scholarly journals Screening and Bioinformatics Analysis of IgA Nephropathy Gene Based on GEO Databases

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Wang Qian ◽  
Wang Xiaoyi ◽  
Ye Zi
Keyword(s):  
Iga Nephropathy ◽  
Signaling Pathway ◽  
Molecular Mechanism ◽  
Biological Networks ◽  
Cellular Response ◽  
Bioinformatics Analysis ◽  
Enrichment Analysis ◽  
Mapk Signaling ◽  
Protein Protein Interaction ◽  
Novel Biomarkers

Purpose. To identify novel biomarkers of IgA nephropathy (IgAN) through bioinformatics analysis and elucidate the possible molecular mechanism. Methods. The GSE93798 and GSE73953 datasets containing microarray data from IgAN patients and healthy controls were downloaded from the GEO database and analyzed by the GEO2R web tool to obtain different expressed genes (DEGs). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, protein-protein interaction (PPI), and Biological Networks Gene Oncology tool (BiNGO) were then performed to elucidate the molecular mechanism of IgAN. Results. A total of 223 DEGs were identified, of which 21 were hub genes, and involved in inflammatory response, cellular response to lipopolysaccharide, transcription factor activity, extracellular exosome, TNF signaling pathway, and MAPK signaling pathway. Conclusions. TNF and MAPK pathways likely form the basis of IgAN progression, and JUN/JUNB, FOS, NR4A1/2, EGR1, and FOSL1/2 are novel prognostic biomarkers of IgAN.

scholarly journals Bioinformatics Analysis of the Molecular Mechanism and Potential Treatment Target of Ankylosing Spondylitis

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Fanyan Meng ◽  
Ningna Du ◽  
Daoming Xu ◽  
Li Kuai ◽  
Lanying Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ankylosing Spondylitis ◽  
Molecular Mechanism ◽  
Soft Tissues ◽  
Bioinformatics Analysis ◽  
Enrichment Analysis ◽  
R Package ◽  
Gene Expression Omnibus ◽  
Treatment Targets ◽  
Protein Protein Interaction

Ankylosing spondylitis (AS) is an autoimmune disease that mainly affects the spinal joints, sacroiliac joints, and adjacent soft tissues. We conducted bioinformatics analysis to explore the molecular mechanism related to AS pathogenesis and uncover novel potential molecular targets for the treatment of AS. The profiles of GSE25101, containing gene expression data extracted from the blood of 16 AS patients and 16 matched controls, were acquired from the Gene Expression Omnibus (GEO) database. The background correction and standardization were carried out utilizing the transcript per million (TPM) method. After analysis of AS patients and the normal groups, we identified 199 differentially expressed genes (DEGs) with upregulation and 121 DEGs with downregulation by the limma R package. The results of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) biological process enrichment analysis revealed that the DEGs with upregulation were mainly associated with spliceosome, ribosome, RNA-catabolic process, electron transport chain, etc. And the DEGs with downregulation primarily participated in T cell-associated pathways and processes. After analysis of the protein-protein interaction (PPI) network, our data revealed that the hub genes, comprising MRPL13, MRPL22, LSM3, COX7A2, COX7C, EP300, PTPRC, and CD4, could be the treatment targets in AS. Our data furnish new hints to uncover the features of AS and explore more promising treatment targets towards AS.

scholarly journals A Network Pharmacology-based Analysis of the Molecular Mechanism of Tanreqing in the Treatment of the Coronavirus Disease 2019 (COVID-19)

2020 ◽  
Author(s):  
Liuliu Yang ◽  
Minyong Wen ◽  
Wenjiang Zheng ◽  
Xiaohong Liu ◽  
yong wang
Keyword(s):  
Signaling Pathway ◽  
Molecular Mechanism ◽  
Chemical Constituents ◽  
Hypoxia Inducible Factor ◽  
Enrichment Analysis ◽  
Clinical Treatment ◽  
Laboratory Research ◽  
Network Pharmacology ◽  
Receptor Interaction ◽  
Protein Protein Interaction

Abstract Background: This paper discusses the molecular mechanism of Tanreqing (TRQ) in the treatment of the coronavirus disease 2019 (COVID-19) using the network pharmacology approach. Our study provides new ideas on the laboratory research and clinical treatment of the disease. Method: Information on the chemical constituents of TRQ and the genes targeted by the disease was collected. The common gene targets of the drug and the disease were input into the Search Tool for the Retrieval of Interacting Genes/Proteins（STRING）database to understand the interaction among target proteins. The protein–protein interaction (PPI) network and a network of the chemical constituents of TRQ and their targets were constructed using Cytoscape. Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using the R program and other relevant software packages. Results: Twenty-eight active chemical constituents and 365 gene targets were identified for TRQ. Out of these genes, 113 were also found to be involved in the pathogenesis of the disease. Enrichment analysis revealed the therapeutic role that TRQ could have played in the treatment of COVID-19, via the regulation of important pathways such as the renin–angiotension system, neuroactive ligand–receptor interaction, phospholipase D (PLD) signaling pathway, calcium signaling pathway, and the hypoxia-inducible factor 1 (HIF-1) signaling pathway. Conclusions: This study attempts to predict the molecular mechanism of TRQ in the treatment of COVID-19, and suggests TRQ intervention through multiple targets and pathways in processes including inflammatory response, immune regulation, and apoptosis during the treatment of the disease. This study indicates the potential rational application of TRQ in the clinical treatment of COVID-19.

scholarly journals Bioinformatics Analysis of the Regulatory lncRNA-miRNA-mRNA Network in Patients with Neuropathic Pain

2020 ◽  
Author(s):  
Huai-Gen Zhang ◽  
Li Liu ◽  
Zhi-Ping Song ◽  
Da-Ying Zhang
Keyword(s):  
Neuropathic Pain ◽  
Signaling Pathway ◽  
Interaction Analysis ◽  
Enrichment Analysis ◽  
Mapk Signaling ◽  
Biological Information ◽  
Receptor Interaction ◽  
Pathway Enrichment Analysis ◽  
Protein Protein Interaction ◽  
Potential Therapeutic Application

Abstract Background: Neuropathic pain (NP) is the main form of chronic pain, caused by damage to the nervous system and dysfunction. Methods: Here, we explore the key molecules involved in the development of NP condition via identification of lncRNA-miRNA-mRNA expression pattern of patients with NP. We identified differentially expressed miRNAs, lncRNA and mRNA through a comprehensive analysis strategy. Subsequently, we used bioinformatics approach to perform pathway enrichment analysis on DEGs and protein-protein interaction analysis. Combined with the three datasets, the lncRNA-miRNA-mRNA network was constructed. It will then be used as targets for drug prediction. Results: The results showed that a total of 8,251 DEGs (4,193 upregulated and 4,058 downregulated) were identified from the three microarray datasets, 959 DEmiRs (455 upregulated and 504 downregulated), 2,848 DElncs (1,324 upregulated and 1,524 downregulated). GO analysis showed that DEGs are mainly enriched in blood circulation, regulation of membrane potential and regulation of ion transmembrane transport. KEGG results showed that DEGs are enriched in neuroactive ligand-receptor interaction, PI3K-Akt signaling pathway and MAPK signaling pathway. When the correlation is set to above 0.8, a total of 31 lncRNAs, 36 miRNAs and 24 mRNAs were screened in the lncRNA-miRNA-mRNAs network. The results of drug prediction indicated the targeted drugs mainly include INDOMETHACIN, GLUTAMIC ACID and PIRACETAM. Conclusion: The lncRNA-miRNA-mRNA network has been carried out a comprehensive biological information analysis and predicted the potential therapeutic application of drugs in patients with NP. The corresponding data has a certain reference for studying the pathological mechanism of NP.

Construction of a lncRNA–miRNA–mRNA network to determine the key regulators of the Th1/Th2 imbalance in multiple sclerosis

Epigenomics ◽  
2021 ◽  
Author(s):  
Hanieh Azari ◽  
Elham Karimi ◽  
Mohammad Shekari ◽  
Ahmad Tahmasebi ◽  
Amin Reza Nikpoor ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Bioinformatics Analysis ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
T Helper ◽  
Protein Protein Interaction ◽  
Novel Biomarkers ◽  
Ifn Γ ◽  
Protein Protein Interaction Network ◽  
Competitive Endogenous Rna

Aim: The exact epigenetic mechanisms that determine the balance of T helper cells 1 and 2 (Th1/Th2) and autoimmune responses in multiple sclerosis (MS) remain unclear. We aim to clarify these. Methods: A combination of bioinformatics analysis and molecular evaluations was utilized to identify master hub genes. Results: A competitive endogenous RNA network containing six long noncoding RNAs (lncRNAs), 21 miRNAs and 86 mRNAs was provided through enrichment analysis and a protein–protein interaction network. NEAT1 and MALAT1 were found as differentially expressed lncRNAs using GEO (GSE21942). Quantitative real-time PCR results demonstrate dysregulation in the RUNX3 (a regulator of Th1/Th2 balance), GATA3 and TBX21, as well as miR-544a and miR-210-3p (which directly target RUNX3). ELISA also confirmed an imbalance in IFN-γ (Th1)/IL-4 (Th2) in MS patients. Conclusion: Our findings introduce novel biomarkers leading to Th1/Th2 imbalance in MS.

Identification of Candidate Genetic Markers and a Novel 4-genes Diagnostic Model in Osteoarthritis through Integrating Multiple Microarray Data

2020 ◽  
Vol 23 (8) ◽  
pp. 805-813
Author(s):  
Ai Jiang ◽  
Peng Xu ◽  
Zhenda Zhao ◽  
Qizhao Tan ◽  
Shang Sun ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Microarray Data ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Mapk Signaling ◽  
Functional Enrichment ◽  
Joint Disease ◽  
Support Vector ◽  
Diagnostic Model ◽  
Data Sets

Background: Osteoarthritis (OA) is a joint disease that leads to a high disability rate and a low quality of life. With the development of modern molecular biology techniques, some key genes and diagnostic markers have been reported. However, the etiology and pathogenesis of OA are still unknown. Objective: To develop a gene signature in OA. Method: In this study, five microarray data sets were integrated to conduct a comprehensive network and pathway analysis of the biological functions of OA related genes, which can provide valuable information and further explore the etiology and pathogenesis of OA. Results and Discussion: Differential expression analysis identified 180 genes with significantly expressed expression in OA. Functional enrichment analysis showed that the up-regulated genes were associated with rheumatoid arthritis (p < 0.01). Down-regulated genes regulate the biological processes of negative regulation of kinase activity and some signaling pathways such as MAPK signaling pathway (p < 0.001) and IL-17 signaling pathway (p < 0.001). In addition, the OA specific protein-protein interaction (PPI) network was constructed based on the differentially expressed genes. The analysis of network topological attributes showed that differentially upregulated VEGFA, MYC, ATF3 and JUN genes were hub genes of the network, which may influence the occurrence and development of OA through regulating cell cycle or apoptosis, and were potential biomarkers of OA. Finally, the support vector machine (SVM) method was used to establish the diagnosis model of OA, which not only had excellent predictive power in internal and external data sets (AUC > 0.9), but also had high predictive performance in different chip platforms (AUC > 0.9) and also had effective ability in blood samples (AUC > 0.8). Conclusion: The 4-genes diagnostic model may be of great help to the early diagnosis and prediction of OA.

Identification of key mRNAs and lncRNAs involved in the effects of anti-TWEAK on Osteosarcoma

2020 ◽  
Vol 15 ◽  
Author(s):  
Mingxuan Yang ◽  
Liangtao Zhao ◽  
Xuchang Hu ◽  
Haijun Feng ◽  
Xuewen Kang
Keyword(s):  
Dna Replication ◽  
Signaling Pathway ◽  
Bioinformatics Analysis ◽  
Mapk Signaling ◽  
Migration And Invasion ◽  
Type I ◽  
Interferon Signaling ◽  
Nf Kappa B ◽  
Ppi Networks ◽  
Coexpression Networks

Background: Osteosarcoma (OS) is one of the most common primary malignant bone tumors in teenagers. Emerging studies demonstrated TWEAK and Fn14 were involved in regulating cancer cell differentiation, proliferation, apoptosis, migration and invasion. Objective: The present study identified differently expressed mRNAs and lncRNAs after anti-TWEAK treatment in OS cells using GSE41828. Methods: We identified 922 up-regulated mRNAs, 863 downregulated mRNAs, 29 up-regulated lncRNAs, and 58 down-regulated lncRNAs after anti-TWEAK treatment in OS cells. By constructing PPI networks, we identified several key proteins involved in anti-TWEAK treatment in OS cells, including MYC, IL6, CD44, ITGAM, STAT1, CCL5, FN1, PTEN, SPP1, TOP2A, and NCAM1. By constructing lncRNAs coexpression networks, we identified several key lncRNAs, including LINC00623, LINC00944, PSMB8-AS1, LOC101929787. Result: Bioinformatics analysis revealed DEGs after anti-TWEAK treatment in OS were involved in regulating type I interferon signaling pathway, immune response related pathways, telomere organization, chromatin silencing at rDNA, and DNA replication. Bioinformatics analysis revealed differently expressed lncRNAs after antiTWEAK treatment in OS were related to telomere organization, protein heterotetramerization, DNA replication, response to hypoxia, TNF signaling pathway, PI3K-Akt signaling pathway, Focal adhesion, Apoptosis, NF-kappa B signaling pathway, MAPK signaling pathway, FoxO signaling pathway. Conclusion: : This study provided useful information for understanding the mechanisms of TWEAK underlying OS progression and identifying novel therapeutic markers for OS.

scholarly journals Identification of Key miRNAs in the Treatment of Dabrafenib-Resistant Melanoma

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Guangyu Gao ◽  
Zhen Yao ◽  
Jiaofeng Shen ◽  
Yulong Liu
Keyword(s):  
Drug Resistance ◽  
Molecular Mechanism ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Obvious Association

Dabrafenib resistance is a significant problem in melanoma, and its underlying molecular mechanism is still unclear. The purpose of this study is to research the molecular mechanism of drug resistance of dabrafenib and to explore the key genes and pathways that mediate drug resistance in melanoma. GSE117666 was downloaded from the Gene Expression Omnibus (GEO) database and 492 melanoma statistics were also downloaded from The Cancer Genome Atlas (TCGA) database. Besides, differentially expressed miRNAs (DEMs) were identified by taking advantage of the R software and GEO2R. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) and FunRich was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to identify potential pathways and functional annotations linked with melanoma chemoresistance. 9 DEMs and 872 mRNAs were selected after filtering. Then, target genes were uploaded to Metascape to construct protein-protein interaction (PPI) network. Also, 6 hub mRNAs were screened after performing the PPI network. Furthermore, a total of 4 out of 9 miRNAs had an obvious association with the survival rate ( P < 0.05 ) and showed a good power of risk prediction model of over survival. The present research may provide a deeper understanding of regulatory genes of dabrafenib resistance in melanoma.

scholarly journals Comprehensive Characterization of Androgen-Responsive lncRNAs Mediated Regulatory Network in Hormone-Related Cancers

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Dan Wang ◽  
Mingyue Li ◽  
Jing Li ◽  
Xuechao Wan ◽  
Yan Huang ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Cancer Progression ◽  
Regulatory Network ◽  
Bioinformatics Analysis ◽  
Mapk Signaling ◽  
Ppi Network ◽  
Network Analyses ◽  
Comprehensive Characterization ◽  
Upstream Regulators

The AR signaling pathway plays an important role in initiation and progression of many hormone-related cancers including prostate, bladder, kidney, lung, and breast cancer. However, the potential roles of androgen-responsive long noncoding RNAs (lncRNAs) in hormone-related cancers remained unclear. In the present study, we identified 469 novel androgen-responsive lncRNAs using microarray data. After validating the accuracy of the array data, we constructed a transcriptional network which contained more than 30 transcriptional factors using ChIP-seq data to explore upstream regulators of androgen-responsive lncRNAs. Next, we conducted bioinformatics analysis to identify lncRNA-miRNA-mRNA regulatory network. To explore the potential roles of androgen-responsive lncRNAs in hormone-related cancers, we performed coexpression network and PPI network analyses using TCGA data. GO and KEGG analyses showed these lncRNAs were mainly involved in regulating signal transduction, transcription, development, cell adhesion, immune response, cell differentiation, and MAPK signaling pathway. We also highlight the prognostic value of HPN-AS1, TPTEP1, and LINC00623 in cancer outcomes. Our results suggest that androgen-responsive lncRNAs played important roles in regulating hormone-related cancer progression and could be novel molecular biomarkers.

scholarly journals A Network Pharmacology Approach to Uncover the Mechanisms of Shen-Qi-Di-Huang Decoction against Diabetic Nephropathy

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Sha Di ◽  
Lin Han ◽  
Qing Wang ◽  
Xinkui Liu ◽  
Yingying Yang ◽  
...  
Keyword(s):  
Diabetic Nephropathy ◽  
Signaling Pathway ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Protein Protein Interaction ◽  
Regulation Of Blood Pressure ◽  
Protein Protein Interaction Network

Shen-Qi-Di-Huang decoction (SQDHD), a well-known herbal formula from China, has been widely used in the treatment of diabetic nephropathy (DN). However, the pharmacological mechanisms of SQDHD have not been entirely elucidated. At first, we conducted a comprehensive literature search to identify the active constituents of SQDHD, determined their corresponding targets, and obtained known DN targets from several databases. A protein-protein interaction network was then built to explore the complex relations between SQDHD targets and those known to treat DN. Following the topological feature screening of each node in the network, 400 major targets of SQDHD were obtained. The pathway enrichment analysis results acquired from DAVID showed that the significant bioprocesses and pathways include oxidative stress, response to glucose, regulation of blood pressure, regulation of cell proliferation, cytokine-mediated signaling pathway, and the apoptotic signaling pathway. More interestingly, five key targets of SQDHD, named AKT1, AR, CTNNB1, EGFR, and ESR1, were significant in the regulation of the above bioprocesses and pathways. This study partially verified and predicted the pharmacological and molecular mechanisms of SQDHD on DN from a holistic perspective. This has laid the foundation for further experimental research and has expanded the rational application of SQDHD in clinical practice.

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