Bioinformatics‐Driven New Immune Target Discovery in Disease

Yang, Chunhe; Chen, Peng; Zhang, Wen; Du, Hongwu

doi:10.1111/sji.12452

Cited by 3 publications

(2 citation statements)

References 43 publications

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“…RA is a chronic inflammatory autoimmune disease characterized by infiltration of immune cells into the affected synovium, release of inflammatory cytokines and degradation of mediators, and subsequent joint damage [35,36]. With the rapid development of molecular biology and analysis tools, bioinformatics has provided powerful strategic markers for the screening of molecules [37,38], and the CIBERSORT tool has promoted disease analysis with patterns of immune cell infiltration [39].…”

Section: Discussionmentioning

confidence: 99%

Disease Markers and Therapeutic Targets for Rheumatoid Arthritis Identified by Integrating Bioinformatics Analysis with Virtual Screening of Traditional Chinese Medicine

Sun¹,

Liu²,

Zhang³

2022

Front. Biosci. (Landmark Ed)

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Objective:The aim of this study was to identify potentially important Rheumatoid arthritis (RA) targets related to immune cells based on bioinformatics analysis, and to identify small molecules of traditional Chinese medicine (TCM) associated with these targets that have potential therapeutic effects on RA. Methods: Gene expression profile data related to RA were downloaded from the Gene Expression Omnibus (GSE55235, GSE55457, and GSE77298), and datasets were merged by the batch effect removal method. The RA key gene set was identified by protein-protein interaction network analysis and machine learning-based feature extraction. Furthermore, immune cell infiltration analysis was carried out on all DEGs to obtain key RA markers related to immune cells. Batch molecular docking of key RA markers was performed on our previously compiled dataset of small molecules in TCM using AutoDock Vina. Moreover, in vitro experiments were performed to examine the inhibitory effect of screened compounds on the synovial cells of an RA rat model. Results:The PPI network and feature extraction with machine learning classifiers identified eight common key RA genes: MYH11, CFP, LY96, IGJ, LPL, CD48, RAC2, and CSK. RAC2 was significantly correlated with the infiltration and expression of five immune cells, with significant differences in these immune cells in the normal and RA samples. Molecular docking and in vitro experiments also showed that sanguinarine, sesamin, and honokiol could effectively inhibit the proliferation of RA rat synovial cells, also could all effectively inhibit the secretion of TNF-α and IL-1β in synovial cells, and had a certain inhibitory effect on expression of the target protein RAC2. Conclusions: The core gene set of RA was screened from a new perspective, revealing biomarkers related to immune cell infiltration. Using molecular docking, we screened out TCM small molecules for the treatment of RA, providing methods and technical support for the treatment of RA with TCM.

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Section: Discussionmentioning

confidence: 99%

Disease Markers and Therapeutic Targets for Rheumatoid Arthritis Identified by Integrating Bioinformatics Analysis with Virtual Screening of Traditional Chinese Medicine

Sun¹,

Liu²,

Zhang³

2022

Front. Biosci. (Landmark Ed)

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“…Unfortunately, the pathogenesis and pathological process of the disease remain largely unclear with the treatment rather challenge. Notably, the recent development of high-throughput technologies has led to a better understanding of the pathogenesis of various diseases [14]. And more genes have been discovered and validated by high-throughput sequencing.…”

Section: Discussionmentioning

confidence: 99%

Genetic Characteristics of Systematic Juvenile Idiopathic Arthritis and The Bioinformatics Basis for Treatment

Liu

Wen

Liu

et al. 2021

Preprint

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Objective Systemic Juvenile Idiopathic Arthritis (sJIA) is a distinctive subtype of Juvenile Idiopathic Arthritis (JIA). The pathogenesis of sJIA is still unclear with the treatment options limited. Although previous bioinformatics analyses have identified some genetic factors underlying sJIA, these studies were mostly single center with a small sample size and the results were often inconsistent. Herein, we combined two datasets of GSE20307 and GSE21521 and select the matrix of patients diagnosed as sJIA in it for further analysis. Methods The GSE20307 and GSE21521 matrixs downloaded from the Gene Expression Omnibus (GEO) were analyzed using online-tool GEO2R, Venny, Metascape, STRING, and Cytoscape to identify differentially expressed genes (DEGs), enrichment pathways, protein-protein interaction (PPI), main Module and hub genes between sJIA individuals and healthy controls. Results A total of 289 overlapping genes (consisting of 41 downregulated genes and 248 upregulated genes) were identified. Hub genes were primarily related to erythropoiesis. And the KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis of overlapping DEGs were maily involved in Malaria and non-small cell lung cancer. Besides, DEGs in main module were involved in ubiquitin mediated proteolysis. Conclusions our study suggests that the erythropoiesis signature indeed exists in sJIA similar to previous reports. And combining previous research and our results, we provide a basis for the application of proteasome inhibitors, hydroxychloroquine and kinase inhibitors in patients with sJIA from the perspective of bioinformatics.

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Lymecycline reverses acquired EGFR-TKI resistance in non–small-cell lung cancer by targeting GRB2

Yang

Yan

et al. 2020

Pharmacological Research

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Bioinformatics‐Driven New Immune Target Discovery in Disease

Cited by 3 publications

References 43 publications

Disease Markers and Therapeutic Targets for Rheumatoid Arthritis Identified by Integrating Bioinformatics Analysis with Virtual Screening of Traditional Chinese Medicine

Disease Markers and Therapeutic Targets for Rheumatoid Arthritis Identified by Integrating Bioinformatics Analysis with Virtual Screening of Traditional Chinese Medicine

Genetic Characteristics of Systematic Juvenile Idiopathic Arthritis and The Bioinformatics Basis for Treatment

Lymecycline reverses acquired EGFR-TKI resistance in non–small-cell lung cancer by targeting GRB2

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