scholarly journals Bioinformatic Analysis of Neuroimmune Mechanism of Neuropathic Pain

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Hao Yu ◽  
Yang Liu ◽  
Chao Li ◽  
Jianhao Wang ◽  
Bo Yu ◽  
...  
Keyword(s):  
Neuropathic Pain ◽  
Inflammatory Responses ◽  
Interaction Network ◽  
Gene Interaction ◽  
Bioinformatic Analysis ◽  
Venn Diagram ◽  
Hub Genes ◽  
Gene Interaction Network ◽  
Pathway Enrichment ◽  
Protein Protein Interaction

Background. Neuropathic pain (NP) is a devastating complication following nerve injury, and it can be alleviated by regulating neuroimmune direction. We aimed to explore the neuroimmune mechanism and identify some new diagnostic or therapeutic targets for NP treatment via bioinformatic analysis. Methods. The microarray GSE18803 was downloaded and analyzed using R. The Venn diagram was drawn to find neuroimmune-related differentially expressed genes (DEGs) in neuropathic pain. Gene Ontology (GO), pathway enrichment, and protein-protein interaction (PPI) network were used to analyze DEGs, respectively. Besides, the identified hub genes were submitted to the DGIdb database to find relevant therapeutic drugs. Results. A total of 91 neuroimmune-related DEGs were identified. The results of GO and pathway enrichment analyses were closely related to immune and inflammatory responses. PPI analysis showed two important modules and 8 hub genes: PTPRC, CD68, CTSS, RAC2, LAPTM5, FCGR3A, CD53, and HCK. The drug-hub gene interaction network was constructed by Cytoscape, and it included 24 candidate drugs and 3 hub genes. Conclusion. The present study helps us better understand the neuroimmune mechanism of neuropathic pain and provides some novel insights on NP treatment, such as modulation of microglia polarization and targeting bone resorption. Besides, CD68, CTSS, LAPTM5, FCGR3A, and CD53 may be used as early diagnostic biomarkers and the gene HCK can be a therapeutic target.

scholarly journals Hub Genes Related to the Pathogenesis and Progression of Colorectal Cancer and Adenoma by Integrative Bioinformatics Approaches

2021 ◽  
Author(s):  
Yuxuan HUANG ◽  
Ge CUI
Keyword(s):  
Colorectal Cancer ◽  
Cell Cycle ◽  
Differentially Expressed Genes ◽  
Protein Interaction ◽  
Biological Networks ◽  
Interaction Network ◽  
Differentially Expressed ◽  
Venn Diagram ◽  
Hub Genes ◽  
Protein Protein Interaction

Abstract Aims: To utilize the bioinformatics to analyze the differentially expressed genes (DEGs), interaction proteins, perform gene enrichment analysis, protein-protein interaction network (PPI) and map the hub genes between colorectal cancer(CRC) and colorectal adenocarcinomas(CA).Methods: We analyzed a microarray dataset (GSE32323 and GSE4183) from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in tumor tissues and non-cancerous tissues were identified using the dplyr and Venn diagram packages of the R Studio software. Functional annotation of the DEGs was performed using the Gene Ontology (GO) website. Pathway enrichment (KEGG) used the WebGestalt to analyze the data and R Studio to generate the graph. We constructed a protein–protein interaction (PPI) network of DEGs using STRING and Cytoscape software was used for visualization. Survival analysis of the hub genes and was performed using the online platform GEPIA to determine the prognostic value of the expression of hub genes in cell lines from CRC patients. The expression of molecules with prognostic values was validated on the UALCAN database. The expression of hub genes was examined using the Human Protein Atlas. Results: Applying the GEO2R analysis and R studio, we identified a total of 471 upregulated and 278 downregulated DEGs. By using the online database WebGestalt, we identified the most relevant biological networks involving DEGs with statistically significant differences in expression were mainly associated with biological processes involved in the cell proliferation, cell cycle transition, cell homeostasis and indicated the role of each DEGs in cell cycle regulation pathways. We found 10 hub genes with prognostic values were overexpressed in the CRC and CA samples.Conclusion: we found out ten hub genes and three core genes closely associated with the pathogenesis and prognosis of CRC and CA, which is of great significance for colorectal tumor early detection and prognosis evaluation.

scholarly journals Screen Key Genes Associated with Distraction-Induced Osteogenesis of Stem Cells Using Bioinformatics Methods

2021 ◽  
Vol 22 (12) ◽  
pp. 6505
Author(s):  
Jishizhan Chen ◽  
Jia Hua ◽  
Wenhui Song
Keyword(s):  
Stem Cells ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Negative Control ◽  
Gene Set Enrichment Analysis ◽  
Venn Diagram ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Two Samples ◽  
Key Genes

Applying mesenchymal stem cells (MSCs), together with the distraction osteogenesis (DO) process, displayed enhanced bone quality and shorter treatment periods. The DO guides the differentiation of MSCs by providing mechanical clues. However, the underlying key genes and pathways are largely unknown. The aim of this study was to screen and identify hub genes involved in distraction-induced osteogenesis of MSCs and potential molecular mechanisms. Material and Methods: The datasets were downloaded from the ArrayExpress database. Three samples of negative control and two samples subjected to 5% cyclic sinusoidal distraction at 0.25 Hz for 6 h were selected for screening differentially expressed genes (DEGs) and then analysed via bioinformatics methods. The Gene Ontology (GO) terms and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment were investigated. The protein–protein interaction (PPI) network was visualised through the Cytoscape software. Gene set enrichment analysis (GSEA) was conducted to verify the enrichment of a self-defined osteogenic gene sets collection and identify osteogenic hub genes. Results: Three hub genes (IL6, MMP2, and EP300) that were highly associated with distraction-induced osteogenesis of MSCs were identified via the Venn diagram. These hub genes could provide a new understanding of distraction-induced osteogenic differentiation of MSCs and serve as potential gene targets for optimising DO via targeted therapies.

scholarly journals Hyaluronan-mediated motility receptor expression functions as a prognostic biomarker in uterine carcinosarcoma based on bioinformatics analysis

2021 ◽  
Vol 49 (6) ◽  
pp. 030006052110210
Author(s):  
Hui Sun ◽  
Li Ma ◽  
Jie Chen
Keyword(s):  
Interaction Network ◽  
Maximal Clique ◽  
Receptor Expression ◽  
The Cancer Genome Atlas ◽  
Uterine Carcinosarcoma ◽  
Sequencing Data ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Normal Tissues ◽  
Potential Biomarker

Objective Uterine carcinosarcoma (UCS) is a rare, aggressive tumour with a high metastasis rate and poor prognosis. This study aimed to explore potential key genes associated with the prognosis of UCS. Methods Transcriptional expression data were downloaded from the Gene Expression Profiling Interactive Analysis database and differentially expressed genes (DEGs) were subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses using Metascape. A protein–protein interaction network was constructed using the STRING website and Cytoscape software, and the top 30 genes obtained through the Maximal Clique Centrality algorithm were selected as hub genes. These hub genes were validated by clinicopathological and sequencing data for 56 patients with UCS from The Cancer Genome Atlas database. Results A total of 1894 DEGs were identified, and the top 30 genes were considered as hub genes. Hyaluronan-mediated motility receptor (HMMR) expression was significantly higher in UCS tissues compared with normal tissues, and elevated expression of HMMR was identified as an independent prognostic factor for shorter survival in patients with UCS. Conclusions These results suggest that HMMR may be a potential biomarker for predicting the prognosis of patients with UCS.

scholarly journals Differential Expression of mRNAs in Peripheral Blood Related to Prodrome and Progression of Alzheimer’s Disease

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Weishuang Xue ◽  
Jinwei Li ◽  
Kailei Fu ◽  
Weiyu Teng
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Peripheral Blood ◽  
Bioinformatics Analysis ◽  
Gene Expression Omnibus ◽  
Venn Diagram ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ppi Networks

Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease that affects the quality of life of elderly individuals, while the pathogenesis of AD is still unclear. Based on the bioinformatics analysis of differentially expressed genes (DEGs) in peripheral blood samples, we investigated genes related to mild cognitive impairment (MCI), AD, and late-stage AD that might be used for predicting the conversions. Methods. We obtained the DEGs in MCI, AD, and advanced AD patients from the Gene Expression Omnibus (GEO) database. A Venn diagram was used to identify the intersecting genes. Gene Ontology (GO) and Kyoto Gene and Genomic Encyclopedia (KEGG) were used to analyze the functions and pathways of the intersecting genes. Protein-protein interaction (PPI) networks were constructed to visualize the network of the proteins coded by the related genes. Hub genes were selected based on the PPI network. Results. Bioinformatics analysis indicated that there were 61 DEGs in both the MCI and AD groups and 27 the same DEGs among the three groups. Using GO and KEGG analyses, we found that these genes were related to the function of mitochondria and ribosome. Hub genes were determined by bioinformatics software based on the PPI network. Conclusions. Mitochondrial and ribosomal dysfunction in peripheral blood may be early signs in AD patients and related to the disease progression. The identified hub genes may provide the possibility for predicting AD progression or be the possible targets for treatments.

scholarly journals Identification of Differentially Expressed Genes and associated pathways common to Eyelid and Non-Ocular Basal Cell Carcinoma to understand the Molecular Biology of BCC

2021 ◽  
Author(s):  
Perumal Jayaraj ◽  
Seema Sen ◽  
Pranjal Vats ◽  
Shefali Dahiya ◽  
Vanshika Mohindroo
Keyword(s):  
Basal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Basal Cell ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Malignant Neoplasms ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Pathway Enrichment

Background: Eyelid BCC accounts for more than 90% of Eyelid malignant neoplasms. Various aberrant signalling pathways and genes in Non-Ocular BCC have been found whereas Eyelid bcc remains elusive. Objective: This study aims to find the common DEGs of Eyelid and Non-Ocular BCC using bioinformatic analysis and text mining to gain more insights into the molecular aspects common to both BCC non-ocular and Eyelid BCC and to identify common potential prognostic markers. Material and method: The Gene Expression profiles of Eyelid BCC (GSE103439) and Non-Ocular BCC (GSE53462) were obtained from the NCBI GEO database followed by identification of common DEGs. Protein-Protein interaction and Pathway Enrichment analysis of these screened genes was done using bioinformatic tools like STRING, Cytoscape and BiNGO, DAVID, KEGG respectively. Results: A total of 181 genes were found common in both datasets. A PPI network was formed for the screened genes and 20 HUB genes were sorted which included CTNNB1, MAPK14, BTRC, EGFR, ADAM17. Pathway enrichment of HUB genes showed that they were dysregulated in carcinogenic and apoptotic pathways that seem to play a role in the progression of both the BCC. Conclusion: The result and findings of bioinformatic analysis highlighted the molecular pathways and genes enriched in both Eyelid BCC as well as Non- Ocular BCC. The identified pathways should be studied further to recognise common molecular events that would lead to the progression of BCC. This may provide a window to explore the prognostic and therapeutic strategies common to both BCC. Keywords: Basal cell carcinoma (BCC), Cancer, Microarray, Ophthalmology, Tumour marker

scholarly journals Community Detection in Large-Scale Bipartite Biological Networks

2021 ◽  
Vol 12 ◽  
Author(s):  
Genís Calderer ◽  
Marieke L. Kuijjer
Keyword(s):  
Biological Networks ◽  
Large Scale ◽  
Interaction Network ◽  
Gene Interaction ◽  
Community Structures ◽  
Gene Interaction Network ◽  
Structure Detection ◽  
Wide Range ◽  
Disease Associations ◽  
Or Gene

Networks are useful tools to represent and analyze interactions on a large, or genome-wide scale and have therefore been widely used in biology. Many biological networks—such as those that represent regulatory interactions, drug-gene, or gene-disease associations—are of a bipartite nature, meaning they consist of two different types of nodes, with connections only forming between the different node sets. Analysis of such networks requires methodologies that are specifically designed to handle their bipartite nature. Community structure detection is a method used to identify clusters of nodes in a network. This approach is especially helpful in large-scale biological network analysis, as it can find structure in networks that often resemble a “hairball” of interactions in visualizations. Often, the communities identified in biological networks are enriched for specific biological processes and thus allow one to assign drugs, regulatory molecules, or diseases to such processes. In addition, comparison of community structures between different biological conditions can help to identify how network rewiring may lead to tissue development or disease, for example. In this mini review, we give a theoretical basis of different methods that can be applied to detect communities in bipartite biological networks. We introduce and discuss different scores that can be used to assess the quality of these community structures. We then apply a wide range of methods to a drug-gene interaction network to highlight the strengths and weaknesses of these methods in their application to large-scale, bipartite biological networks.

scholarly journals Venn-diaNet : venn diagram based network propagation analysis framework for comparing multiple biological experiments

2019 ◽  
Vol 20 (S23) ◽  
Author(s):  
Benjamin Hur ◽  
Dongwon Kang ◽  
Sangseon Lee ◽  
Ji Hwan Moon ◽  
Gung Lee ◽  
...  
Keyword(s):  
Interaction Network ◽  
Venn Diagram ◽  
Analysis Framework ◽  
Seed Selection ◽  
Systematic Analysis ◽  
Main Research ◽  
Protein Protein Interaction ◽  
Propagation Analysis ◽  
Network Propagation ◽  
Main Research Topic

Abstract Background The main research topic in this paper is how to compare multiple biological experiments using transcriptome data, where each experiment is measured and designed to compare control and treated samples. Comparison of multiple biological experiments is usually performed in terms of the number of DEGs in an arbitrary combination of biological experiments. This process is usually facilitated with Venn diagram but there are several issues when Venn diagram is used to compare and analyze multiple experiments in terms of DEGs. First, current Venn diagram tools do not provide systematic analysis to prioritize genes. Because that current tools generally do not fully focus to prioritize genes, genes that are located in the segments in the Venn diagram (especially, intersection) is usually difficult to rank. Second, elucidating the phenotypic difference only with the lists of DEGs and expression values is challenging when the experimental designs have the combination of treatments. Experiment designs that aim to find the synergistic effect of the combination of treatments are very difficult to find without an informative system. Results We introduce Venn-diaNet, a Venn diagram based analysis framework that uses network propagation upon protein-protein interaction network to prioritizes genes from experiments that have multiple DEG lists. We suggest that the two issues can be effectively handled by ranking or prioritizing genes with segments of a Venn diagram. The user can easily compare multiple DEG lists with gene rankings, which is easy to understand and also can be coupled with additional analysis for their purposes. Our system provides a web-based interface to select seed genes in any of areas in a Venn diagram and then perform network propagation analysis to measure the influence of the selected seed genes in terms of ranked list of DEGs. Conclusions We suggest that our system can logically guide to select seed genes without additional prior knowledge that makes us free from the seed selection of network propagation issues. We showed that Venn-diaNet can reproduce the research findings reported in the original papers that have experiments that compare two, three and eight experiments. Venn-diaNet is freely available at: http://biohealth.snu.ac.kr/software/venndianet

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