STAT1: a novel candidate biomarker and potential therapeutic target of the recurrent aphthous stomatitis

Abstract Background The recurrent aphthous stomatitis (RAS) frequently affects patient quality of life as a result of long lasting and recurrent episodes of burning pain. However, there were temporarily few available effective medical therapies currently. Drug target identification was the first step in drug discovery, was usually finding the best interaction mode between the potential target candidates and probe small molecules. Therefore, elucidating the molecular mechanism of RAS pathogenesis and exploring the potential molecular targets of medical therapies for RAS was of vital importance. Methods Bioinformatics data mining techniques were applied to explore potential novel targets, weighted gene co-expression network analysis (WGCNA) was used to construct a co-expression module of the gene chip data from GSE37265, and the hub genes were identified by the Molecular Complex Detection (MCODE) plugin. Results A total of 16 co-expression modules were identified, and 30 hub genes in the turquoise module were identified. In addition, functional analysis of Hub genes in modules of interest was performed, which indicated that such hub genes were mainly involved in pathways related to immune response, virus infection, epithelial cell, signal transduction. Two clusters (highly interconnected regions) were determined in the network, with score = 17.647 and 10, respectively, cluster 1 and cluster 2 are linked by STAT1 and ICAM1, it is speculated that STAT1 may be a primary gene of RAS. Finally, genistein, daidzein, kaempferol, resveratrol, rosmarinic acid, triptolide, quercetin and (-)-epigallocatechin-3-gallate were selected from the TCMSP database, and both of them is the STAT-1 inhibitor. The results of reverse molecular docking suggest that in addition to triptolide, (-)-Epigallocatechin-3-gallate and resveratrol, the other 5 compounds (flavonoids) with similar structures may bind to the same position of STAT1 protein with different docking score. Conclusions Our study identified STAT1 as the potential biomarkers that might contribute to the diagnosis and potential therapeutic target of RAS, and we can also screen RAS therapeutic drugs from STAT-1 inhibitors.

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Some Perspectives on Network Modeling in Therapeutic Target Prediction

Biomedical Engineering and Computational Biology ◽

10.4137/becb.s10793 ◽

2013 ◽

Vol 5 ◽

pp. BECB.S10793 ◽

Cited By ~ 3

Author(s):

Reka Albert ◽

Bhaskar DasGupta ◽

Nasim Mobasheri

Keyword(s):

Therapeutic Target ◽

Drug Target ◽

Biological Network ◽

Target Identification ◽

Target Prediction ◽

Identification Problem ◽

Pharmaceutical Companies ◽

Specific Behavior ◽

Drug Target Identification ◽

Key Steps

Drug target identification is of significant commercial interest to pharmaceutical companies, and there is a vast amount of research done related to the topic of therapeutic target identification. Interdisciplinary research in this area involves both the biological network community and the graph algorithms community. Key steps of a typical therapeutic target identification problem include synthesizing or inferring the complex network of interactions relevant to the disease, connecting this network to the disease-specific behavior, and predicting which components are key mediators of the behavior. All of these steps involve graph theoretical or graph algorithmic aspects. In this perspective, we provide modelling and algorithmic perspectives for therapeutic target identification and highlight a number of algorithmic advances, which have gotten relatively little attention so far, with the hope of strengthening the ties between these two research communities.

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Identification of cell division cycle 20 as a candidate biomarker and potential therapeutic target in bladder cancer using bioinformatics analysis

Bioscience Reports ◽

10.1042/bsr20194429 ◽

2020 ◽

Vol 40 (7) ◽

Cited By ~ 1

Author(s):

Peilin Shen ◽

Xuejun He ◽

Lin Lan ◽

Yingkai Hong ◽

Mingen Lin

Keyword(s):

Gene Expression ◽

Bladder Cancer ◽

Cell Division ◽

Mrna Expression ◽

Therapeutic Target ◽

Gene Expression Omnibus ◽

Cell Division Cycle ◽

Hub Genes ◽

Potential Therapeutic Target ◽

Protein Protein Interaction

Abstract Purpose: As bladder cancer (BC) is very heterogeneous and complicated in the genetic level, exploring genes to serve as biomarkers and therapeutic targets is practical. Materials and methods: We searched Gene Expression Omnibus (GEO) and downloaded the eligible microarray datasets. After intersection analysis for identified differentially expressed genes (DEGs) of included datasets, overlapped DEGs were identified and subsequently analyzed with Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Protein–Protein Interaction (PPI) and hub genes identification. Hub genes were further analyzed with mRNA expression comparation in Oncomine and Gene Expression Profiling Interactive Analysis (GEPIA) database, proteomics-based validation in The Human Protein Atlas (THPA) and survival analysis in GEO and Oncolnc database. Results: We analyzed five eligible GEO datasets and identified 76 overlapped DEGs mapped into PPI network with 459 edges which were mainly enriched in cell cycle pathway and related terms in GO and KEGG analysis. Among five identified hub genes, which are Cyclin-Dependent Kinase 1 (CDK1), Ubiquitin-Conjugating Enzyme E2 C (UBE2C), Cell Division Cycle 20 (CDC20), Microtubule Nucleation Factor (TPX2) and Cell Division Cycle Associated 8 (CDCA8); CDC20 and CDCA8 were confirmed as significant in mRNA expression comparation and proteomics-based validation. However, only CDC20 was considered prognostically significant in both GEO and Oncolnc database. Conclusions: CDC20 and CDCA8 were identified as candidate diagnostic biomarkers for BC in the present study; however, only CDC20 was validated as prognostically valuable and may possibly serve as a candidate prognostic biomarker and potential therapeutic target. Still, further validation studies are essential and indispensable.

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Selected Ideal Natural Ligand Against TNBC by Inhibiting CDC20, Using Bioinformatics and Molecular Biology

10.21203/rs.3.rs-770130/v1 ◽

2021 ◽

Author(s):

Naimeng Liu ◽

Xinhui Wang ◽

Xiaye Lv ◽

Duo Li ◽

Haoqun Xie ◽

...

Keyword(s):

Breast Cancer ◽

Molecular Biology ◽

Triple Negative Breast Cancer ◽

Therapeutic Target ◽

Triple Negative ◽

Survival Curve ◽

Hub Genes ◽

Potential Therapeutic Target ◽

Normal Tissues ◽

Natural Ligand

Abstract Background: Triple-negative breast cancer (TNBC), as the most aggressive kinds of breast tumor, still cannot get effective targeted therapy. Recently, techniques of bioinformatics and molecular biology were hot topics in drug development. We want to find potential therapeutic targets of triple-negative breast cancer (TNBC) patients by bioinformatics and screen ideal natural ligand that can bind with the potential target and inhibit it by using molecular biology.Methods: Bioinformatics and molecular biology were combined to analyze potential therapeutic targets. Differential expression analysis identified the differentially expressed genes (DEGs) between TNBC tissues and normal tissues. The functional enrichment analyses of DEGs shown the important gene ontology (GO) terms and pathways of TNBC. Protein-protein interaction (PPI) network construction screened 20 hub genes, while Kaplan website was used to analyze the relationship between the survival curve and expression of hub genes. Then Discovery Studio 4.5 screened ideal natural inhibitors of the potential therapeutic target by LibDock, ADME, toxicity prediction, CDOCKER and molecular dynamic simulation.Results: 1,212 and 353 DEGs were respectively found between TNBC tissues and normal tissues, including 88 up-regulated and 141 down-regulated DEGs in both databases. 20 hub genes were screened, and the higher expression of CDC20 was associated with a poor prognosis. Therefore, we chose CDC20 as the potential therapeutic target. 7,416 natural ligands were conducted to bind firmly with CDC20, and among these ligands, ZINC000004098930 was regarded as the potential ideal ligand, owing to its non-hepatotoxicity, more solubility level and less carcinogenicity than the reference drug, apcin. The ZINC000004098930-CDC20 could exist stably in natural environment.Conclusion: 20 genes were regarded as hub genes of TNBC and most of them were relevant to the survival curve of breast cancer patients, especially CDC20. ZINC000004098930 was chosen as the ideal natural ligand that can targeted and inhibited CDC20, which may give great contribution to TNBC targeted treatment.

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STAT1: A novel candidate biomarker and potential therapeutic target of the recurrent aphthous stomatitis

10.21203/rs.3.rs-355780/v1 ◽

2021 ◽

Author(s):

Ming-Chen CAO ◽

Meng-Xiang Fang ◽

Lei Li ◽

XING Xiao-Min ◽

Chang-Kai ZHOU ◽

...

Keyword(s):

Gene Expression Omnibus ◽

Recurrent Aphthous Stomatitis ◽

Aphthous Stomatitis ◽

Diagnosis And Treatment ◽

Hub Genes ◽

Diagnostic Biomarkers ◽

Ncbi Gene Expression Omnibus ◽

New Ideas ◽

Stat1 Protein ◽

Primary Gene

Abstract At present, the recurrent aphthous stomatitis (RAS) are not completely clear. Therefore, identifying the underlying diagnostic biomarkers of RAS can provide new ideas for the diagnosis and treatment of RAS. The gene chip data of RAS (GSE37265) were downloaded from the NCBI Gene Expression Omnibus (GEO) database. Weighted Gene Co-Expression Network Analysis (WGCNA) was used to construct a co-expression module. A total of 16 co-expression modules were identified, and 30 hub genes in the turquoise module were identified. In addition, functional analysis of hub genes in modules of interest was performed, which indicated that such hub genes were mainly involved in pathways related to immune response, virus infection, epithelial cell, signal transduction. module two clusters (highly interconnected regions) were determined in the network, with score=17.647 and 10, respectively, cluster 1 and cluster 2 are linked by STAT1 and ICAM1, it is speculated that STAT1 may be a primary gene of RAS. Finally, genistein, daidzein, kaempferol, resveratrol, rosmarinic acid, triptolide, quercetin and (-)-epigallocatechin-3-gallate were selected from the TCMSP database, and both of them is the STAT-1 inhibitor. The results of reverse molecular docking suggest that in addition to triptolide, (-)-Epigallocatechin-3-gallate and resveratrol, the other 5 compounds (flavonoids) with similar structures may bind to the same position of STAT1 protein with different docking score. In conclusion, our results screened potential biomarkers that might contribute to the diagnosis and treatment of RAS, STAT1 protein is one of the potential therapeutic targets of RAS, and this target can be used to screen potential compounds.

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Bioinformatics analysis of KIF20A, a potential therapeutic target for glioblastoma

10.21203/rs.3.rs-138484/v1 ◽

2021 ◽

Author(s):

Lu Han ◽

Jiayang Wang

Keyword(s):

Therapeutic Target ◽

High Mobility ◽

Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Pathway Enrichment Analysis ◽

Normal Brain ◽

Hub Genes ◽

R Software ◽

Potential Therapeutic Target ◽

Geo Database

Abstract Background: Glioblastoma (GBM) is a malignant brain tumor with high mobility. The median survival time of GBM patients is 15 months. Currently, there is no effective treatment for improving the prognosis of the GBM due to a lack of prognostic markers. Materials and methods: To predict core therapeutic targets for GBM, we analyzed four microarray datasets (GSE49810, GSE50161, GSE65624, and GSE90604) selected from the Gene Expression Omnibus (GEO) database and the other datasets obtained from The Cancer Genome Atlas (TCGA) database. Expression protein array of 227 GBM samples and 18 normal samples were clustered to summarize GBM tissue classification. Differentially expressed genes (DEGs) were analyzed by comparing GBM and normal brain tissues in each profile using the limma package of R software. GO function and KEGG pathway enrichment analysis was performed using the DAVID database. Overlapping DEGs were ranked based on protein expression ratios from the comparison between cancer and normal samples using robustRankaggreg package of R software and scored from high to low. Protein-protein interaction (PPI) network was visualized using CytoHubba and Cluego plugins in Cytoscape software. Core hub genes were analyzed by MCC, MNC, DMNC, and EPC methods. Besides, the GEPIA tool was used to create the survival curves and boxplots to evaluate the prognostic effect of hub genes for improving the diagnostic outcomes and treatment of GBM. Results: A total of 2064 DEGs were analyzed (1400 downregulated DEGs and 1664 upregulated DEGs) in the GEO database. 3292 DEGs were found (1485 upregulated DEGs and 1807 downregulated DEGs) in TCGA. We selected 221 significant DEGs from four microarrays. Combining the GEO results with the results of TCGA, we found only 181 common DEGs by using Venn analysis. Further, expression levels of KIF20A selected from 10 hub genes closely associated with the survival rate. Conclusion: Up-regulation of KIF20A has a pivotal role in controlling the prognosis of GBM in 2 years follow-up period; KIF20A should be considered as a potential therapeutic target for GBM.

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