The Impacts of IL1R1 and IL1R2 Genetic Variants on Rheumatoid Arthritis Risk in the Chinese Han Population: A Case–Control Study

Liu, Xiaoli; Peng, Linna; Li, Dandan; He, Chunjuan; Xing, Shishi; Wang, Yuhe; He, Yongjun

doi:10.2147/ijgm.s291395

Cited by 7 publications

(6 citation statements)

References 32 publications

(33 reference statements)

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“…Another study showed that IL1R2 acts specifically on macrophages as a decoy receptor for IL-1 and is an important regulator of arthritis with an impact on RA risk in Chinese Han population. [23] IL1R2 promotes self-renewal of breast tumor initiating cells and proliferation and invasion of breast cancer cells by binding to and enhancing nuclear ubiquitin-specific protease 15 (USP15) activity. [24] IL1R2 gene polymorphisms and their interactions are associated with the susceptibility to osteoporosis in Chinese Han population.…”

Section: Discussionmentioning

confidence: 99%

Il1r2 and Tnfrsf12a in transcranial magnetic stimulation effect of ischemic stroke via bioinformatics analysis

Zhao,

Liu,

et al. 2024

Medicine

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Ischemic stroke refers to ischemic necrosis or softening of localized brain tissue. Transcranial magnetic stimulation (TMS) is a painless, noninvasive and green treatment method, which acts on the central nervous system through a pulsed magnetic field to assist in the treatment of central nervous system injury diseases. However, the role of Il1r2 and Tnfrsf12a in this is unknown. The ischemic stroke datasets GSE81302 and TMS datasets GSE230148 were downloaded from Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened and weighted gene co-expression network analysis (WGCNA) was performed. The construction and analysis of protein-protein interaction (PPI) network and functional enrichment analysis were performed. Draw heat map gene expression. Through the Comparative Toxicogenomics Database (CTD) to find the most relevant and core gene diseases. TargetScan was used to screen miRNAs regulating DEGs. A total of 39 DEGs were identified. According to gene ontology (GO) analysis results, in biological process (BP) analysis, they were mainly enriched in the positive regulation of apoptosis process, inflammatory response, positive regulation of p38MAPK cascade, and regulation of cell cycle. In cellular component (CC) analysis, they were mainly enriched in the cell surface, cytoplasm, and extracellular space. In Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, they were mainly enriched in nf-κB signaling pathway, fluid shear stress and atherosclerosis, P53 signaling pathway, TNF signaling pathway, and apoptosis. Among the enrichment items of metascape, negative regulation of T cell activation, hematopoietic cell lineage, positive regulation of apoptotic process, fluid shear stress and atherosclerosis were observed in GO enrichment items. Five core genes (Socs3, Irf1, Il1r2, Ccr1, and Tnfrsf12a) were obtained, which were highly expressed in ischemic stroke samples. Il1r2 and Tnfrsf12a were lowly expressed in TMS samples. CTD analysis found that the core gene (Socs3, Irf1 and Il1r2, Ccr1, Tnfrsf12a) and ischemic stroke, atherosclerosis, hypertension, hyperlipidemia, thrombosis, stroke, myocardial ischemia, myocardial infarction, and inflammation. Il1r2 and Tnfrsf12a are highly expressed in ischemic stroke, but lowly expressed in TMS samples.

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Section: Discussionmentioning

confidence: 99%

Il1r2 and Tnfrsf12a in transcranial magnetic stimulation effect of ischemic stroke via bioinformatics analysis

Zhao,

Liu,

et al. 2024

Medicine

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“…Both the membrane‐bound and soluble forms of IL1R1 have biological activity, which regulates the inflammatory response by activating and antagonizing cytokine activity. 47 , 48 Studies have demonstrated the association of IL1R1 with autoimmune diseases, including rheumatoid arthritis, 49 as well as cancers such as breast cancer 50 and colorectal cancer, 51 along with inflammatory diseases like inflammatory bowel disease. 52 First of all, our research shows that the expression level of IL1R1 protein is related to the occurrence of allergic diseases.…”

Section: Discussionmentioning

confidence: 99%

Identification of potential drug targets for allergic diseases from a genetic perspective: A mendelian randomization study

Wang,

Pang,

Zhou

et al. 2024

Clinical & Translational All

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BackgroundAllergic diseases typically refer to a heterogeneous group of conditions primarily caused by the activation of mast cells or eosinophils, including atopic dermatitis (AD), allergic rhinitis (AR), and asthma. Asthma, AR, and AD collectively affect approximately one‐fifth of the global population, imposing a significant economic burden on society. Despite the availability of drugs to treat allergic diseases, they have been shown to be insufficient in controlling relapses and halting disease progression. Therefore, new drug targets are needed to prevent the onset of allergic diseases.MethodWe employed a Mendelian randomization approach to identify potential drug targets for the treatment of allergic diseases. Leveraging 1798 genetic instruments for 1537 plasma proteins from the latest reported Genome‐Wide Association Studies (GWAS), we analyzed the GWAS summary statistics of Ferreira MA et al. (nCase = 180,129, nControl = 180,709) using the Mendelian randomization method. Furthermore, we validated our findings in the GWAS data from the FinnGen and UK Biobank cohorts. Subsequently, we conducted sensitivity tests through reverse causal analysis, Bayesian colocalization analysis, and phenotype scanning. Additionally, we performed protein‐protein interaction analysis to determine the interaction between causal proteins. Finally, based on the potential protein targets, we conducted molecular docking to identify potential drugs for the treatment of allergic diseases.ResultsAt Bonferroni significance (p < 3.25 × 10−5), the Mendelian randomization analysis revealed 11 significantly associated protein‐allergic disease pairs. Among these, the increased levels of TNFAIP3, ERBB3, TLR1, and IL1RL2 proteins were associated with a reduced risk of allergic diseases, with corresponding odds ratios of 0.82 (0.76–0.88), 0.74 (0.66–0.82), 0.49 (0.45–0.55), and 0.81 (0.75–0.87), respectively. Conversely, increased levels of IL6R, IL1R1, ITPKA, IL1RL1, KYNU, LAYN, and LRP11 proteins were linked to an elevated risk of allergic diseases, with corresponding odds ratios of 1.04 (1.03–1.05), 1.25 (1.18–1.34), 1.48 (1.25–1.75), 1.14 (1.11–1.18), 1.09 (1.05–1.12), 1.96 (1.56–2.47), and 1.05 (1.03–1.07), respectively. Bayesian colocalization analysis suggested that LAYN (coloc.abf‐PPH4 = 0.819) and TNFAIP3 (coloc.abf‐PPH4 = 0.930) share the same variant associated with allergic diseases.ConclusionsOur study demonstrates a causal association between the expression levels of TNFAIP3 and LAYN and the risk of allergic diseases, suggesting them as potential drug targets for these conditions, warranting further clinical investigation.

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“…Previous research found that MMP9 [347], CBS (cystathionine beta-synthase) [348], IGF2 [349], IGFBP2 [350], HP (haptoglobin) [351], MAPK14 [352], LRG1 [353], LCN2 [354], SBNO2 [355], ITGA1 [356], IL1R2 [357], IL1RN [358], TLR4 [359], IRS2 [360], CORIN (corin, serine peptidase) [361], WNT16 [362], CD80 [363], FASLG (Fas ligand) [364], CXXC5 [365], CNR2 [366], TCF4 [367], PTCH1 [368], TXNIP (thioredoxin interacting protein) [369], TRAF3IP2 [370], ETS1 [371] and ARHGEF3 [372] are strongly associated with osteoporosis. MMP9 [373], S100A12 [374], IGF2 [375], GPR84 [376], SOCS3 [377], CEBPA (CCAAT enhancer binding protein alpha) [378], PGLYRP1 [379], HP (haptoglobin) [380], MMP8 [381], OSM (oncostatin M) [382], TLR5 [383], S100A8 [384], ARG1 [385], S100A9 [386], BCL2A1 [387], IRAK3 [388], ST3GAL4 [389], IL1R1 [390], MAPK14 [391], IL4R [392], PADI4 [393], LCN2 [394], CEBPB (CCAAT enhancer binding protein beta) [395], TSPO (translocator protein) [396], IL18RAP [397], PLAU (plasminogen activator, urokinase) [398], CDA (cytidinedeaminase) [399], SLC11A1 [400], RETN (resistin) [401], GADD45A [402], BCL6 [403], CD55 [404], THBS1 [405], IL1R2 [406], TGFA (transforming growth factor alpha) [407], IL1RN [408], ALDH1A2 [409], SLC8A3 [410], HMGB2 [411], NFIL3 [412], MSRA (methionine sulfoxidereductase A) [413], ITGAM (integrin subunit alpha M) [414], TLR4 [415], SIGLEC9 [416], GRB10 [417]...…”

Section: Discussionmentioning

confidence: 99%

Identification of Key Genes and Underlying Mechanisms in Cystic Fibrosis and its Associated Complications Based on Bioinformatics Analysis of Next Generation Sequencing Data Analysis

Vastrad¹,

Vastrad²

2022

Preprint

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Cystic fibrosis (CF) is one of the inherited autosomal recessive disorders with very complicated pathogenesis. Identifying the molecular signatures and specific biomarkers of CF might provide novel clues for CF and and CF associated complications prognosis and targeted therapy. Based on the nexgeneration sequencing (NGS) dataset GSE136371 downloaded from the Gene Expression Omnibus (GEO) database, the differentially expressed genes (DEGs) between CF samples and normal controls were identified by using DESeq2 bioconductor package of R software. Gene ontology (GO) and REACTOME pathway enrichment analyses were applied for the DEGs. Then protein-protein interaction (PPI) network of these DEGs was visualized by Cytoscape with IMEx interactome database. The most significant module from the PPI network was selected for GO and pathway enrichment analysis. Subsequently, a miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed to identify hub genes, miRNAs and TFs. Finally, receiver operating characteristic curve (ROC) analysis was established to validate these hub genes. Total of 917 DEGs were identified between CF and normal control samples in GSE136371 dataset, including 479 up regulated and 438 down regulated genes. The most enriched DEGs in GO and pathway enrichment analysis were mainly associated with response to stimulus, regulation of cellular process, Neutrophil degranulation and rRNA processing. PPI network, module analysis, miRNA-hub gene regulatory network and TF-hub gene regulatory network predicted ten hub genes (FN1, UBE2D1, SRPK1, MAPK14, CEBPB, HSP90AB1, HSPA8, XRCC6, NCL and PARP1). In conclusion, the DEGs, relative pathways and hub genes identified in the present study might aid in understanding of the molecular mechanisms underlying CF and CF associated complications progression and provide potential molecular targets and biomarkers for CF and CF associated complications.

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The Impacts of IL1R1 and IL1R2 Genetic Variants on Rheumatoid Arthritis Risk in the Chinese Han Population: A Case–Control Study

Cited by 7 publications

References 32 publications

Il1r2 and Tnfrsf12a in transcranial magnetic stimulation effect of ischemic stroke via bioinformatics analysis

Il1r2 and Tnfrsf12a in transcranial magnetic stimulation effect of ischemic stroke via bioinformatics analysis

Identification of potential drug targets for allergic diseases from a genetic perspective: A mendelian randomization study

Identification of Key Genes and Underlying Mechanisms in Cystic Fibrosis and its Associated Complications Based on Bioinformatics Analysis of Next Generation Sequencing Data Analysis

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