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

Cao, Ying; Tang, Weihao; Tang, Wanxin

doi:10.21203/rs.2.12276/v1

Cited by 4 publications

(4 citation statements)

References 26 publications

(27 reference statements)

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“…In the current study, we screened GSE32591, GSE69438, and GSE127797 gene datasets of LN. As immune infiltration in glomeruli has been wellstudied by several groups (34,35), we set out to assess the importance of the immune microenvironment in LN tubulointerstitial lesions. One single cell sequence study reported kidney infiltrating immune cells mainly .…”

Section: Discussionmentioning

confidence: 99%

Identification of the tubulointerstitial infiltrating immune cell landscape and immune marker related molecular patterns in lupus nephritis using bioinformatics analysis

Zhang

Chen

et al. 2020

Ann Transl Med

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Background: Systemic lupus erythematosus (SLE) is a multisystem autoimmune disease that commonly affects the kidneys. Research into markers that can predict the prognosis of tubulointerstitial lupus nephritis (LN) has been impeded by the lack of well-designed studies.Methods: In this study, we selected and merged 3 sets of renal biopsy tubulointerstitial data from GSE32591, GSE69438, and GSE127797, including 95 LN and 15 living healthy donors. CIBERSORTx was utilized for differentially infiltrating immune cell (DIIC) analysis. Weighted Gene Co-Expression network analysis (WGCNA) was employed to explore differentially expressed gene (DEG) related modules.Combined WGCNA hub genes and protein-protein interaction (PPI) validation was used for immune marker identification. Lastly, unsupervised clustering was carried out to validate the correlation between these markers and clinical characteristics.Results: Our findings unveiled TYROBP, C1QB, LAPTM5, CTSS, PTPRC as the 5 immune markers, which were negatively correlated with glomerular filtration rate (GFR). Specifically, the expression levels of TYROBP and C1QB were significantly different between proliferative LN (PLN) and membranous LN (MLN). Unsupervised clustering could aggregate LN by these immune marker expression spectrums.Conclusions: This study is the first to identify infiltrating immune cells and associated molecular patterns in the tubulointerstitium of LN by utilizing bioinformatics methods. These findings contribute to a better understanding of the mechanisms behind LN, and promote more precise diagnosis.

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Section: Discussionmentioning

confidence: 99%

Identification of the tubulointerstitial infiltrating immune cell landscape and immune marker related molecular patterns in lupus nephritis using bioinformatics analysis

Zhang

Chen

et al. 2020

Ann Transl Med

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“…Cibersort is an online analysis tool for immunocell in ltration for a variety of diseases, including osteoarthritis, lupus nephritis, atopic dermatitis, acne and rosacea dermatitis [23][24][25][26]. At present, few studies have used the Cibersort method to study the characteristics of immune cell in ltration in tendinopathy.…”

Section: The Analysis Of Immune Cell In Ltrationmentioning

confidence: 99%

Screening of key biomarkers of tendinopathy based on bioinformatics and machine learning algorithms

Zhu

Huang

Zhou³

et al. 2021

Preprint

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Tendinopathy is a complex, multifaceted tendon disease that is often associated with overuse and causes significant health care costs with its high prevalence. At present, the pathogenesis and effective treatment of tendinopathy cannot be fully elucidated. This study aims to deeply explore and analyze the key genes, functional pathways, and immune infiltration characteristics of the occurrence and development of tendinopathy.MethodsThe gene expression profiles of GSE106292, GSE26051 and GSE167226 were downloaded from GEO database. The WGCNA analysis was performed on GSE106292 data set by the R package in R software, and differential gene analysis was performed on GSE26051 and GSE167226 data sets by combining and screening. The gene enrichment analysis of GO and KEGG and immune cell infiltration analysis were performed. Lasso logistic regression, support vector machine (SVM-REF) and Gaussian mixture model (GMMS) algorithm were used to screen and identify early diagnostic genes.ResultsWe have obtained a total of 171 DEGs through WGCNA analysis and screening of different expressed genes. By GO and KEGG enrichment analysis, it was found that these malregulated genes were related to mTOR, HIF-1, MAPK, NF-κB and VEGF signaling pathways. Immunoinfiltration analysis showed that M1 macrophages, activated mast cells and activated NK cells had infiltration significance. MacroD1 may be an early diagnosis gene, and it was found based on Lasso, SVM-REF and GMMS algorithm.ConclusionsBased on comprehensive bioinformatics analysis, we identified the potential early diagnosis genes MACROD1, key regulatory pathways and immune infiltration characteristics of tendinopathy. These key genes and pathways may be used as biomarkers and molecular therapeutic targets for early tendon injury to guide drugs and basic research.

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“…CIBERSORT is a computational approach that assesses the immune cell population pro les from fresh, frozen and xed specimens (18) originating from RNA-seq or microarray data, which is widely used in the analysis of the in ltration of 22 kinds of immune cells in different diseases such as osteoarthritis (19,20), ischemia-reperfusion injury in renal transplantation (21), lupus nephritis (22) and atopic dermatitis (23).…”

Section: Introductionmentioning

confidence: 99%

Elucidating the Key Biomarkers and Immune Infiltration in Acne by Integrated Bioinformatics Analysis

Yang

Shou

Yang

et al. 2021

Preprint

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Background Acne vulgaris is a common inflammatory condition of skin. However, the landscape of immune infiltration in acne has not been entirely described. Objectives This study used a bioinformatics approach to investigate the inflammatory acne-related key biomarkers and signaling pathways, and immune infiltration in the acne lesion. Methods Two microarray datasets (GSE108110 and GSE53795) were downloaded from Gene Expression Omnibus. We used “limma” package from R software to identify the differentially expressed genes (DEGs) and perform the functional enrichment analyses. Then we built a protein-protein interaction network (PPI), performed the hub genes’ identification through STRING and Cytoscape. We applied the CIBERSORT algorithm to describe the immune infiltration in acne, and explored the correlation between biomarkers and immune infiltration. In the end, our findings in the study were verified by analyzing microarray dataset GSE6475. Results The differentially expressed genes (DEGs) including 292 upregulated genes and 150 downregulated genes in acne compared with non-lesional skin. The hub genes FPR1, C3AR1, CXCL1, CXCL8, FPR2, C3, CCR7, ITGB2 and pivotal pathways JAK-STAT signaling pathway, Toll-like receptor and NOD-like receptor signaling pathway were the most significantly associated with raising neutrophils, monocytes, activated mast cells, as well as reducing resting mast cells and Tregs. Conclusions Our study provides new insights into the pathogenesis and the targets which might be immunomodulatory potential for acne.

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

Cited by 4 publications

References 26 publications

Identification of the tubulointerstitial infiltrating immune cell landscape and immune marker related molecular patterns in lupus nephritis using bioinformatics analysis

Identification of the tubulointerstitial infiltrating immune cell landscape and immune marker related molecular patterns in lupus nephritis using bioinformatics analysis

Screening of key biomarkers of tendinopathy based on bioinformatics and machine learning algorithms

Elucidating the Key Biomarkers and Immune Infiltration in Acne by Integrated Bioinformatics Analysis

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