scholarly journals Identification of Potential Therapeutic Targets For Rheumatoid Arthritis By Bioinformatics

2021 ◽  
Author(s):  
Yi-kuan Du ◽  
Erbai Ye ◽  
Xiaoling Xiao ◽  
Senpeng Zhang ◽  
XinNi Ye ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Transcription Factors ◽  
Signaling Pathway ◽  
Programming Language ◽  
Enrichment Analysis ◽  
R Programming Language ◽  
Key Genes ◽  
R Programming ◽  
Geo Database ◽  
Differential Genes

Abstract Objective: Based on GEO database, we performed bioinformatics analysis on rheumatoid arthritis (RA)-related gene chips to obtain key genes and signaling pathways of RA, understand the molecular mechanism of RA occurrence and development, and provide candidate targets for the diagnosis and treatment of RA.Methods: The chip GSE77298 related to rheumatoid arthritis in GEO database was retrieved, and the R Programming Language analyzed the differential genes. Subsequently, the differential gene protein-protein interaction (PPI) relationship was constructed. The hub gene was screened, and the DAVID database was used for GO enrichment analysis and KEGG pathway analysis of key differential genes. The miRNAs were then subjected to target gene prediction, and then a miRNA-mRNA visualization network map was constructed using Cytoscape. Finally, transcription factors were predicted by the AnimalTFDB database.Results: ①The chip with the serial number of GSE77298 was retrieved from GEO database, and 1539 differential genes were screened out using the R Programming language analysis, including 1156 up-regulated genes and 383 down-regulated genes. ②By DAVID online functional enrichment analysis of differential genes, it was shown that the signaling pathways were mainly rheumatoid arthritis, Staphylococcus aureus infection, chemokine signaling pathway, viral myocarditis, cytokine-cytokine receptor interaction, etc. ③PPI was constructed through String database. Its hub genes were CXCL12, CD44 and CDH2. The top 10 key differential genes in Degree were CXCL8, PTPRC, MMP9, TLR2, FN1, ITGB2, CXCL1, CCL5, CXCR4 and CXCL10. ④Ten important miRNAs such as hsa-miR-30a-3p, hsa-miR-34a-5p, hsa-miR-30d-3p were predicted. ⑤Transcription factors such as GTF3C2, GLYR1, TRIM24, YY1 were predicted.Conclusion: PI3K-AKT signaling pathway and many other pathways are involved in the occurrence and development of RA, and CXCL8, SOCS3, and TLR2 genes may be the key genes in RA. Ten important miRNAs such as has-miR-340-5p may participate in the pathogenesis of RA. Many transcription factors such as YY1 may be involved in RA's disease process, which will provide directions for further research on RA diagnosis and treatment targets of RA.

scholarly journals Disorder Genes Regulate the Progression of Ischemic Stroke through the NF-κB Signaling Pathway

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Wei Wei ◽  
Wenqiang Xin ◽  
Yufeng Tang ◽  
Zhonglun Chen ◽  
Yue Heng ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Signaling Pathway ◽  
Noncoding Rna ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Differential Gene ◽  
Coexpression Network Analysis ◽  
Geo Database ◽  
Differential Genes

Stroke is an acute cerebrovascular disease, including ischemic and hemorrhagic stroke. Stroke is the second leading cause of death after ischemic heart disease, which accounts for 9% of the global death toll. To explore the molecular mechanisms of the effects of the dysregulated factors, in the GEO database, we obtained transcriptome data from 24 h/72 h of mice with ischemic stroke and 24 h/72 h of normal mice. We then performed differential gene analysis, coexpression analysis, enrichment analysis, and regulator prediction bioinformatics analysis to identify the potential genes. We made a comparison between the ischemic stroke 72 h and the ischemic stroke for 24 h, and 5103 differential genes were obtained ( p < 0.05 ). Four functional barrier modules were obtained by weighted gene coexpression network analysis. The critical genes of each module were ASTL, Zfp472, Fmr1 gene, and Nap1l1. The results of the enrichment analysis showed ncRNA metabolism, microRNAs in cancer, and biosynthesis of amino acids. These three functions and pathways have the most considerable count value. The regulators of the regulatory dysfunction module were predicted by pivotal analysis of TF and noncoding RNA, and critical regulators including NFKB1 (NF-κB1), NFKBIA, CTNNB1, and SP1 were obtained. Finally, the pivotal target gene found that CTNNB1, NFKB1, NFKBia, and Sp1 are involved in 18, 32, 2, and 60 target genes, respectively. Therefore, we believe that NFKB1 and Sp1 have a potential role in the progression of ischemic stroke. The NFKB signaling pathway promotes inflammatory cytokines and regulates the progression of ischemic stroke.

scholarly journals Immunological Analysis of Peripheral Artery Disease

2021 ◽  
Author(s):  
Wan-Xia Yang ◽  
Fang-Fang Wang ◽  
Fei-Fei Li ◽  
Jian-Qin Xie ◽  
Chong Shi ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Signaling Pathway ◽  
Protein Interaction ◽  
Enrichment Analysis ◽  
Arterial Disease ◽  
Receptor Interaction ◽  
Pathway Enrichment Analysis ◽  
R Software ◽  
Peripheral Arterial ◽  
Differential Genes

Abstract Purpose: To explore the pathogenesis of Peripheral arterial disease (PAD) and provide bioinformatics basis for the prevention and treatment of PAD.Methods: R software is used to analyze differentially expressed genes (DEGs) in PAD patient and control blood samples in GSE27034 and screen for immune differential genes, and then perform GO and KEGG pathway enrichment analysis for immune differential genes. The protein-protein interaction (PPI) network was constructed by using STRING database, and functional modules were analyzed using Cytoscape software. Coexpedia database was used to analyze the gene co-expression network of immune differential genes. Finally, combined with CIBERSORT database, immune cells were obtained by R software.Results: The 21 immune differential genes screened in PAD were mainly involved in TNF signaling pathway, IL-17 signaling pathway, cytokine-cytokine receptor interaction, viral protein interaction with cytokines andcytokine receptor signaling pathways, and rheumatoid arthritis. Compared with the normal group, neutrophils were higher in number in the PAD group, while macrophages M0 were significantly lower (P<0.05).Conclusions: TNF signaling pathway, IL-17 signaling pathway and rheumatoid arthritis are most closely related to the occurrence and development of PAD, and immune differential genes may be the key molecules of PAD, which provides a new idea for further exploring the pathogenesis of PAD.

scholarly journals Developing codes for validation of PM10, PM2.5, and O3 datasets using R programming language

2019 ◽  
Author(s):  
Ramin Nabizadeh ◽  
Mostafa Hadei
Keyword(s):  
Air Pollution ◽  
Programming Language ◽  
Assessment Method ◽  
Daily Maximum ◽  
Data Handling ◽  
R Programming Language ◽  
Wide Range ◽  
Us Epa ◽  
R Programming ◽  
Pm 10

Introduction: The wide range of studies on air pollution requires accurate and reliable datasets. However, due to many reasons, the measured concentra-tions may be incomplete or biased. The development of an easy-to-use and reproducible exposure assessment method is required for researchers. There-fore, in this article, we describe and present a series of codes written in R Programming Language for data handling, validating and averaging of PM10, PM2.5, and O3 datasets.   Findings: These codes can be used in any types of air pollution studies that seek for PM and ozone concentrations that are indicator of real concentra-tions. We used and combined criteria from several guidelines proposed by US EPA and APHEKOM project to obtain an acceptable methodology. Separate   .csv files for PM 10, PM 2.5 and O3 should be prepared as input file. After the file was imported to the R Programming software, first, negative and zero values of concentrations within all the dataset will be removed. Then, only monitors will be selected that have at least 75% of hourly concentrations. Then, 24-h averages and daily maximum of 8-h moving averages will be calculated for PM and ozone, respectively. For output, the codes create two different sets of data. One contains the hourly concentrations of the interest pollutant (PM10, PM2.5, or O3) in valid stations and their average at city level. Another is the   final 24-h averages of city for PM10 and PM2.5 or the final daily maximum 8-h averages of city for O3. Conclusion: These validated codes use a reliable and valid methodology, and eliminate the possibility of wrong or mistaken data handling and averaging. The use of these codes are free and without any limitation, only after the cita-tion to this article.

scholarly journals Spatial variation of physicochemical parameters in a constructed wetland for wastewater treatment: An example of the use of the R programming language

2021 ◽  
Vol 13 (1) ◽  
pp. 15
Author(s):  
Junior Pastor Pérez-Molina ◽  
Carola Scholz ◽  
Roy Pérez-Salazar ◽  
Carolina Alfaro-Chinchilla ◽  
Ana Abarca Méndez ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Spatial Variation ◽  
Water Flow ◽  
Programming Language ◽  
Constructed Wetland ◽  
Physicochemical Parameters ◽  
Preferential Flow ◽  
Oxygen Demand ◽  
R Programming Language ◽  
R Programming

Introduction: The implementation of wastewater treatment systems such as constructed wetlands has a growing interest in the last decade due to its low cost and high effectiveness in treating industrial and residential wastewater. Objective: To evaluate the spatial variation of physicochemical parameters in a constructed wetland system of sub-superficial flow of Pennisetum alopecuroides (Pennisetum) and a Control (unplanted). The purpose is to provide an analysis of spatial dynamic of physicochemical parameters using R programming language. Methods: Each of the cells (Pennisetum and Control) had 12 piezometers, organized in three columns and four rows with a separation distance of 3,25m and 4,35m, respectively. The turbidity, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total Kjeldahl nitrogen (TKN), ammoniacal nitrogen (N-NH4), organic nitrogen (N-org.) and phosphorous (P-PO4-3) were measured in water under in-flow and out-flow of both conditions Control and Pennisetum (n= 8). Additionally, the oxidation-reduction potential (ORP), dissolved oxygen (DO), conductivity, pH and water temperature, were measured (n= 167) in the piezometers. Results: No statistically significant differences between cells for TKN, N-NH4, conductivity, turbidity, BOD, and COD were found; but both Control and Pennisetum cells showed a significant reduction in these parameters (P<0,05). Overall, TKN and N-NH4 removal were from 65,8 to 84,1% and 67,5 to 90,8%, respectively; and decrease in turbidity, conductivity, BOD, and COD, were between 95,1-95,4%; 15-22,4%; 65,2-77,9% and 57,4-60,3% respectively. Both cells showed ORP increasing gradient along the water-flow direction, contrary to conductivity (p<0,05). However, OD, pH and temperature were inconsistent in the direction of the water flow in both cells. Conclusions: Pennisetum demonstrated pollutant removal efficiency, but presented results similar to the control cells, therefore, remains unclear if it is a superior option or not. Spatial variation analysis did not reflect any obstruction of flow along the CWs; but some preferential flow paths can be distinguished. An open-source repository of R was provided. 

scholarly journals Integrating Network Pharmacology with Molecular Docking to Unravel the Active Compounds and Potential Mechanism of Simiao Pill Treating Rheumatoid Arthritis

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Mengshi Tang ◽  
Xi Xie ◽  
Pengji Yi ◽  
Jin Kang ◽  
Jiafen Liao ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Molecular Docking ◽  
Signaling Pathway ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Binding Activity ◽  
New Combination ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Potential Mechanism

Objective. To explore the main components and unravel the potential mechanism of simiao pill (SM) on rheumatoid arthritis (RA) based on network pharmacological analysis and molecular docking. Methods. Related compounds were obtained from TCMSP and BATMAN-TCM database. Oral bioavailability and drug-likeness were then screened by using absorption, distribution, metabolism, and excretion (ADME) criteria. Additionally, target genes related to RA were acquired from GeneCards and OMIM database. Correlations about SM-RA, compounds-targets, and pathways-targets-compounds were visualized through Cytoscape 3.7.1. The protein-protein interaction (PPI) network was constructed by STRING. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed via R packages. Molecular docking analysis was constructed by the Molecular Operating Environment (MOE). Results. A total of 72 potential compounds and 77 associated targets of SM were identified. The compounds-targets network analysis indicated that the 6 compounds, including quercetin, kaempferol, baicalein, wogonin, beta-sitosterol, and eugenol, were linked to ≥10 target genes, and the 10 target genes (PTGS1, ESR1, AR, PGR, CHRM3, PPARG, CHRM2, BCL2, CASP3, and RELA) were core target genes in the network. Enrichment analysis indicated that PI3K-Akt, TNF, and IL-17 signaling pathway may be a critical signaling pathway in the network pharmacology. Molecular docking showed that quercetin, kaempferol, baicalein, and wogonin have good binding activity with IL6, VEGFA, EGFR, and NFKBIA targets. Conclusion. The integrative investigation based on bioinformatics/network topology strategy may elaborate on the multicomponent synergy mechanisms of SM against RA and provide the way out to develop new combination medicines for RA.

scholarly journals mRNAsi Index: Machine Learning in Mining Lung Adenocarcinoma Stem Cell Biomarkers

Genes ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 257 ◽  
Author(s):  
Yitong Zhang ◽  
Joseph Ta-Chien Tseng ◽  
I-Chia Lien ◽  
Fenglan Li ◽  
Wei Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cell ◽  
Lung Adenocarcinoma ◽  
Tumor Necrosis Factor Receptor ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Clinical Stages ◽  
Key Genes ◽  
Geo Database

Cancer stem cells (CSCs), characterized by self-renewal and unlimited proliferation, lead to therapeutic resistance in lung cancer. In this study, we aimed to investigate the expressions of stem cell-related genes in lung adenocarcinoma (LUAD). The stemness index based on mRNA expression (mRNAsi) was utilized to analyze LUAD cases in the Cancer Genome Atlas (TCGA). First, mRNAsi was analyzed with differential expressions, survival analysis, clinical stages, and gender in LUADs. Then, the weighted gene co-expression network analysis was performed to discover modules of stemness and key genes. The interplay among the key genes was explored at the transcription and protein levels. The enrichment analysis was performed to annotate the function and pathways of the key genes. The expression levels of key genes were validated in a pan-cancer scale. The pathological stage associated gene expression level and survival probability were also validated. The Gene Expression Omnibus (GEO) database was additionally used for validation. The mRNAsi was significantly upregulated in cancer cases. In general, the mRNAsi score increases according to clinical stages and differs in gender significantly. Lower mRNAsi groups had a better overall survival in major LUADs, within five years. The distinguished modules and key genes were selected according to the correlations to the mRNAsi. Thirteen key genes (CCNB1, BUB1, BUB1B, CDC20, PLK1, TTK, CDC45, ESPL1, CCNA2, MCM6, ORC1, MCM2, and CHEK1) were enriched from the cell cycle Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, relating to cell proliferation Gene Ontology (GO) terms, as well. Eight of the thirteen genes have been reported to be associated with the CSC characteristics. However, all of them have been previously ignored in LUADs. Their expression increased according to the pathological stages of LUAD, and these genes were clearly upregulated in pan-cancers. In the GEO database, only the tumor necrosis factor receptor associated factor-interacting protein (TRAIP) from the blue module was matched with the stemness microarray data. These key genes were found to have strong correlations as a whole, and could be used as therapeutic targets in the treatment of LUAD, by inhibiting the stemness features.

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