes and individual cancer hallmarks varies widely between studies. When we examine different strategies for linking and mapping cancer hallmarks in detail, we see significant differences, but also consensus. Results: Here we compare hallmark mapping schemes from multiple studies and explore the consensus knowledge from these different approaches, in order to help us better understand the core biological processes and pathways that are associated with the hallmarks of cancer. We also explore the differences between mapping schemes and identify which differences represent changes in our understanding of cancer, changes in our understanding of biological processes in the non-disease state, or the accumulation of more experimental evidence over time. Conclusions: Mapping strategies rely on intermediate knowledge resources, such as biological pathway databases like KEGG or the Gene Ontology. The structure and annotations of these intermediate resources also change over time. The results of this study therefore highlight the challenges of integrating accumulated, distributed and changing biological knowledge in bioinformatics.

scholarly journals GSAn: an alternative to enrichment analysis for annotating gene sets

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Aaron Ayllon-Benitez ◽  
Romain Bourqui ◽  
Patricia Thébault ◽  
Fleur Mougin
Keyword(s):  
Gene Ontology ◽  
Semantic Similarity ◽  
A Priori ◽  
Similarity Measures ◽  
Enrichment Analysis ◽  
Biological Information ◽  
Underlying Structure ◽  
Gene Set ◽  
Sequencing Technologies ◽  
Gene Coverage

Abstract The revolution in new sequencing technologies is greatly leading to new understandings of the relations between genotype and phenotype. To interpret and analyze data that are grouped according to a phenotype of interest, methods based on statistical enrichment became a standard in biology. However, these methods synthesize the biological information by a priori selecting the over-represented terms and may suffer from focusing on the most studied genes that represent a limited coverage of annotated genes within a gene set. Semantic similarity measures have shown great results within the pairwise gene comparison by making advantage of the underlying structure of the Gene Ontology. We developed GSAn, a novel gene set annotation method that uses semantic similarity measures to synthesize a priori Gene Ontology annotation terms. The originality of our approach is to identify the best compromise between the number of retained annotation terms that has to be drastically reduced and the number of related genes that has to be as large as possible. Moreover, GSAn offers interactive visualization facilities dedicated to the multi-scale analysis of gene set annotations. Compared to enrichment analysis tools, GSAn has shown excellent results in terms of maximizing the gene coverage while minimizing the number of terms.

scholarly journals Transcriptional Effects of Psychoactive Drugs on Genes Involved in Neurogenesis

2020 ◽  
Vol 21 (21) ◽  
pp. 8333
Author(s):  
Chiara C. Bortolasci ◽  
Briana Spolding ◽  
Srisaiyini Kidnapillai ◽  
Timothy Connor ◽  
Trang T.T. Truong ◽  
...  
Keyword(s):  
Gene Ontology ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Gas Chromatography Mass Spectrometry ◽  
Transcriptional Level ◽  
Psychoactive Drugs ◽  
Gene Set ◽  
Expression Of Genes

Although neurogenesis is affected in several psychiatric diseases, the effects and mechanisms of action of psychoactive drugs on neurogenesis remain unknown and/or controversial. This study aims to evaluate the effects of psychoactive drugs on the expression of genes involved in neurogenesis. Neuronal-like cells (NT2-N) were treated with amisulpride (10 µM), aripiprazole (0.1 µM), clozapine (10 µM), lamotrigine (50 µM), lithium (2.5 mM), quetiapine (50 µM), risperidone (0.1 µM), or valproate (0.5 mM) for 24 h. Genome wide mRNA expression was quantified and analysed using gene set enrichment analysis, with the neurogenesis gene set retrieved from the Gene Ontology database and the Mammalian Adult Neurogenesis Gene Ontology (MANGO) database. Transcription factors that are more likely to regulate these genes were investigated to better understand the biological processes driving neurogenesis. Targeted metabolomics were performed using gas chromatography-mass spectrometry. Six of the eight drugs decreased the expression of genes involved in neurogenesis in both databases. This suggests that acute treatment with these psychoactive drugs negatively regulates the expression of genes involved in neurogenesis in vitro. SOX2 and three of its target genes (CCND1, BMP4, and DKK1) were also decreased after treatment with quetiapine. This can, at least in part, explain the mechanisms by which these drugs decrease neurogenesis at a transcriptional level in vitro. These results were supported by the finding of increased metabolite markers of mature neurons following treatment with most of the drugs tested, suggesting increased proportions of mature relative to immature neurons consistent with reduced neurogenesis.

A MULTIVARIATE EXTENSION OF THE GENE SET ENRICHMENT ANALYSIS

2007 ◽  
Vol 05 (05) ◽  
pp. 1139-1153 ◽  
Author(s):  
LEV KLEBANOV ◽  
GALINA GLAZKO ◽  
PETER SALZMAN ◽  
ANDREI YAKOVLEV ◽  
YUANHUI XIAO
Keyword(s):  
Null Distribution ◽  
Enrichment Analysis ◽  
Biological Data ◽  
Gene Set Enrichment Analysis ◽  
Future Research ◽  
Test Statistic ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Joint Distributions ◽  
Gene Sets

A test-statistic typically employed in the gene set enrichment analysis (GSEA) prevents this method from being genuinely multivariate. In particular, this statistic is insensitive to changes in the correlation structure of the gene sets of interest. The present paper considers the utility of an alternative test-statistic in designing the confirmatory component of the GSEA. This statistic is based on a pertinent distance between joint distributions of expression levels of genes included in the set of interest. The null distribution of the proposed test-statistic, known as the multivariate N-statistic, is obtained by permuting group labels. Our simulation studies and analysis of biological data confirm the conjecture that the N-statistic is a much better choice for multivariate significance testing within the framework of the GSEA. We also discuss some other aspects of the GSEA paradigm and suggest new avenues for future research.

scholarly journals The PARP Enzyme Family and the Hallmarks of Cancer Part 1. Cell Intrinsic Hallmarks

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2042 ◽  
Author(s):  
Máté A. Demény ◽  
László Virág
Keyword(s):  
Cancer Biology ◽  
Cancer Metabolism ◽  
Replicative Senescence ◽  
Synthetic Lethality ◽  
Family Members ◽  
Hallmarks Of Cancer ◽  
Novel Therapy ◽  
Cancer Hallmarks ◽  
Enzyme Family ◽  
Wide Range

The 17-member poly (ADP-ribose) polymerase enzyme family, also known as the ADP-ribosyl transferase diphtheria toxin-like (ARTD) enzyme family, contains DNA damage-responsive and nonresponsive members. Only PARP1, 2, 5a, and 5b are capable of modifying their targets with poly ADP-ribose (PAR) polymers; the other PARP family members function as mono-ADP-ribosyl transferases. In the last decade, PARP1 has taken center stage in oncology treatments. New PARP inhibitors (PARPi) have been introduced for the targeted treatment of breast cancer 1 or 2 (BRCA1/2)-deficient ovarian and breast cancers, and this novel therapy represents the prototype of the synthetic lethality paradigm. Much less attention has been paid to other PARPs and their potential roles in cancer biology. In this review, we summarize the roles played by all PARP enzyme family members in six intrinsic hallmarks of cancer: uncontrolled proliferation, evasion of growth suppressors, cell death resistance, genome instability, reprogrammed energy metabolism, and escape from replicative senescence. In a companion paper, we will discuss the roles of PARP enzymes in cancer hallmarks related to cancer-host interactions, including angiogenesis, invasion and metastasis, evasion of the anticancer immune response, and tumor-promoting inflammation. While PARP1 is clearly involved in all ten cancer hallmarks, an increasing body of evidence supports the role of other PARPs in modifying these cancer hallmarks (e.g., PARP5a and 5b in replicative immortality and PARP2 in cancer metabolism). We also highlight controversies, open questions, and discuss prospects of recent developments related to the wide range of roles played by PARPs in cancer biology. Some of the summarized findings may explain resistance to PARPi therapy or highlight novel biological roles of PARPs that can be therapeutically exploited in novel anticancer treatment paradigms.

scholarly journals Higher Acid-Base Imbalance Associated with Respiratory Failure Could Decrease the Survival of Patients with Scrub Typhus during Intensive Care Unit Stay: A Gene Set Enrichment Analysis

2019 ◽  
Vol 8 (10) ◽  
pp. 1580 ◽  
Author(s):  
Kyoung Min Moon ◽  
Kyueng-Whan Min ◽  
Mi-Hye Kim ◽  
Dong-Hoon Kim ◽  
Byoung Kwan Son ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Respiratory Failure ◽  
Scrub Typhus ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Acid Base ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Gene Sets

Ninety percent of patients with scrub typhus (SC) with vasculitis-like syndrome recover after mild symptoms; however, 10% can suffer serious complications, such as acute respiratory failure (ARF) and admission to the intensive care unit (ICU). Predictors for the progression of SC have not yet been established, and conventional scoring systems for ICU patients are insufficient to predict severity. We aimed to identify simple and robust indicators to predict aggressive behaviors of SC. We evaluated 91 patients with SC and 81 non-SC patients who were admitted to the ICU, and 32 cases from the public functional genomics data repository for gene expression analysis. We analyzed the relationships between several predictors and clinicopathological characteristics in patients with SC. We performed gene set enrichment analysis (GSEA) to identify SC-specific gene sets. The acid-base imbalance (ABI), measured 24 h before serious complications, was higher in patients with SC than in non-SC patients. A high ABI was associated with an increased incidence of ARF, leading to mechanical ventilation and worse survival. GSEA revealed that SC correlated to gene sets reflecting inflammation/apoptotic response and airway inflammation. ABI can be used to indicate ARF in patients with SC and assist with early detection.

scholarly journals Gene set enrichment analysis for genome-wide DNA methylation data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jovana Maksimovic ◽  
Alicia Oshlack ◽  
Belinda Phipson
Keyword(s):  
Dna Methylation ◽  
Enrichment Analysis ◽  
R Package ◽  
Gene Set Enrichment Analysis ◽  
Methylation Array ◽  
Gene Set ◽  
Genome Wide ◽  
Genome Methylation ◽  
Unbiased Gene ◽  
Gene Set Testing

AbstractDNA methylation is one of the most commonly studied epigenetic marks, due to its role in disease and development. Illumina methylation arrays have been extensively used to measure methylation across the human genome. Methylation array analysis has primarily focused on preprocessing, normalization, and identification of differentially methylated CpGs and regions. GOmeth and GOregion are new methods for performing unbiased gene set testing following differential methylation analysis. Benchmarking analyses demonstrate GOmeth outperforms other approaches, and GOregion is the first method for gene set testing of differentially methylated regions. Both methods are publicly available in the missMethyl Bioconductor R package.

scholarly journals IKBIP is a novel EMT-related biomarker and predicts poor survival in glioma

2021 ◽  
Vol 12 (1) ◽  
pp. 009-019
Author(s):  
Ying Yang ◽  
Jin Wang ◽  
Shihai Xu ◽  
Wen Lv ◽  
Fei Shi ◽  
...  
Keyword(s):  
Epithelial To Mesenchymal Transition ◽  
Molecular Subtype ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Transcriptional Level ◽  
Who Grade ◽  
Interacting Protein ◽  
Mesenchymal Transition ◽  
Gene Set ◽  
Substrate Adhesion

Abstract Background In cancer, kappa B-interacting protein (IKBIP) has rarely been reported. This study aimed at investigating its expression pattern and biological function in brain glioma at the transcriptional level. Methods We selected 301 glioma patients with microarray data from CGGA database and 697 glioma patients with RNAseq data from TCGA database. Transcriptional data and clinical data of 998 samples were analyzed. Statistical analysis and figure generating were performed with R language. Results We found that IKBIP expression showed positive correlation with WHO grade of glioma. IKBIP was increased in isocitrate dehydrogenase (IDH) wild type and mesenchymal molecular subtype of glioma. Gene ontology analysis demonstrated that IKBIP was profoundly associated with extracellular matrix organization, cell–substrate adhesion and response to wounding in both pan-glioma and glioblastoma. Subsequent gene set enrichment analysis revealed that IKBIP was particularly correlated with epithelial-to-mesenchymal transition (EMT). To further elucidate the relationship between IKBIP and EMT, we performed gene set variation analysis to screen the EMT-related signaling pathways and found that IKBIP expression was significantly associated with PI3K/AKT, hypoxia and TGF-β pathway. Moreover, IKBIP expression was found to be synergistic with key biomarkers of EMT, especially with N-cadherin, vimentin, snail, slug and TWIST1. Finally, higher IKBIP indicated significantly shorter survival for glioma patients. Conclusions IKBIP was associated with more aggressive phenotypes of gliomas. Furthermore, IKBIP was significantly involved in EMT and could serve as an independent prognosticator in glioma.

scholarly journals Proteomic analysis of the pulvinus, a heliotropic tissue, in Glycine max

2014 ◽  
Vol 5 (1) ◽  
Author(s):  
Hakme Lee ◽  
Wesley M. Garrett ◽  
Joseph Sullivan ◽  
Irwin Forseth ◽  
Savithiry S. Natarajan
Keyword(s):  
Mass Spectrometry ◽  
Gene Ontology ◽  
Gel Electrophoresis ◽  
Proton Transport ◽  
Enrichment Analysis ◽  
The Sun ◽  
Leaf Movement ◽  
Two Dimensional ◽  
Two Dimensional Gel Electrophoresis ◽  
Leguminous Plants

Certain plant species respond to light, dark, and other environmental factors by leaf movement. Leguminous plants both track and avoid the sun through turgor changes of the pulvinus tissue at the base of leaves. Mechanisms leading to pulvinar turgor flux, particularly knowledge of the proteins involved, are not well-known. In this study we used two-dimensional gel electrophoresis and liquid chromatography-tandom mass spectrometry to separate and identify the proteins located in the soybean pulvinus. A total of 183 spots were separated and 195 proteins from 165 spots were identified and functionally analyzed using single enrichment analysis for gene ontology terms. The most significant terms were related to proton transport. Comparison with guard cell proteomes revealed similar significant processes but a greater number of pulvinus proteins are required for comparable analysis. To our knowledge, this is a novel report on the analysis of proteins found in soybean pulvinus. These findings provide a better understanding of the proteins required for turgor change in the pulvinus.

scholarly journals A Systematic Framework for Drug Repositioning from Integrated Omics and Drug Phenotype Profiles Using Pathway-Drug Network

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Erkhembayar Jadamba ◽  
Miyoung Shin
Keyword(s):  
Breast Cancer ◽  
Drug Repositioning ◽  
Expression Profiles ◽  
A Priori ◽  
Gene Expression Profiles ◽  
Biological Data ◽  
Genomic Knowledge ◽  
High Level ◽  
Systematic Framework ◽  
Phenotype Expression

Drug repositioning offers new clinical indications for old drugs. Recently, many computational approaches have been developed to repurpose marketed drugs in human diseases by mining various of biological data including disease expression profiles, pathways, drug phenotype expression profiles, and chemical structure data. However, despite encouraging results, a comprehensive and efficient computational drug repositioning approach is needed that includes the high-level integration of available resources. In this study, we propose a systematic framework employing experimental genomic knowledge and pharmaceutical knowledge to reposition drugs for a specific disease. Specifically, we first obtain experimental genomic knowledge from disease gene expression profiles and pharmaceutical knowledge from drug phenotype expression profiles and construct a pathway-drug network representing a priori known associations between drugs and pathways. To discover promising candidates for drug repositioning, we initialize node labels for the pathway-drug network using identified disease pathways and known drugs associated with the phenotype of interest and perform network propagation in a semisupervised manner. To evaluate our method, we conducted some experiments to reposition 1309 drugs based on four different breast cancer datasets and verified the results of promising candidate drugs for breast cancer by a two-step validation procedure. Consequently, our experimental results showed that the proposed framework is quite useful approach to discover promising candidates for breast cancer treatment.

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