eISSN: 1644-4124
ISSN: 1426-3912
Central European Journal of Immunology
Current issue Archive Manuscripts accepted About the journal Editorial board Abstracting and indexing Subscription Contact Instructions for authors Ethical standards and procedures
SCImago Journal & Country Rank
3/2022
vol. 47
 
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abstract:
Experimental immunology

Identification of key biomarkers and signaling pathways and analysis of their association with immune cells in immunoglobulin A nephropathy

Guoxin Zhang
1
,
Lanfen Xue
1
,
Shanshan Zhang
1
,
Na Liu
1
,
Xing Yao
1
,
Jieqiong Fu
1
,
Limin Nie
1

1.
Department of Nephrology, Shijiazhuang People’s Hospital, Hebei Province, China
Cent Eur J Immunol 2022; 47 (3): 189-205
Online publish date: 2022/09/29
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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 κ (TNF-κ) signaling via nuclear factor αB (NF-αB), 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+ memory T-cells. AKAP8L was also significantly negatively correlated with TNF-α signaling via NF-κB, apoptosis, and MTORC-1 signaling. Subsequently, 5 mRNA biomarkers predicted corresponding negative regulatory miRNAs.

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.

keywords:

immunoglobulin A nephropathy, AKAP8L, CD4+ memory T-cells, biomarkers, machine learning

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