Identification and Validation of Prognostic Biomarkers Specifically Expressed in Macrophage in IgA Nephropathy Patients Based on Integrated Bioinformatics Analyses

Ding, Yuhao; Li, Hua; Xu, Lichen; Wang, Yukun; Yang, Hui Ying

doi:10.3389/fmolb.2022.884588

Cited by 5 publications

(3 citation statements)

References 47 publications

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“… 43 CYBB has been proposed as a primary component of the microbicidal oxidase system of phagocytes. Recent studies have suggested its role in the pathogenesis of immunoglobulin A nephropathy 44 , 45 , though its role in AKI remains unclear. VCAN, a member of the aggrecan/versican proteoglycan family, is involved in cell adhesion, proliferation, migration, and angiogenesis, playing an essential role in tissue morphogenesis and maintenance.…”

Section: Discussionmentioning

confidence: 99%

Exploration of potential biomarkers and therapeutic targets for trauma-related acute kidney injury

Qi,

Huang,

et al. 2024

Chinese Journal of Traumatology

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Section: Discussionmentioning

confidence: 99%

Exploration of potential biomarkers and therapeutic targets for trauma-related acute kidney injury

Qi,

Huang,

et al. 2024

Chinese Journal of Traumatology

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“…CYBB promotes macrophage in ltration in diabetes nephropathy. On the contrary, inhibiting NOX2 can alleviate podocyte damage and oxidative stress 54 .…”

Section: Discussionmentioning

confidence: 99%

Exploring the crosstalk mechanism and immune relationship between SARS-CoV-2 infection and IgA nephropathy based on comprehensive bioinformatics analysis and machine learning

Gong,

Che,

et al. 2024

Preprint

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Background: COVID-19 is a disease caused by SARS CoV-2 infection and has caused a global pandemic. Currently, it remains a threat to global human health. IgA nephropathy (IgAN) is the main pathological type of renal injury in patients with COVID-19, and leads to poor prognosis. However, the correlation and pathogenesis between COVID-19 and IgAN are not completely clear. The purpose of this study is to identify the crosstalk gene between COVID-19 and IgAN, and to preliminarily explore its clinical value and molecular mechanism. Methods: We obtained the COVID-19 and IgAN expression profile datasets from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) analysis was applied to identify the crosstalk DEGs (CDEGs). Two machine learning methods (LASSO regression and SVM-RFE) were used to further screen for common diagnostic biomarkers of these two diseases from CDEGs. Furthermore, we conducted GO and KEGG pathway analysis, protein-protein interaction (PPI) network construction, hub gene identification, immune microenvironment analysis, and candidate traditional Chinese medicine herbs and ingredients prediction on CDEGs. Result: A total of 61 CDEGs were identified by the intersection of DEGs between COVID-19 and IgAN cohorts. CDEGs were mainly involved in many infectious disease pathways and cytokine-cytokine receptor interaction pathways. Functional enrichment showed that IL-1B involved six pathways, which may be a key gene in the common mechanism of COVID-19 and IgAN. After screening through machine learning, we found that TOP2A was the best shared diagnostic biomarker for COVID-19 and IgAN. Hub genes, as well as immune-related genes, and transcription factors of hub genes, were screened out. The results of immune infiltration showed that CDEGs are highly correlated with multiple infiltrating immune cells. Based on the CDEGs, cluster analysis was used to respectively regroup COVID-19 and IgAN samples into two clusters. 10 potential traditional Chinese medicine herbs and ingredients for treating COVID-19 related IgAN were proposed. Conclusion: In our study, we reveal the molecular network and signal pathway connecting COVID-19 and IgAN. Furthermore, we have screened the optimal diagnostic biomarkers for COVID-19 and IgAN. Our findings reveal a close relationship between CDEGs and the immune microenvironment of diseases. This provides clues for further exploring the common mechanism and interaction of these two diseases, and may provide new targets and drugs for intervention of COVID-19 related IgAN.

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“…IFI44L promoter methylation can be used as a blood biomarker for LN (132)(133)(134). The protein encoded by IL10RA is a receptor for interleukin 10 (IL-10) (135). Overexpression of IL-10 in LN renal tissue and peripheral blood is highly correlated with the onset and activity of LN (136, 137).…”

Section: Correlation Of Degs With Lnmentioning

confidence: 99%

Predicting diagnostic gene expression profiles associated with immune infiltration in patients with lupus nephritis

Wang

Yang

et al. 2022

Front. Immunol.

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ObjectiveTo identify potential diagnostic markers of lupus nephritis (LN) based on bioinformatics and machine learning and to explore the significance of immune cell infiltration in this pathology.MethodsSeven LN gene expression datasets were downloaded from the GEO database, and the larger sample size was used as the training group to obtain differential genes (DEGs) between LN and healthy controls, and to perform gene function, disease ontology (DO), and gene set enrichment analyses (GSEA). Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE), were applied to identify candidate biomarkers. The diagnostic value of LN diagnostic gene biomarkers was further evaluated in the area under the ROC curve observed in the validation dataset. CIBERSORT was used to analyze 22 immune cell fractions from LN patients and to analyze their correlation with diagnostic markers.ResultsThirty and twenty-one DEGs were screened in kidney tissue and peripheral blood, respectively. Both of which covered macrophages and interferons. The disease enrichment analysis of DEGs in kidney tissues showed that they were mainly involved in immune and renal diseases, and in peripheral blood it was mainly enriched in cardiovascular system, bone marrow, and oral cavity. The machine learning algorithm combined with external dataset validation revealed that C1QA(AUC = 0.741), C1QB(AUC = 0.758), MX1(AUC = 0.865), RORC(AUC = 0.911), CD177(AUC = 0.855), DEFA4(AUC= 0.843)and HERC5(AUC = 0.880) had high diagnostic value and could be used as diagnostic biomarkers of LN. Compared to controls, pathways such as cell adhesion molecule cam, and systemic lupus erythematosus were activated in kidney tissues; cell cycle, cytoplasmic DNA sensing pathways, NOD-like receptor signaling pathways, proteasome, and RIG-1-like receptors were activated in peripheral blood. Immune cell infiltration analysis showed that diagnostic markers in kidney tissue were associated with T cells CD8 and Dendritic cells resting, and in blood were associated with T cells CD4 memory resting, suggesting that CD4 T cells, CD8 T cells and dendritic cells are closely related to the development and progression of LN.ConclusionC1QA, C1QB, MX1, RORC, CD177, DEFA4 and HERC5 could be used as new candidate molecular markers for LN. It may provide new insights into the diagnosis and molecular treatment of LN in the future.

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Identification and Validation of Prognostic Biomarkers Specifically Expressed in Macrophage in IgA Nephropathy Patients Based on Integrated Bioinformatics Analyses

Cited by 5 publications

References 47 publications

Exploration of potential biomarkers and therapeutic targets for trauma-related acute kidney injury

Exploration of potential biomarkers and therapeutic targets for trauma-related acute kidney injury

Exploring the crosstalk mechanism and immune relationship between SARS-CoV-2 infection and IgA nephropathy based on comprehensive bioinformatics analysis and machine learning

Predicting diagnostic gene expression profiles associated with immune infiltration in patients with lupus nephritis

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