ArticleCentral-European journal of immunology2022
Identification of key biomarkers and signaling pathways and analysis of their association with immune cells in immunoglobulin A nephropathy.
Article in Central-European journal of immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed, 1 citations in OpenAlex.
- Interferon Gamma Receptor 2 Collaborates With Circular RNA/MicroRNA to Modulate Programmed Cell Death-Ligand 1 Levels in Nasopharyngeal Carcinoma.World journal of oncology · 2024Article
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Authors and funding
7 authors at 1 institution in 1 country.
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Abstract
Introduction: Immunoglobulin A nephropathy (IgAN) is the most common glomerular disease worldwide, with a poor prognosis. The aim of our study was to identify key biomarkers and their associations with immune cells to aid in the study of IgAN pathology and immunotherapy. Material and methods: The data of IgAN were downloaded from a public database. The metaMA package and limma package were used to identify differentially expressed mRNAs (DEmRNAs) and differentially expressed miRNAs (DEmiRNAs), respectively. Biological functions of the DEmRNAs were analyzed. Machine learning was used to screen the mRNA biomarkers of IgAN. Pearson's correlation coefficient was used to analyze the correlation between mRNA biomarkers, immune cells and signaling pathways. Moreover, we constructed a miRNAs-mRNAs targeted regulatory network. Finally, we performed in vitro validation of the identified miRNAs and mRNAs. Results: 1205 DEmRNAs and 125 DEmiRNAs were identified. In gene set enrichment analysis (GSEA), tumor necrosis factor α (TNF-α) signaling via nuclear factor κB (NF-κB), apoptosis and MTORC-1 signaling were inhibited in IgAN. 8 mRNA biomarkers were screened by machine learning. In addition, the distribution of 8 immune cell types was found to be significantly different between normal controls and IgAN by difference analysis. Pearson correlation coefficient analysis demonstrated that AKAP8L was significantly negatively correlated with CD4 Conclusions: The identification of 8 important biomarkers and their correlation with immune cells and biological signaling pathways provides new ideas for further study of IgAN.
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