Gene Expression Analysis Reveals Novel Shared Gene Signatures and Candidate Molecular Mechanisms between Pemphigus and Systemic Lupus Erythematosus in CD4+ T Cells

Sezin, Tanya; Vorobyev, Artem; Sadik, Christian D.; Zillikens, Detlef; Gupta, Yask; Ludwig, Ralf J.

doi:10.3389/fimmu.2017.01992

Cited by 51 publications

(49 citation statements)

References 61 publications

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“…For example, FCN1 was proven to be associated with monocytes in patients with microscopic polyangiitis [30]. Another study involving weighted correlation network analysis showed that RSAD2 related to CD4+ T cells may be the most highly ranked hub gene in SLE [7]. BTK mediates TLR signaling in macrophages and may be a promising treatment approach for LN [31][32][33].…”

Section: Discussionmentioning

confidence: 99%

“…Bioinformatic analysis can process large amounts of samples within an extremely short time and provide valuable information about diseases. Several genes closely associated with SLE have been identified and driven research innovations over the past years [6][7][8]. Few studies have explored LN on the basis of kidney tissue to reveal its characteristics.…”

Section: Introductionmentioning

confidence: 99%

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Immune cell infiltration characteristics and related core genes in lupus nephritis: results from bioinformatic analysis

Cao

Tang

2019

Preprint

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Objects Lupus nephritis (LN) is a common complication of systemic lupus erythematosus that presents a high risk of end-stage renal disease. In the present study, we used CIBERSORT and gene set enrichment analysis (GSEA) of gene expression profiles to identify immune cell infiltration characteristics and related core genes in LN. Methods Datasets from the Gene Expression Omnibus, GSE32591 and GSE113342, were downloaded for further analysis. The GSE32591 dataset, which included 32 LN glomerular biopsy tissues and 14 glomerular tissues of living donors, was analyzed by CIBERSORT. Different immune cell types in LN were analyzed by the Limma software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis based on GSEA were performed by clusterProfiler software. Lists of core genes were derived from Spearman correlation between the most significant GO term and differentially expressed immune cell gene from CIBERSORT. GSE113342 was employed to validate the association between selected core genes and clinical manifestation. Result Five types of immune cells revealed important associations with LN, and monocytes emerged to be the prominent differences. GO and KEGG analyses indicated that immune response pathways are significantly enriched in LN. The Spearman correlation indicated that 15 genes, including FCER1G, CLEC7A, MARCO, CLEC7A, PSMB9, and PSMB8, were closely related to clinical features. Conclusion This study is the first to identify immune cell infiltration with microarray data of glomeruli in LN by using CIBERSORT analysis and provides novel evidence and clues for further research of the molecular mechanisms of LN.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Immune cell infiltration characteristics and related core genes in lupus nephritis: results from bioinformatic analysis

Cao

Tang

2019

Preprint

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“…STAT1 and JAK1 participate in the biological process via participating in type I interferons binding. STAT1 is a key regulatory gene in autoimmune diseases and metastatic melanoma [45,46] . Studies found that JAK/STAT pathway genes may play roles in lymphomagenesis, but they still need further investigation [47][48][49] .…”

Section: Discussionmentioning

confidence: 99%

Identification of key genes related to dexamethasone-resistance in acute lymphoblastic leukemia

Chen

Shí

et al. 2018

Preprint

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Drug resistance is the main cause of poor chemotherapy response in acute leukemia.Despite the extensive use of dexamethasone(DEX) in the treatment of acute lymphoblastic leukemia for many years, the mechanisms of dexamethasone -resistance has not been fully understood. We choose GSE94302 from GEO database aiming to identify key genes that contribute to the DEX resistance in acute lymphoblastic leukemia. Differentially expressed gene(DEGs) are selected by using GEO2R tools. A total of 837 DEGs were picked out, including 472 up-regulated and 365 down-regulated DEGs. All the DEGs were underwent gene ontology(GO) analysis and Kyoto Encyclopedia of Gene and Genome(KEGG) pathway analysis. In addition, the DEGsencoded protein-protein interaction (PPI) was screened by using Cytoscape and Search Tool for the Retrieval of Interacting Genes(STRING). Total 20 genes were found as key genes related to DEX resistance with high degree of connectivity, including CDK1,

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“…Hub Genes with the highest connectivity in each module were identi ed using the function "chooseTopHubInEachModule" in WGCNA (Sezin et al, 2017). Bee swarm plots were created using the R package (https://cran.r-project.org/web/packages/beeswarm/index.html).…”

Section: Hub Gene Identi Cation and Validationmentioning

confidence: 99%

Weighted Gene Co-Expression Network Analysis revealed key biomarkers associated with the diagnosis of hypertrophic cardiomyopathy

Wang

Zhang

et al. 2020

Preprint

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Objective: To reveal the molecular mechanism underlying the pathogenesis of HCM and find new effective therapeutic strategies using a systematic biological approach.Methods: The WGCNA algorithm was applied to building the co-expression network of HCM samples. A sample cluster analysis was performed using the hclust tool and a co-expression module was constructed. The WGCNA algorithm was used to study the interactive connection between co-expression modules and draw a heat map to show the strength of interactions between modules. The genetic information of the respective modules was mapped to the associated GO terms and KEGG pathways, and the Hub Genes with the highest connectivity in each module were identified. The Wilcoxon test was used to verify the expression level of hub genes between HCM and normal samples, and the "pROC" R package was used to verify the possibility of hub genes as biomarkers. Finally, the potential functions of hub genes were analyzed by GSEA software. Results: Seven co-expression modules were constructed using sample clustering analysis. GO and KEGG enrichment analysis judged that the turquoise module is an important module. The hub genes of each module are RPL35A for module Black, FH for module Blue, PREI3 for module Brown, CREB1 for module Green, LOC641848 for module Pink, MYH7 for module Turquoise and MYL6 for module Yellow. The results of the differential expression analysis indicate that MYH7 and FH are considered true hub genes. In addition, the ROC curves revealed their high diagnostic value as biomarkers for HCM. Finally, in the results of the GSEA analysis, MYH7 and FH highly expressed genes were enriched with the "proteasome" and a "PPAR signaling pathway," respectively.Conclusions: The MYH7 and FH genes may be the true hub genes of HCM. Their respective enriched pathways, namely the "proteasome" and the "PPAR signaling pathway," may play an important role in the development of HCM.

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Gene Expression Analysis Reveals Novel Shared Gene Signatures and Candidate Molecular Mechanisms between Pemphigus and Systemic Lupus Erythematosus in CD4+ T Cells

Cited by 51 publications

References 61 publications

Immune cell infiltration characteristics and related core genes in lupus nephritis: results from bioinformatic analysis

Immune cell infiltration characteristics and related core genes in lupus nephritis: results from bioinformatic analysis

Identification of key genes related to dexamethasone-resistance in acute lymphoblastic leukemia

Weighted Gene Co-Expression Network Analysis revealed key biomarkers associated with the diagnosis of hypertrophic cardiomyopathy

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