scholarly journals Integrated bioinformatics approach to understand immune-related key genes and pathways in chronic spontaneous urticaria

2020 ◽  
Author(s):  
wenxing su ◽  
biao huang ◽  
ying zhao ◽  
xiaoyan zhang ◽  
lu chen ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Protein Interaction ◽  
Chronic Spontaneous Urticaria ◽  
Signal Pathways ◽  
Radical Cure ◽  
Ppi Network ◽  
Erythroid Progenitor ◽  
Immune Infiltration ◽  
Protein Protein Interaction ◽  
Key Genes

Abstract Background Chronic spontaneous urticaria (CSU) refers to recurrent urticaria that lasts for more than 6 weeks in the absence of an identifiable trigger. Due to its recurrent wheal and severe itching, CSU seriously affects patients' life quality. There is currently no radical cure for it and its vague pathogenesis limits the development of targeted therapy. With the goal of revealing the underlying mechanism, two data sets with accession numbers GSE57178 and GSE72540 were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the differentially expressed genes (DEGs) of CSU skin lesion samples and healthy controls, four kinds of analyses were performed, namely functional annotation, protein-protein interaction (PPI) network and module construction, co-expression and drug-gene interaction prediction analysis, and immune and stromal cells deconvolution analyses. Results 92 up-regulated genes and 7 down-regulated genes were selected for subsequent analyses. Through the enrichment analysis of the core modules, three signal pathways were found to be closely related to the occurrence and development of CSU, including TNF signaling pathway, NF-κB signaling pathway and Jak-STAT signaling pathway. Referring to protein-protein interaction (PPI) network analysis and GeneCards database, we identified four key genes, IL6, TLR4, ICAM1, and PTGS2. In addition, according to the results of immune infiltration analysis, CSU tissue generally contained a higher proportion of dendritic cells, Th2 cells, mast cells, megakaryocyte-erythroid progenitor, preadipocytes, and macrophages M1. Conclusions To conclude, the key genes and pathways identified from CSU lesions and normal controls along with the immune infiltration profile may provide new insights into the development of CSU.

scholarly journals Effect and mechanism of Acoritataninowii Rhizoma and Curcumae Radix on chronic gastritis: a network pharmacology study

2020 ◽  
Author(s):  
Ling Zhang ◽  
Yunkai Dai ◽  
Yuping Li ◽  
Weijing Chen ◽  
Ruliu Li ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Chronic Gastritis ◽  
Protein Interaction ◽  
Target Genes ◽  
Kegg Pathway ◽  
Network Pharmacology ◽  
Signal Pathways ◽  
Ppi Network ◽  
Pathway Enrichment ◽  
Go Enrichment

Abstract Background Chronic gastritis (CG) is an inflammatory disease which is one of the common diseases of the digestive system. To investigate the mechanisms of herbal pair Acoritataninowii Rhizoma(Shichangpu, AR) and Curcumae Radix༈Yujin, CR༉ in treatment of CG based on the network pharmacology. Methods The possible active ingredients and targets of AR-CR were obtained by the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). The UniProt database was used to query the human gene corresponding to each target protein. The genes related to CG were collected from the GeneCards database, the OMIM database, the DisGeNET database and the PharmGKB database. Intersected the target genes of AR-CR and CG, then protein-protein interaction(PPI) network was constructed by STRING website. The overlapped genes were subjected to gene ontology༈GO༉enrichment and Kyoto encyclopedia of genes and genomes༈KEGG༉pathway enrichment analyses by David. Results 45 intersection genes were obtained, and there were 40 targets in the PPI network for protein interaction, the kernel targets with Degree ≥ 10 included AKT1, TNF, JUN, MAPK3, MAPK8 and MAPK1. The Go enrichment analysis was mainly related to protein binding, enzyme binding, protein homodimerization activity, etc. The KEGG pathway enrichment analyses mainly involved the Pathways in cancer, TNF signaling pathway, Apoptosis, and VEGF signaling pathway. Conclusion AR-CR might delayed, blocked or reversed the atrophy, intestinal metaplasia, dysplasia and canceration of gastric mucosa by targeting key proteins and signal pathways,achieved the effect of the treatment of CG.

scholarly journals ITGB2 as a Novel Biomarker Correlating With Prognosis and Immune Infiltrates in Ovarian Cancer

2021 ◽  
Author(s):  
chanyuan li ◽  
Ting Wan ◽  
Ting Deng ◽  
Junya Cao ◽  
He Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Protein Interaction ◽  
Ppi Network ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Protein Protein Interaction ◽  
Kaplan Meier ◽  
Kaplan Meier Plotter

Abstract Background: Epithelial ovarian cancer is nowadays one of the malignancies in women, this study aimed to identify novel biomarkers to predict prognosis and immunotherapy efficacy.Methods: The differentially expressed genes (DEGs) obtained from online database Gene Expression Omnibus (GEO)were screened via GEO2R and Venn diagram software, gene enrichment was analysed by Gene Ontology(GO) function and Kyoto Encyclopedia of Genes and Genomes(KEGG), then protein protein interaction(PPI)network and Cytoscape software were used to confirm the genes closely related to ovarian cancer. Survival analysis of hub genes were obtained from Kaplan–Meier plotter, with their differential expression in specimen validated by Gene Expression Profiling Interactive Analysis (GEPIA) and an integrated repository portal for tumor-immune system interactions (TISIDB). Finally, we used the Tumor Immune Estimation Resource 2.0 (TIMER2.0) and application Estimate the Proportion of Immune and Cancer cells (EPIC) to search the immune infiltration characteristics of the genes.Results: 355 DEGs between epithelial ovarian cancer and normal ovarian tissue were screened out. These DEGs were associated with extracellular exosome, bicellular tight junction and cell-cell junction, and remarkably enriched in molecules of cell adhesion and leukocyte transendothelial migration activity. Ten hub genes were identified via protein protein interaction (PPI) network: PTAFR, HLA-DRA, OAS2, OAS3, PTPN6, LYN, VAMP8, IRF6, ITGB2, CD47. Furthermore, the Kaplan–Meier plotter was conducted, overexpression of four genes was positively connected to poor prognosis in ovarian cancer:OAS2, OAS3, ITGB2, CD47,which were also correlated with immune infiltrates in ovarian cancer and had the highest degree of correlation with tumor associated macrophages (TAMs) infiltration, among which ITGB2 was highly correlated with TAMs infiltration level.Conclusion: ITGB2, OAS2, OAS3, and CD47 are upregulated with unfavorable prognosis in ovarian cancer, and ITGB2 may act as a novel prognostic biomarker with immune infiltration values.

A Computational Framework to Identify Cross Association between Complex Disorders by Protein-Protein Interaction Network Analysis

2020 ◽  
Vol 15 ◽  
Author(s):  
Nikhila T Suresh ◽  
Vimina E R ◽  
U. Krishnakumar
Keyword(s):  
Blood Pressure ◽  
Signaling Pathway ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Complex Disorders ◽  
Protein Protein Interaction ◽  
Associated Proteins ◽  
Key Genes ◽  
Protein Protein Interaction Network

Objective: It is a known fact that numerous complex disorders do not happen in isolation indicating the plausible set of shared causes common to several different sicknesses. Hence, analysis of comorbidity can be utilized to explore association between several disorders. In this study, we have proposed a network-based computational approach, in which genes are organized based on the topological characteristics of the constructed Protein-Protein Interaction Network (PPIN) followed by a network prioritization scheme, to identify distinctive key genes and biological pathways shared among diseases. Methods: The proposed approach is initiated from constructed PPIN of any randomly chosen disease genes in order to infer its associations with other diseases in terms of shared pathways, co-expression, co-occurrence etc. For this, initially proteins associated to any disease based on random choice were identified. Secondly, PPIN is organized through topological analysis to define hub genes. Finally, using a prioritization algorithm a ranked list of newly predicted multimorbidity-associated proteins is generated. Using Gene Ontology (GO), cellular pathways involved in multimorbidity-associated proteins are mined. Result and Conclusion: The proposed methodology is tested using three disorders namely Diabetes, Obesity and blood pressure at an atomic level and the results suggest the comorbidity of other complex diseases that have associations with the proteins included in disease of present study through shared proteins and pathways. For diabetes, we have obtained key genes like GAPDH, TNF, IL6, AKT1, ALB, TP53, IL10, MAPK3, TLR4 and EGF with key pathways like P53 pathway, VEGF signaling pathway, Ras Pathway, Interleukin signaling pathway, Endothelin signaling pathway, Huntington disease etc. Study on other disorders such as obesity and blood pressure also revealed promising results.

scholarly journals Decoding the molecular mechanism of parthenocarpy in Musa spp. through protein-protein interaction network

2021 ◽  
Author(s):  
Backiyarani Suthanthiram ◽  
Sasikala Rajendran ◽  
Sharmiladevi Simeon ◽  
Uma Subbaraya
Keyword(s):  
Candidate Genes ◽  
Signaling Pathway ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Auxin Signaling ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Abstract Banana, one of the most important staple, delicious fruit among global consumers is highly sterile owing to natural parthenocarpy. Identification of genetic factors responsible for parthenocarpy would facilitate the conventional breeders to improve the seeded accessions. We have constructed Protein-protein interaction (PPI) network through mining differentially expressed genes and the genes used for transgenic studies with respect to parthenocarpy. Based on the topological and pathway enrichment analysis of proteins in PPI network, 12 candidate genes were shortlisted. By exploring the PPI of candidate genes from the putative network, we postulated a putative pathway that bring insights into the significance of cytokinin mediated CLV-WUSHEL signaling pathway in addition to gibberellin mediated auxin signaling pathway in parthenocarpy. Further validation of candidate genes in seeded and seedless accession of Musa spp using qRT-PCR put forward AGL8, MADS16, IAA (GH3.8), RGA1, EXPA1, GID1C, HK2 and BAM1 as possible target genes in natural parthenocarpy. In contrary, expression profile of ACLB-2 and ZEP is anticipated to highlight the difference in artificially induced and natural parthenocarpy. Our analysis is the first attempt to identify candidate genes and to hypothesize a putative mechanism that bridges the gaps in understanding natural parthenocarpy through protein-protein interaction network.

Protein-Protein Interaction (PPI) Network: Recent Advances in Drug Discovery

2017 ◽  
Vol 18 (1) ◽  
pp. 5-10 ◽  
Author(s):  
Alexiou Athanasios ◽  
Vairaktarakis Charalampos ◽  
Tsiamis Vasileios ◽  
Ghulam Ashraf
Keyword(s):  
Drug Discovery ◽  
Protein Interaction ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Recent Advances

scholarly journals Key Genes and Prognostic Analysis in HER2+ Breast Cancer

2021 ◽  
Vol 20 ◽  
pp. 153303382098329
Author(s):  
Yujie Weng ◽  
Wei Liang ◽  
Yucheng Ji ◽  
Zhongxian Li ◽  
Rong Jia ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Protein Interaction ◽  
Differentially Expressed ◽  
Protein Kinase Activity ◽  
Protein Protein Interaction ◽  
Risk Of Death ◽  
Her2 Breast Cancer ◽  
Key Genes

Human epidermal growth factor 2 (HER2)+ breast cancer is considered the most dangerous type of breast cancers. Herein, we used bioinformatics methods to identify potential key genes in HER2+ breast cancer to enable its diagnosis, treatment, and prognosis prediction. Datasets of HER2+ breast cancer and normal tissue samples retrieved from Gene Expression Omnibus and The Cancer Genome Atlas databases were subjected to analysis for differentially expressed genes using R software. The identified differentially expressed genes were subjected to gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses followed by construction of protein-protein interaction networks using the STRING database to identify key genes. The genes were further validated via survival and differential gene expression analyses. We identified 97 upregulated and 106 downregulated genes that were primarily associated with processes such as mitosis, protein kinase activity, cell cycle, and the p53 signaling pathway. Visualization of the protein-protein interaction network identified 10 key genes ( CCNA2, CDK1, CDC20, CCNB1, DLGAP5, AURKA, BUB1B, RRM2, TPX2, and MAD2L1), all of which were upregulated. Survival analysis using PROGgeneV2 showed that CDC20, CCNA2, DLGAP5, RRM2, and TPX2 are prognosis-related key genes in HER2+ breast cancer. A nomogram showed that high expression of RRM2, DLGAP5, and TPX2 was positively associated with the risk of death. TPX2, which has not previously been reported in HER2+ breast cancer, was associated with breast cancer development, progression, and prognosis and is therefore a potential key gene. It is hoped that this study can provide a new method for the diagnosis and treatment of HER2 + breast cancer.

scholarly journals Decoding the molecular mechanism of parthenocarpy in Musa spp. through protein–protein interaction network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Suthanthiram Backiyarani ◽  
Rajendran Sasikala ◽  
Simeon Sharmiladevi ◽  
Subbaraya Uma
Keyword(s):  
Candidate Genes ◽  
Protein Interaction ◽  
Target Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Auxin Signaling ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
The Difference

AbstractBanana, one of the most important staple fruit among global consumers is highly sterile owing to natural parthenocarpy. Identification of genetic factors responsible for parthenocarpy would facilitate the conventional breeders to improve the seeded accessions. We have constructed Protein–protein interaction (PPI) network through mining differentially expressed genes and the genes used for transgenic studies with respect to parthenocarpy. Based on the topological and pathway enrichment analysis of proteins in PPI network, 12 candidate genes were shortlisted. By further validating these candidate genes in seeded and seedless accession of Musa spp. we put forward MaAGL8, MaMADS16, MaGH3.8, MaMADS29, MaRGA1, MaEXPA1, MaGID1C, MaHK2 and MaBAM1 as possible target genes in the study of natural parthenocarpy. In contrary, expression profile of MaACLB-2 and MaZEP is anticipated to highlight the difference in artificially induced and natural parthenocarpy. By exploring the PPI of validated genes from the network, we postulated a putative pathway that bring insights into the significance of cytokinin mediated CLAVATA(CLV)–WUSHEL(WUS) signaling pathway in addition to gibberellin mediated auxin signaling in parthenocarpy. Our analysis is the first attempt to identify candidate genes and to hypothesize a putative mechanism that bridges the gaps in understanding natural parthenocarpy through PPI network.

The Screening and Identification of Key Biomarkers in Adrenocortical Carcinoma: Evidence from a Bioinformatics Analysis

2022 ◽  
Vol 12 (3) ◽  
pp. 523-532
Author(s):  
Xin Yan ◽  
Chunfeng Liang ◽  
Xinghuan Liang ◽  
Li Li ◽  
Zhenxing Huang ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Gene Ontology ◽  
Protein Interaction ◽  
Adrenocortical Carcinoma ◽  
Gene Expression Omnibus ◽  
Heparin Binding ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Upregulated Genes

<sec> <title>Objective:</title> This study aimed to identify the potential key genes associated with the progression and prognosis of adrenocortical carcinoma (ACC). </sec> <sec> <title>Methods:</title> Differentially expressed genes (DEGs) in ACC cells and normal adrenocortical cells were assessed by microarray from the Gene Expression Omnibus database. The biological functions of the classified DEGs were examined by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses and a protein–protein interaction (PPI) network was mapped using Cytoscape software. MCODE software was also used for the module analysis and then 4 algorithms of cytohubba software were used to screen hub genes. The overall survival (OS) examination of the hub genes was then performed by the ualcan online tool. </sec> <sec> <title>Results:</title> Two GSEs (GSE12368, GSE33371) were downloaded from GEO including 18 and 43 cases, respectively. One hundred and sixty-nine DEGs were identified, including 57 upregulated genes and 112 downregulated genes. The Gene Ontology (GO) analyses showed that the upregulated genes were significantly enriched in the mitotic cytokines is, nucleus and ATP binding, while the downregulated genes were involved in the positive regulation of cardiac muscle contraction, extracellular space, and heparin-binding (P < 0.05). The Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) pathway examination showed significant pathways including the cell cycle and the complement and coagulation cascades. The protein– protein interaction (PPI) network consisted of 162 nodes and 847 edges, including mitotic nuclear division, cytoplasmic, protein kinase binding, and cell cycle. All 4 identified hub genes (FOXM1, UBE2C, KIF11, and NDC80) were associated with the prognosis of adrenocortical carcinoma (ACC) by survival analysis. </sec> <sec> <title>Conclusions:</title> The present study offered insights into the molecular mechanism of adrenocortical carcinoma (ACC) that may be beneficial in further analyses. </sec>

Identification of Potential Genes Associated with Vemurafenib Efficacy and Melanoma Prognosis

2021 ◽  
Vol 24 ◽  
Author(s):  
Yue Qi ◽  
GuiE Ma
Keyword(s):  
Signaling Pathway ◽  
Estrogen Signaling ◽  
Akt Signaling Pathway ◽  
Ppi Network ◽  
Hub Genes ◽  
Ampk Signaling ◽  
Melanoma Patients ◽  
Key Genes ◽  
Neurotrophin Signaling ◽  
Upregulated Genes

Objective: This work aimed to investigate the molecular mechanisms underlying the efficacy of vemurafenib as a treatment for melanoma. Methods: The GSE52882 dataset, which includes A375 and A2058 melanoma cell lines treated with vemurafenib and dimethyl sulfoxide (DMSO), and clinical information associated with melanoma patients, were acquired from the Gene Expression Omnibus (GEO) database and University of California Santa Cruz (UCSC), respectively. Functional enrichment analysis, protein-protein interaction (PPI) network construction, sub-module analysis, and transcriptional regulation analysis were performed on overlapping differentially expressed genes (DEGs) identified in both cell lines. Finally, we performed a survival analysis based on the genes identified. Results: A total of 447 consistently overlapping DEGs (176 up- and 271 down-regulated DEGs) were screened. Upregulated genes were enriched in pathways of neurotrophin signaling, estrogen signaling, and transcriptional misregulation in cancer. Downregulated DEGs played essential roles in melanogenesis, pathways of cancer, PI3K-Akt signaling pathway, and AMPK signaling pathway. Upregulated (MMP2, JUN, KAT28, and PIK3R3) and downregulated genes (CXCL8, CCND1, IGF1R, and ITGB3) were considered as hub genes in the PPI network. Additionally, PIK3R3 and LEF1 served as key genes in the regulatory network. The overexpression of MMP2 and CXCL8 was associated with a poor prognosis in melanoma patients. Results: A total of 447 consistently overlapping DEGs (176 up- and 271 down-regulated DEGs) were screened. Upregulated genes were enriched in pathways of neurotrophin signaling, estrogen signaling, and transcriptional misregulation in cancer. Downregulated DEGs played essential roles in melanogenesis, pathways of cancer, PI3K-Akt signaling pathway, and AMPK signaling pathway. Upregulated (MMP2, JUN, KAT28, and PIK3R3) and downregulated genes (CXCL8, CCND1, IGF1R, and ITGB3) were considered as hub genes in the PPI network. Additionally, PIK3R3 and LEF1 served as key genes in the regulatory network. The overexpression of MMP2 and CXCL8 was associated with a poor prognosis in melanoma patients. Conclusion: MMP2, CXCL8, PIK3R3, ITGB3, and LEF1 may play roles in the efficacy of vemurafenib treatment in melanoma; for example, MMP2 and PIK3R3 are likely associated with vemurafenib resistance. These findings will contribute to the development of novel therapies for melanoma.

scholarly journals Network Pharmacology study of Curcuma longa L.: Potential Target Proteins and their Functional Enrichment Analysis.

2020 ◽  
Author(s):  
SANGEETA KUMARI
Keyword(s):  
Signaling Pathway ◽  
Mechanism Of Action ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Curcuma Longa ◽  
Interaction Network ◽  
Functional Enrichment ◽  
Target Proteins ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Abstract Objective This study’s primary goal is unraveling the mechanism of action of bioactives of Curcuma longa L. at the molecular level using protein-protein interaction network.Results We used target proteins to create protein-protein interaction network (PPIN) and identified significant node and edge attributes of PPIN. We identified the cluster of proteins in the PPIN, which were used to identify enriched pathways. . We identified closeness centrality and jaccard score as most important node and edge attribute of the PPIN respectively. The enriched pathways of various clusters were overlapped suggesting synergistic mechanism of action. The three pathways found to be common among three clusters were Gonadotropin-releasing hormone receptor pathway, Endothelin signaling pathway, and Inflammation mediated by chemokine and cytokine signaling pathway.

Sign in / Sign up

Export Citation Format

Share Document