scholarly journals Juxtapose: a gene-embedding approach for comparing co-expression networks

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Katie Ovens ◽  
Farhad Maleki ◽  
B. Frank Eames ◽  
Ian McQuillan
Keyword(s):  
Language Processing ◽  
Complex Structure ◽  
Similarity Measures ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Biological Evaluation ◽  
Gene Set Enrichment Analysis ◽  
Biological Information ◽  
Post Hoc ◽  
Network Comparisons

Abstract Background Gene co-expression networks (GCNs) are not easily comparable due to their complex structure. In this paper, we propose a tool, Juxtapose, together with similarity measures that can be utilized for comparative transcriptomics between a set of organisms. While we focus on its application to comparing co-expression networks across species in evolutionary studies, Juxtapose is also generalizable to co-expression network comparisons across tissues or conditions within the same species. Methods A word embedding strategy commonly used in natural language processing was utilized in order to generate gene embeddings based on walks made throughout the GCNs. Juxtapose was evaluated based on its ability to embed the nodes of synthetic structures in the networks consistently while also generating biologically informative results. Evaluation of the techniques proposed in this research utilized RNA-seq datasets from GTEx, a multi-species experiment of prefrontal cortex samples from the Gene Expression Omnibus, as well as synthesized datasets. Biological evaluation was performed using gene set enrichment analysis and known gene relationships in literature. Results We show that Juxtapose is capable of globally aligning synthesized networks as well as identifying areas that are conserved in real gene co-expression networks without reliance on external biological information. Furthermore, output from a matching algorithm that uses cosine distance between GCN embeddings is shown to be an informative measure of similarity that reflects the amount of topological similarity between networks. Conclusions Juxtapose can be used to align GCNs without relying on known biological similarities and enables post-hoc analyses using biological parameters, such as orthology of genes, or conserved or variable pathways. Availability A development version of the software used in this paper is available at https://github.com/klovens/juxtapose

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.

Mind Bomb 1 Promotes Pancreatic Cancer Proliferation by Activating β-Catenin Signaling

2020 ◽  
Vol 20 (12) ◽  
pp. 7276-7282
Author(s):  
Xiao Fu ◽  
Neng Tang ◽  
Weiqi Xie ◽  
Liang Mao ◽  
Yudong Qiu
Keyword(s):  
Pancreatic Cancer ◽  
Cancer Metastasis ◽  
Mrna Level ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Pancreatic Tumors ◽  
Mrna Levels ◽  
Cancer Proliferation

Mind bomb 1 (MIB1), an E3 ligase, plays a vital role in chemo-resistance and cancer metastasis. According to The Cancer Genome Atlas (TCGA), MIB1 gene is preferentially amplified in pancreatic cancer. Copy number alterations in MIB1 gene are associated with worse survival. Gene Expression Omnibus (GEO) also showed that pancreatic cancer with high mRNA level of MIB1 tend to be more resistant to gemcitabine and higher mRNA levels of MIB1 are found in pancreatic tumors compared with adjacent normal tissues. MIB1 knockdown (KD) in Panc-1 and HPAF2 cell lines significantly inhibit proliferation and colony formation of pancreatic cancer. The gene set enrichment analysis (GSEA) has also showed that β-catenin is the downstream of MIB1. Western blot analysis showed that total and active β-catenin levels are decreased in MIB1 KD cells. β-catenin inhibitor also inhibits proliferation of Panc-1 and HPAF2 cells. We in this study implanted HPAF2 scramble and MIB1 KD cells orthotopically in athymic nude mice. Gemcitabine was used to treat the mice. Results revealed that after MIB1 KD HPAF2 cells were more sensitive to gemcitabine. In conclusion, we demonstrated that MIB1 promotes pancreatic cancer proliferation through activating β-catenin signaling. MIB1 may thus be a therapeutic target in pancreatic cancer therapy.

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

2019 ◽  
Author(s):  
Aaron Ayllon-Benitez ◽  
Romain Bourqui ◽  
Patricia Thébaut ◽  
Fleur Mougin
Keyword(s):  
A Priori ◽  
Similarity Measures ◽  
Enrichment Analysis ◽  
Biological Information ◽  
New Approach ◽  
Gene Set ◽  
Multi Scale ◽  
Sequencing Technologies ◽  
Gene Sets ◽  
Limited Coverage

AbstractThe revolution in new sequencing technologies, by strongly improving the production of omics data, is greatly leading to new understandings of the relations between genotype and phenotype. To interpret and analyze these massive 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 the gene set.To address these limitations, we developed GSAn, a novel gene set annotation Web server that uses semantic similarity measures to reduce a priori Gene Ontology annotation terms. The originality of this new 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. GSAn is available at: https://gsan.labri.fr.

scholarly journals Bioinformatic Identification of Hub Genes and Biological Pathways for Sepsis-Associated Acute Lung Injury

2021 ◽  
Author(s):  
Chao Zhang ◽  
Feng Xu ◽  
Fang Fang
Keyword(s):  
Gene Expression ◽  
Acute Lung Injury ◽  
Lung Injury ◽  
Function Analysis ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Biological Pathways ◽  
Gene Set Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes

Abstract Background: Sepsis-associated acute lung injury (ALI) is a potentially lethal complication associated with a poor prognosis and high mortality worldwide, especially in the outbreak of COVID-19. However, the fundamental mechanisms of this complication were still not fully elucidated. Thus, we conducted this study to identify hub genes and biological pathways of sepsis-associated ALI, mainly focus on two pathways of LPS and HMGB1. Methods: Gene expression profile GSE3037 were downloaded from Gene Expression Omnibus (GEO) database, including 8 patients with sepsis-induced acute lung injury, with 8 unstimulated blood neutrophils, 8 LPS- induced neutrophils and 8 HMGB1-induced neutrophils. Differentially expressed genes (DEGs) identifications, Gene Ontology (GO) function analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis, Gene Set Enrichment Analysis (GSEA) and protein-protein interaction (PPI) network constructions were performed to obtain hub genes and relevant biological pathways.Results: We identified 534 and 317 DEGs for LPS- and HMGB1-induced ALI, respectively. The biological pathways involved in LPS- and HMGB1-induced ALI were also identified accordingly. By PPI network analysis, we found that ten hub genes for LPS-induced ALI (CXCL8, TNF, IL6, IL1B, ICAM1, CXCL1, CXCL2, IL1A, IL1RN and CXCL3) and another ten hub genes for HMGB1-induced ALI (CCL20, CXCL2, CXCL1, CCL4, CXCL3, CXCL9, CCL21, CXCR6, KNG1 and SST). Furthermore, by combining analysis, the results revealed that genes of TNF, CCL20, IL1B, NFKBIA, CCL4, PTGS2, TNFAIP3, CXCL2, CXCL1 and CXCL3 were potential biomarkers for sepsis-associated ALI. Conclusions: Our study revealed that ten hub genes associated with sepsis-induced ALI were TNF, CCL20, IL1B, NFKBIA, CCL4, PTGS2, TNFAIP3, CXCL2, CXCL1 and CXCL3, which may serve as genetic biomarkers and be further verified in prospective experimental trials.

scholarly journals Identification of key genes involved in type 2 diabetic islet dysfunction: a bioinformatics study

2019 ◽  
Vol 39 (5) ◽  
Author(s):  
Ming Zhong ◽  
Yilong Wu ◽  
Weijie Ou ◽  
Linjing Huang ◽  
Liyong Yang
Keyword(s):  
Type 2 Diabetes ◽  
Bioinformatics Analysis ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Atp Citrate Lyase ◽  
Hub Gene ◽  
Protein Protein Interaction ◽  
Search Tool

Abstract Aims: To identify the key differentially expressed genes (DEGs) in islet and investigate their potential pathway in the molecular process of type 2 diabetes. Methods: Gene Expression Omnibus (GEO) datasets (GSE20966, GSE25724, GSE38642) of type 2 diabetes patients and normal controls were downloaded from GEO database. DEGs were further assessed by enrichment analysis based on the Database for Annotation, Visualization and Integrated Discovery (DAVID) 6.8. Then, by using Search Tool for the Retrieval Interacting Genes (STRING) 10.0 and gene set enrichment analysis (GSEA), we identified hub gene and associated pathway. At last, we performed quantitative real-time PCR (qPCR) to validate the expression of hub gene. Results: Forty-five DEGs were co-expressed in the three datasets, most of which were down-regulated. DEGs are mostly involved in cell pathway, response to hormone and binding. In protein–protein interaction (PPI) network, we identified ATP-citrate lyase (ACLY) as hub gene. GSEA analysis suggests low expression of ACLY is enriched in glycine serine and threonine metabolism, drug metabolism cytochrome P450 (CYP) and NOD-like receptor (NLR) signaling pathway. qPCR showed the same expression trend of hub gene ACLY as in our bioinformatics analysis. Conclusion: Bioinformatics analysis revealed that ACLY and the pathways involved are possible target in the molecular mechanism of type 2 diabetes.

scholarly journals Integrating Literature-Based Knowledge Database and Expression Data to Explore Molecular Pathways Connecting PPARG and Myocardial Infarction

PPAR Research ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Rongyuan Cao ◽  
Yan Dong ◽  
Kamil Can Kural
Keyword(s):  
Gene Expression ◽  
Myocardial Infarction ◽  
Enrichment Analysis ◽  
Protective Role ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Peroxisome Proliferator ◽  
Peroxisome Proliferator Activated Receptor ◽  
Knowledge Database ◽  
Expression Activity

Peroxisome proliferator-activated receptor γ (PPARG) might play a protective role in the development of myocardial infarction (MI) with limited mechanisms identified. Genes associated with both PPARG and MI were extracted from Elsevier Pathway Studio to construct the initial network. The gene expression activity within the network was estimated through a mega-analysis with eight independent expression datasets derived from Gene Expression Omnibus (GEO) to build PPARG and MI connecting pathways. After that, gene set enrichment analysis (GSEA) was conducted to explore the functional profile of the genes involved in the PPARG-driven network. PPARG demonstrated a significantly low expression in MI patients (LFC=−0.52; p<1.84e−9). Consequently, PPARG could indicatively be promoting three MI inhibitors (e.g., SOD1, CAV1, and POU5F1) and three MI-downregulated markers (e.g., ALB, ACADM, and ADIPOR2), which were deactivated in MI cases (p<0.05), and inhibit two MI-upregulated markers (RELA and MYD88), which showed increased expression levels in MI cases (p=0.0077 and 0.047, respectively). These eight genes were mainly enriched in nutrient- and cell metabolic-related pathways and functionally linked by GSEA and PPCN. Our results suggest that PPARG could protect the heart against both the development and progress of MI through the regulation of nutrient- and metabolic-related pathways.

scholarly journals Overexpression of SP6 Correlates with Osteosarcoma Metastasis and Poor Prognosis

2020 ◽  
pp. 1-7
Author(s):  
Dongmei Guo ◽  
Chunpu Li ◽  
Sicheng Wang ◽  
Lili Zhao ◽  
Dongmei Guo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cox Regression ◽  
Excision Repair ◽  
Enrichment Analysis ◽  
Analysis Data ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Roc Curve Analysis ◽  
Gene Set ◽  
Base Excision

Background: SP6 (Specificity protein 6) has been explored as a prospective biomarker in several cancers. In this research, the prognostic value of SP6 expression in osteosarcoma was predicted by bioinformatics analysis. Data were obtained from the Gene Expression Omnibus (GEO) database. Methods: Gene expression data and clinical materials were downloaded from the GSE21257 dataset. The mRNA expression of SP6 was compared between metastatic and non-metastatic tissues with the Wilcoxon rank-sum test, and the relationship between SP6 and clinicopathological characters was analysed using logistic regression. In addition, the correlation between SP6 and survival rate was assessed using KaplanMeier and Cox regression. Moreover, receiver operating characteristic (ROC) curve analysis was conducted to determine the prognostic merit of SP6 for osteosarcoma. The biological functions of SP6 were annotated and evaluated through gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA). Results: SP6 was significantly highly expressed in metastatic osteosarcoma tissues (p = 0.002). High SP6 expression showed a positive correlation with Huvos grade (OR = 6.60 for I vs. II, p = 0.028). The overall survival (OS) of the patients with high SP6 expression was significantly poorer than the low SP6 expression group (p = 0.027). The multivariate analysis revealed that SP6 expression (p = 0.002, HR = 15.40 (95% CI [2.84–83.44])) was independently correlated with OS. GSEA and GSVA showed that "spliceosome" and "base excision repair" were significantly upregulated in the high expression group of SP6. Conclusion: SP6 may serve an independent prognostic biomarker in osteosarcoma.

scholarly journals Identification of biomarkers and pathways in hypertensive nephropathy based on the ceRNA regulatory network

2019 ◽  
Author(s):  
lei kang ◽  
Zhen Wang ◽  
Zhongjie Liu ◽  
Yingxia Liu
Keyword(s):  
Signaling Pathway ◽  
Insulin Signaling ◽  
Regulatory Network ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Insulin Signaling Pathway ◽  
Gene Set Enrichment Analysis ◽  
Chronic Hypertension ◽  
Hypertensive Nephropathy ◽  
Kegg Pathways

Abstract Background Hypertensive nephropathy (HTN) is a kind of renal injury caused by chronic hypertension, which seriously affect people’s life. The purpose of this study was to identify the potential biomarkers of HTN and understand its possible mechanisms.Methods The dataset numbered GSE28260 related to hypertensive and normotensive was downloaded from NCBI Gene Expression Omnibus. Then, the differentially expressed RNAs (DERs) were screened using R limma package, and functional analyses of DE-mRNA were performed by DAVID. Afterwards, a ceRNA network was established and KEGG pathway was analyzed based on the Gene Set Enrichment Analysis (GSEA) database. Finally, a ceRNA regulatory network directly associated with HTN was proposed.Results A total of 947 DERs were identified, including 900 DE-mRNAs, 20 DE-lncRNAs and 27 DE-miRNAs. Based on these DE-mRNAs, they were involved in biological processes such as fatty acid beta-oxidation, IRE1-mediated unfolded protein response, and transmembrane transport, and many KEGG pathways like glycine, serine and threonine metabolism, carbon metabolism. Subsequently, lncRNAs KCTD21-AS1, LINC00470 and SNHG14 were found to be hub nodes in the ceRNA regulatory network. KEGG analysis showed that insulin signaling pathway, glycine, serine and threonine metabolism, pathways in cancer, lysosome, and apoptosis was associated with hypertensive. Finally, insulin signaling pathway was screened to directly associate with HTN and was regulated by mRNAs PPP1R3C, PPKAR2B and AKT3, miRNA has-miR-107, and lncRNAs SNHG14, TUG1, ZNF252P-AS1 and MIR503HG.Conclusions Insulin signaling pathway was directly associated with HTN, and miRNA has-miR-107 and lncRNAs SNHG14, TUG1, ZNF252P-AS1 and MIR503HG were the biomarkers of HTN. These results would improve our understanding of the occurrence and development of HTN.

scholarly journals Identification of Infertility-Associated Topologically Important Genes Using Weighted Co-expression Network Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Jingni Wu ◽  
Xiaomeng Xia ◽  
Ye Hu ◽  
Xiaoling Fang ◽  
Sandra Orsulic
Keyword(s):  
Network Analysis ◽  
Topological Analysis ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Messenger Rnas ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Highly Correlated

Endometriosis has been associated with a high risk of infertility. However, the underlying molecular mechanism of infertility in endometriosis remains poorly understood. In our study, we aimed to discover topologically important genes related to infertility in endometriosis, based on the structure network mining. We used microarray data from the Gene Expression Omnibus (GEO) database to construct a weighted gene co-expression network for fertile and infertile women with endometriosis and to identify gene modules highly correlated with clinical features of infertility in endometriosis. Additionally, the protein–protein interaction network analysis was used to identify the potential 20 hub messenger RNAs (mRNAs) while the network topological analysis was used to identify nine candidate long non-coding RNAs (lncRNAs). Functional annotations of clinically significant modules and lncRNAs revealed that hub genes might be involved in infertility in endometriosis by regulating G protein-coupled receptor signaling (GPCR) activity. Gene Set Enrichment Analysis showed that the phospholipase C-activating GPCR signaling pathway is correlated with infertility in patients with endometriosis. Taken together, our analysis has identified 29 hub genes which might lead to infertility in endometriosis through the regulation of the GPCR network.

Identification of MEG8-miR378d-SOBP Axis as a Novel Regulatory Network and Associated With Immune Infiltrates in Ovarian Carcinoma by Integrated Bioinformatics Analysis

2020 ◽  
Author(s):  
Jian Lei ◽  
Zhen-Yu He ◽  
Jun Wang ◽  
Min Hu ◽  
Ping Zhou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Bioinformatics Analysis ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Human Protein Atlas

Abstract BackgroundTo investigate the potential molecular mechanism of ovarian cancer (OC) evolution and immunological correlation using the integrated bioinformatics analysis.MethodsData from the Gene Expression Omnibus (GEO) was used to gain differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis were completed by utilizing the Database for Annotation, Visualization, and Integrated Discovery (DAVID). After multiple validation via The Cancer Genome Atlas (TCGA), Gene Expression Profiling Interactive Analysis 2 (GEPIA 2), the Human Protein Atlas (HPA) and Kaplan-Meier (KM) plotter, immune logical relationships of the key gene SOBP were evaluated based on Tumor Immune Estimation Resource (TIMER), and Gene Set Enrichment Analysis (GSEA) software. Finally, the lncRNAs-miRNAs-mRNAs sub-network was predicted by starBase, Targetscan, miRBD, and LncBase, individually. Correlation of expression and prognosis for mRNAs, miRNAs and lncRNAs were confirmed by TCGA, GEPIA 2, starBase, and KM.ResultsA total of 192 shared DEGs were discovered from the four data sets, including 125 upregulated and 67 downregulated genes. Functional enrichment analysis presented that they were mainly enriched in cartilage development, pathway in PI3K-Akt signaling pathway. Lower expression of SOBP was the independent prognostic factor for inferior prognosis in OC patients. Intriguingly, downregulated SOBP enhanced the infiltration levels of B cells, CD8+ T cells, Macrophage, Neutrophil and Dendritic cells. GSEA also disclosed low SOBP showed significantly association with the activation of various immune-related pathways. Finally, we firstly reported that MEG8-miR378d-SOBP axis was linked to development and prognosis of ovarian cancer through regulating cytokines pathway.Conclusions Our study establishes a novel MEG8-miR378d-SOBP axis in the development and prognosis of OC, and the triple sub-network probably affects the progression of ovarian tumor by regulating cytokines pathway.

Sign in / Sign up

Export Citation Format

Share Document